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FINANCIAL LITERACY IN INDIA: STATISTICS AND SOLUTIONS

Financial Literacy means the ability to understand how money works in the world and take an informed as well as judicious decision with regards to all the financial activities.

A person who is Financially literate knows how to earn, manage and invest money. He is familiar with financial products and applies his knowledge to make the best use of them.

Deep Institute providing ISS coaching in Lucknow quoted the President of the Institute of Company Secretaries of India (ICSI), Ashish Garg, "Despite having the world's 10th largest and Asia's oldest stock exchange, low per capita income, educational inequality, non-banking habits, informal borrowing and lending practices that have been going on for years. Thus, it is imperative for the country to now understand how to optimize its resources and boost the economic and financial backbone of the nation."

Financial Literacy in India

Financial literacy and financial inclusion are two aspects of financial stability in a country.

When people are financially literate, they are more likely to explore the products and services offered by banks and use them for their benefits. This accelerates the pace of financial inclusion, where everyone can access the basic banking facilities rather than relying on the orthodox systems of money market such as borrowing money from Zamindaars or village money lenders.

Unfortunately, when it comes to India’s financial literacy rate the statistics are quite shocking.

According to a survey conducted by Standard & Poor’s, over 76% Indian adults lack basic financial literacy and they don’t understand the most basic and key financial concepts.

 While the number is much lower than the worldwide financial literacy rate, it’s roughly in line with the BRIC and South Asian nations.       

More About the Survey on India’s Financial Literacy

The survey was based on the interviews conducted on 150,000 adults from 140 countries. The individuals were tested on their knowledge of four basic financial concepts: numeracy, risk diversification, inflation, and compound interest (savings and debt). The one who answered three out of four concepts correctly, was defined as financially literate.

According to the survey, “Countries with higher literacy rates include Australia, Canada, Denmark, Finland, Germany, Israel, the Netherlands, Norway, Sweden, and the UK, where more than 65% of adults are financially literate. South Asia is home to countries with some of the lowest financial literacy scores, where only a quarter of adults—or fewer—are financially literate. Singapore has the highest percentage of financially literate adults (59%) in Asia.”

Here are some of the key findings on India’s Financial Literacy.

=> Only 14% Indian adults could answer questions on risk diversification while 51% understood compound interest and 56% were correct with questions on inflation.

=> 39% of adults who have a formal loan are financially literate, while 27% of formal borrowers are not financially literate.

=> A mere 14% of Indian adults save at a formal institution.

=> Going by the gender gap, 73% of men and 80% of women in India are not financially literate.

=>  26% of the adults in the richest 60% of households are financially literate, while 20% of the poorest 40% of households are financially literate.

According to a survey on Global Financial Literacy in 2012 conducted by VISA, only 35% of Indians were financially literate and India was among the least financially literate countries.

Another survey of “Financial Literacy among Students, Young Employees and the Retired in India” conducted by IIM-A supported by CITI Foundation reveals that” high financial literacy is not widespread among Indians where only less than a quarter population have adequate knowledge on financial matters. There is lack of understanding among Indians about the basic principles of money and household finance, such as compound interest, impact of inflation on rates of return and prices, and the role of diversification in investments.”

Clearly, the statistics are disappointing. The lack of essential knowledge on financial matters and inability to manage personal finance not only affect an household, but makes an economy as a whole suffer too.

Like I said earlier, financial inclusion and financial literacy are two essential ingredients of an efficient economy. While, financial literacy can accelerate financial inclusion, the vice versa may not hold true.

Financial inclusion is a priority in our country. And the Govt has been fairly active on its strategies on financial inclusion where various schemes are being introduced and awareness campaigns are being held from time to time. But owing to the existing bottlenecks in terms income disparity, poverty, gender gaps and all, the implementation of financial inclusion policies has been challenging too.

For example, when Pradhan Mantri Jan Dhan Yojana, a National Mission on Financial Inclusion kicked off in 2014, the result was record-breaking. About 214 million zero balance accounts were created, which means a huge segment of population could access banking facilities at a nominal cost.

But, unfortunately this many number of accounts do not ensure financial literacy. If it had, our performance in Global Financial Literacy wouldn’t have been this poor.

In an article on Financial Inclusion published in Economic Times, Rajat Gandhi rightly says that, “No matter how many banks you open and how many boots you have on the ground, if a person does not know about the financial options that are open to him, policies, schemes and financial instruments will mean little. It is important for a person to firstly know what to look for and only then think of the benefits that he can obtain from it.”

To make things clear, financial inclusion focuses on volume or quantity whereas financial literacy is more about quality.

While financial inclusion emphasises on creating more accounts in order to make the common banking facilities easily accessible to all, financial literacy emphasises on expanding the knowledge on financial matters and products so that one can,

·         Understand how to use and manage money and minimize financial risk

·         Manage personal finance quite efficiently

·         Identify the benefits and facilities offered by banks and boycott the dodgy moneylenders.

·         Derive the long-term benefits of savings

And eventually it will further the financial inclusion movements.

What Should be Done to Increase Financial Literacy in India?

Considering the scenario, deliberate actions to promote financial literacy is the need of the hour.  Before initiating the steps, the target group should be divided on the basis of their age, income, education and gender and given opportunities to enhance their financial literacy in a more simple and easy-to-understand manner.

Here are few factors that can help.

Financial Literacy Month: Countries that boast high financial literacy rate observe financial literacy month. In US april is considered as the month of financial literacy where effort are taken to educate the citizens about the importance of financial literacy and why it’s important to maintain healthy financial habit. India too needs to realise the importance of Financial Literacy month.

Five Reasons Why India Needs a Financial Literacy Month?

Including Financial Literacy in School Syllabus: Financial literacy should begun at school stage. Recently, RBI Governor Raghuram Rajan has proposed inclusion of financial literacy in school curriculum. When children are aware of the concept, they can influence their families on the importance of savings and take necessary steps to better manage their money.  Thus, spreading the concept of financial literacy by inculcating banking habits and creating financial awareness among children is a great help.

The Role of Technology: We all are living in a digital era. The role of technology in financial literacy thus can never be overlooked. Financial literacy through the use of technology can be accelerated via three medium- computer, mobile, and internet. With mobile phones getting more convenient each passing day, it’s more easy to reach people through the platform. The platforms should so designed that whenever somebody needs financial advice, they can easily access the necessary information.

Technology allows independent learning. And it’s important to exploit the means in our advantage.

This is all I have to share about financial literacy in India. If you have something to add or share, please write to me or use the comment box given below.

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STATISTICS AND THE MEDICAL TREATMENT OF DRUG ADDICTION

National Survey on Drug Use and Health (NSDUH) is the primary source of statistical information on the use of tobacco, alcohol, prescription pain relievers, and other substances (e.g., marijuana, cocaine) by the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several series of questions that focus on mental health issues. NSDUH has been ongoing since 1971 and is conducted by the federal government. The survey also by ISS coaching in Lucknow collects information from residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories) and from civilians living on military bases. NSDUH excludes homeless people who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails or prisons and long-term hospitals. From 1999 to 2019, the data were collected via face-to- face (in-person) interviews at a respondent’s place of residence using a combination of computer-assisted personal interviewing conducted by an interviewer and audio computer- assisted self-interviewing. Because of the COVID-19 pandemic, an additional web data collection mode was also used to collect 2020 survey data.

 NSDUH measures:

·         use of illegal drugs, prescription drugs, alcohol, and tobacco and misuse of prescription drugs

·         substance use disorder and substance use treatment major depressive episode and depression care

·         serious psychological distress, mental illness, and mental health care

The data provide estimates of substance use and mental illness at the national, state, and substate levels. NSDUH data also help to identify the extent of substance use and mental illness among different subgroups, estimate trends over time, and determine the need for treatment services.

 Statistics On Alcohol Addiction And Abuse

Alcohol is the most widely-abused substance in the US, yet alcoholism is often left untreated. An addiction to alcohol can be detrimental to a person’s physical, mental, and social wellbeing.

·         Every year, worldwide, alcohol is the cause of 5.3% of deaths (or 1 in every 20).

·         About 300 million people throughout the world have an alcohol use disorder.

·         On average, 30 Americans die every day in an alcohol-related car accident, and 6 Americans die every day from alcohol poisoning.

·         About 88,000 people die as a result of alcohol every year in the United States.

·         About 6% of American adults (about 15 million people) have an alcohol use disorder; only about 7% of those people ever get treatment.

·         Men between the ages of 18 and 25 are most likely to binge drink and become alcoholics.

  Statistics On Nicotine Addiction And Abuse

As of 2019, anyone over the age of 21 in the US can easily purchase a box of cigarettes. Although cigarettes are legal and accessible, they cause a variety of fatal health conditions and are also addictive.

·         About 34 million Americans smoke cigarettes.

·         Each day, roughly 1,600 young people smoke a cigarette for the first time.

·         About 15% of American men and about 13% of American women smoke cigarettes.

·         People who are disabled, live below the poverty line, or lack a college education are more likely to smoke cigarettes.

·         Over 16 million Americans have a smoking-related illness.

·         Smoking cigarettes is the cause of over 480,000 deaths every year in the United States.

Drug Abuse Demographics

Drug abuse and substance disorders are more likely to affect young males

·         22% of males and 17% of females used illegal drugs or misused prescription drugs within the last year.

·         5% of people in non-metropolitan, rural counties used illegal drugs compared to 20.2% of people in larger metropolitan counties.

·         Drug use is highest among persons between the ages of 18-25 at 39% compared to persons aged 26-29, at 34%.

·         70% of users who try an illegal drug before age 13 develop a substance abuse disorder within the next 7 years compared to 27% of those who try an illegal drug after age 17.

·         47% of young people use an illegal drug by the time they graduate from high school; other users within the last 30 days include:

o    5% of 8th graders.

o    20% of 10th graders.

o    24% of 12th graders.

Starting 2014, NSDUH introduced an independent multistage area probability sample within each state and D.C. States are the first level of stratification, and each state was then stratified into approximately equally populated state sampling regions (SSRs). Census tracts within each SSR were then selected, followed by census block groups within census tracts and area segments (i.e., a collection of census blocks) within census block groups. Finally, dwelling units (DUs) were selected within segments, and within each selected DU, up to two residents who were at least 12 years old were selected for the interview. Professional interviewers conduct the face-to-face surveys, and the data are used to support prevention and treatment programs, monitor substance use trends, estimate the need for treatment, and inform public health policy.

NSDUH is representative of persons aged 12 and over in the civilian noninstitutionalized population of the United States, and in each state and the District of Columbia (D.C.). The survey covers residents of households (including those living in houses, townhouses, apartments, and condominiums), persons in noninstitutional group quarters (including those in shelters, boarding houses, college dormitories, migratory work camps, and halfway houses), and civilians living on military bases. Persons excluded from the survey include people experiencing homelessness who do not use shelters, active military personnel, and residents of institutional group quarters such as jails, nursing homes, mental institutions, and long-term care hospitals.

The Federal Government has conducted the survey since 1971. Over the years, the survey has undergone a series of changes. In 1999, the survey shifted from paper-and-pencil data collection to computer-assisted interviewing (CAI). With CAI, staff administer most questions with audio computer-assisted self-interviewing. This provides a confidential way to answer questions and encourages honest responses.

In 1999, the sample design expanded to include all 50 states and the District of Columbia. In 2002, the name of the survey changed from the National Household Survey on Drug Abuse to NSDUH. The survey also began including a $30 incentive for respondents.

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USING STATISTICS IN MACHINE LEARNING

Statistics a subfield of Mathematics. Statistical modeling is a formalization of relationships between variables in the data in the form of mathematical equations. There are two major schools of thought: Frequentist and Bayesians (based on probability — another subfield of Mathematics that deals with predicting the likelihood of future events). ISS coaching in Lucknow further explains that statistics is usually applied to low-dimension problems when you need to know more about data and properties of estimators. Common examples of estimator properties include p-value, standard deviation, confidence interval or unbiased estimator.

Machine Learning (ML) is a subfield of computer science and artificial intelligence. ML deals with building systems (algorithms, models) that can learn from data and observations, instead of explicitly programmed.

Machine learning focus on algorithms, and a subset of these has as their objective to prediction some outcome based on a set of inputs (or predictors as we might call them in statistics). In contrast to parametric statistical models, these algorithms typically do not make rigid assumptions about the relationships between the inputs and the outcome, and therefore can perform well then the dependence of the outcome on the predictors is complex or non-linear. The potential to capture such complex relationships is however not unique to machine learning – within statistical models we have flexible parametric / semiparametric, and even non-parametric methods such as non-parametric regression.

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.

You need Statistics for machine learning because with a decent understanding of statistical methods you can convert raw observations into information that is easy to understand, digest, and share. This will allow you to create machine learning models that will consistently deliver results.

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.

 In Machine Learning, Data Analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information by informing conclusions and supporting decision making. It is used in many interdisciplinary fields such as Artificial Intelligence, Pattern Recognition, Neural Networks, etc…

The major difference between machine learning and statistics is their purpose. Machine learning models are designed to make the most accurate predictions possible. Statistical models are designed for inference about the relationships between variables

The machine learning pipeline is nothing but the workflow of the Machine Learning process starting from Defining our business problem to Deployment of the model. In the Machine Learning pipeline, the data preparation part is the most difficult and time-consuming one as the data is present in an unstructured format and it needs some cleaning. 

Data collection in ML

As we all know 21th century is the known as ” Age of Data Abundance”. The collection of data is the collection of mosaic pieces. How we arrange this data to get useful insights is what machine learning provides us!!

you need statistics for machine learning. Both fields of study are highly intertwined, to the point that some statisticians refer to machine learning as statistical learning or applied statistics—instead of the name that is designed to sound a bit more computer-centric.

When getting started with machine learning, the bulk of the texts assume that you already have some statistics foundation, highlighting how it’s hard to have a sound foundation in machine learning without it.

These are just some examples showing that you need some basic understanding of statistics to properly understand machine learning. Almost anyone can apply an algorithm lifted off different sources to a dataset and claim proficiency in machine learning.

However, without adequate knowledge of statistics, you’ll find out that you can’t interpret logistic regression results. You’ll also see a poor performance from your models because you’ve failed to normalize predictors, and you’re likely using the incorrect splitting criterion with your tree-based models. You need a proper background in statistics to avoid these problems.

 Raw observations are just data. They are not pieces of information or knowledge. With every dataset, there are a few questions that have to be answered: What does the data look like? Are there any limits on the observation? What observation is most common? 

Away from raw data, you may need to design an experiment that will help you to collect observations. The result of the experiment will raise more questions like the difference in the outcome of the two experiments and whether these differences are noise in the data or real. You’ll also need to know what variables in the experiment are most relevant.

By answering these questions, you can turn the raw observation into usable information. The results generated will be vital to the project. It will also matter to your stakeholders because the information generated will ensure better decision making overall.

So, to understand the data used in training a machine learning model and properly interpret the results, you’ll need statistics. Every step in a typical predictive modeling project will involve some use of a statistical method.

Many machine learning techniques are drawn from statistics (e.g., linear regression and logistic regression), in addition to other disciplines like calculus, linear algebra, and computer science. But it is this association with underlying statistical techniques that causes many people to conflate the disciplines.  

Interestingly, newer machine learning engineers and data scientists who use machine learning packages like scikit-learn in Python may be unaware of the underlying relationship between machine learning and statistics. 

This abstraction of machine learning from statistics with the use of libraries is often why some individuals make the argument that knowledge of statistics is not necessary to do machine learning. While this may be true for more basic tasks, experienced data scientists and machine learning engineers draw on their knowledge probability and statistics to develop models. 

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GDP Growth Of India

India's economy expanded by 8.4 percent year-on-year in July-September 2021, following a record 20.1 percent growth in the previous three-month period and matching market expectations. The reading marked a fourth straight quarter of expansion, as coronavirus-related disruptions continued to ease and as the economic activity rebounded helped by a faster pace of vaccinations and a drop in cases. By sectors, service activity growth was supported by increases in trade, hotels, transport & communication (8.2% vs 34.3%), financial, real estate & professional services (7.8% vs 3.7%), and public administration, defense & other services (17.4% vs 5.8%). In addition, output rose for manufacturing (5.5% vs 49.6%), mining & quarrying (15.4% vs 18.6%), utilities (8.9% vs 14.3%), construction (7.5% vs 68.3%), and agriculture (4.5%, the same as in July-September). The Reserve Bank of India has forecast annual growth of 9.5 percent in the current fiscal year. ISS coaching in Lucknow explains and covers in this article India’s GDP growth rate – whether headed up or down ?

As things stand in India, when we say that the Indian economy grew by 10 per cent in a particular quarter (that is, a period of three months) what it essentially means is that the total GDP of the country in that quarter was 10 per cent more than the total GDP produced in the same quarter a year ago.

Similarly, when we say the economy contracted by 8 per cent this year what we mean to say is that the total output of the economy (as calculated by GDP) is 8 per cent less than the total output of the economy in the preceding year.

This is called the year-on-year (YoY) method of arriving at the growth rate.

But this is not the only way to arrive at a growth rate. One could have compared GDP quarter-on-quarter (QoQ) — that is, compare the GDP in the current quarter with the GDP in the preceding quarter. For that matter, theoretically speaking, if the data were available, one could calculate the growth rate month-on-month (MoM) or even week-on-week.

 India GDP Annual Growth Rate

The most important and the fastest growing sector of Indian economy are services. Trade, hotels, transport and communication; financing, insurance, real estate and business services and community, social and personal services account for more than 60 percent of GDP. Agriculture, forestry and fishing constitute around 12 percent of the output, but employs more than 50 percent of the labor force. Manufacturing accounts for 15 percent of GDP, construction for another 8 percent and mining, quarrying, electricity, gas and water supply for the remaining 5 percent.

India GDP Grows 8.4% in July-September

India's economy expanded by 8.4 percent year-on-year in July-September 2021, following a record 20.1 percent growth in the previous three-month period and matching market expectations. The reading marked a fourth straight quarter of expansion, as coronavirus-related disruptions continued to ease .

 Indian Economy Expands at a Record 20.1% in Q2

The Indian economy expanded at a record 20.1% year-on-year in Q2 2021, slightly higher than market forecasts of 20%, amid a low base effect from last year and despite a second wave of covid-19 infections and localised lockdowns.

 India GDP Growth Beats Forecasts at 1.6% in Q1

The Indian economy expanded 1.6% year-on-year in Q1 2021, accelerating from an upwardly revised 0.5% growth in Q4 and beating market forecasts of 1%. It was the 2nd straight quarter of growth since the country exit a pandemic-induced recession.

 Commenting on India’s GDP outlook, Barnabas Gan, an economist at UOB, noted: “In a nutshell, India’s growth prospects will depend largely on how Covid-19 evolves. India’s GDP had expanded strongly from its full-year contraction of 7.3% in FY2020/21, and anecdotal evidence from lower Covid-19 infections and higher vaccination rates are credible signals that the economy is gearing towards a more resilient growth pattern. On the back of an accommodative monetary policy expected in the year ahead, coupled with a strong fiscal response as seen from the Union Budget, we keep to our full-year growth outlook of 8.5% in FY2021/22.”

FocusEconomics panelists project GDP to expand 9.0% in FY 2021, which is down 0.2 percentage points from last month’s forecast, and increase 7.3% in FY 2022, which is up 0.2 percentage points from the previous month’s estimate.

The Economic Survey has projected the Indian economy will grow between 8 and 8.5 per cent in 2022-23, amid expectations of recovery in momentum due to the benefits of the supply-side reforms announced by the Narendra Modi government in the last two years.

However, the survey added the caveat that its projections are based on the assumption that with Covid-19 infections dipping, there won’t be further pandemic-related disruptions, and oil prices will remain in the $70-75/barrel range, among others. The price hit a fresh seven-year high of $88.85 per barrel Friday, the highest level since October 2014, according to data from the Petroleum Planning and Analysis Cell.

“This projection is also based on the assumption that there will be no further debilitating pandemic-related economic disruption, monsoon will be normal, withdrawal of global liquidity by major central banks will be broadly orderly, oil prices will be in the range of US$70-$75/bbl, and global supply chain disruptions will steadily ease over the course of the year,” said the survey. 

Finance Minister Nirmala Sitharaman tabled the Economic Survey 2021-22 in Parliament Monday ahead of the Union Budget 2022-23, which she will present Tuesday.

According to the annual document, which gives projections for the forthcoming fiscal and presents a review of the financial year gone by, the supply-side reforms undertaken by the government over the last two years include deregulation of numerous sectors, simplification of processes, removal of legacy issues like ‘retrospective tax’, privatisation, production-linked incentives and so on. 

While the projected GDP growth for FY23 will make India the fastest growing economy in the world even next year, it is still below the International Monetary Fund (IMF)’s projections of 9 per cent.

The GDP growth in the ongoing fiscal (2021-22) is expected at 9.2 per cent, according to the National Statistical Office (NSO), while the Reserve Bank of India (RBI) has pegged it at 9.5 per cent.

At 9.2 per cent, India’s GDP growth in 2021-22 will be the fastest in at least 17 years. It had contracted by a record 7.3 per cent in 2020-21.

“Overall, macroeconomic stability indicators suggest that the Indian economy is well-placed to take on the challenges of 2022-23,” said the survey.

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THE EFFECTS OF PROBABILITY ON BUSINESS DECISIONS

Many businesses apply the understanding of uncertainty and probability in their business decision practices. While your focus is on formulas and statistical calculations used to define probability, underneath these lie basic concepts that determine whether -- and how much -- event interactions affect probability. Together, statistical calculations and probability concepts allow you to make good business decisions, even in times of uncertainty. Probability models can greatly help businesses in optimizing their policies and making safe decisions. Though complex, these probability methods can increase the profitability and success of a business. In this article, ISS coaching in Lucknow highlights how analytical tools such as probabilistic modeling can be effectively used for dealing with uncertainty.

THE ROLE OF PROBABILITY DISTRIBUTION IN BUSINESS MANAGEMENT

 Sales Predictions

A major application for probability distributions lies in anticipating future sales incomes. Companies of all sizes rely on sales forecasts to predict revenues, so the probability distribution of how many units the firm expects to sell in a given period can help it anticipate revenues for that period. The distribution also allows a company to see the worst and best possible outcomes and plan for both. The worst outcome could be 100 units sold in a month, while the best result could be 1,000 units sold in that month.

Risk Assessments

Probability distributions can help companies avoid negative outcomes just as they help predict positive results. Statistical analysis can also be useful in analyzing outcomes of ventures that involve substantial risks. The distribution shows which outcomes are most likely in a risky proposition and whether the rewards for taking specific actions compensate for those risks. For instance, if the probability analysis shows that the costs of launching a new project is likely to be $350,000, the company must determine whether the potential revenues will exceed that amount to make it a profitable venture.

Probability Distribution

A probability distribution is a statistical function that identifies all the conceivable outcomes and odds that a random variable will have within a specific range. This range is determined by the lowest and highest potential values for that variable. For instance, if a company expects to bring in between $100,000 and $500,000 in monthly revenue, the graph will start with $100,000 at the low end and $500,000 at the high end. The graph for a typical probability distribution resembles a bell curve, where the least likely events fall closest to the extreme ends of the range and the most likely events occur closer to the midpoint of the extremes.

Investment

The optimization of a business’s profit relies on how a business invests its resources. One important part of investing is knowing the risks involved with each type of investment. The only way a business can take these risks into account when making investment decisions is to use probability as a calculation method. After analyzing the probabilities of gain and loss associated with each investment decision, a business can apply probability models to calculate which investment or investment combinations yield the greatest expected profit.

Customer Service

Customer service may be physical customer service, such as bank window service, or virtual customer service, such as an Internet system. In either case, probability models can help a company in creating policy related to customer service. For such policies, the models of queuing theory are integral. These models allow companies to understand the efficiency related to their current system of customer service and make changes to optimize the system. If a company encounters problems with long lines or long online wait times, this may cause the company to lose customers. In this situation, queuing models become an important part of problem solving.

Competitive Strategy

Although game theory is an important part of determining company strategy, game theory lacks the inclusion of uncertainty in its models. Such a deterministic model can't allow a company to truly optimize its strategy in terms of risk. Probability models such as Markov chains allow companies to design a set of strategies that not only account for risk but are self-altering in the face of new information regarding competing companies. In addition, Markov chains allow companies to mathematically analyze long-term strategies to find which ones yield the best results.

Product Design

Product design, especially the design of complicated products such as computing devices, includes the design and arrangement of multiple components in a system. Reliability theory provides a probabilistic model that helps designers model their products in terms of the probability of failure or breakdown. This model allows for more efficient design and allows businesses to optimally draft warranties and return policies.

ABOUT PROBABILITY, STATISTICS AND CHANCE

Probability concepts are abstract ideas used to identify the degree of risk a business decision involves. In determining probability, risk is the degree to which a potential outcome differs from a benchmark expectation. You can base probability calculations on a random or full data sample. For example, consumer demand forecasts commonly use a random sampling from the target market population. However, when you’re making a purchasing decision based solely on cost, the full cost of each item determines which comes the closest to matching your cost expectation.

Mutual Exclusivity

The concept of mutually exclusivity applies if the occurrence of one event prohibits the occurrence of another event. For example, assume you have two tasks on your to-do list. Both tasks are due today and both will take the entire day to complete. Whichever task you choose to complete means the other will remain incomplete. These two tasks can’t have the same outcome. Thus, these tasks are mutually exclusive.

Dependent Events

A second concept refers to the impact two separate events have on each other. Dependent events are those in which the occurrence of one event affects -- but doesn't prevent -- the probability of the other occurring. For example, assume a five-year goal is to purchase a new building and pay the full purchase price in cash. The expected funding source is investment returns from excess sales revenue investments. The probability of the purchase happening within the five-year period depends on whether sales revenues meet projected expectations. This makes these dependent events.

Interdependent Events

Interdependent events are those in which the occurrence of one event has no effect of the probability of another event. For example, assume consumer demand for hairbrushes is falling to an all-time low. The concept of interdependence says that declining demand for hairbrushes and the probability that demand for shampoo will also decline share no relationship. In the same way, if you intend to purchase a new building by investing personal funds instead of relying on investment returns from excess sales revenues, the purchase of a new building and sales revenues share no relationship. Thus, these are now interdependent events.

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EFFECTIVE USES OF STATISTICS IN KEY BUSINESS DECISIONS

Operating a business of any size is a complex undertaking. In addition to day-to-day responsibilities, your company must engage in long-term planning, develop new products or services, streamline production or delivery and locate new customers while serving existing clients. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them.

Effective uses of statistics in key business decisions

A recent study by ISS coaching in Lucknow found that by 2023, “more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling,” endeavors that only succeed with the contributions of statisticians. Statistical research gives managers the information they need to make informed decisions in uncertain circumstances. When managers analyze statistical research in business, they determine how to proceed in areas including auditing, financial analysis and marketing research. Future business professionals need to recognize the importance of statistics in creating accurate predictions. Companies that rely on analytics can be more effective when they work with the right statistics.

What Are Business Statistics?

Statistical research in business enables managers to analyze past performance, predict future business practices and lead organizations effectively. Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand.

Descriptive analytics look at what has happened and helps explain why. By using historical data, managers can analyze past successes and failures. This is also called “cause and effect analysis.” Some common applications of descriptive analytics include sales, marketing, finance and operations.

Predictive analytics uses a variety of statistical techniques (such as modeling and data mining) to predict future probabilities and trends based on historical data. This goes beyond reporting what has happened to create best estimates for what will happen. Some common applications of predictive analysis include fraud detection and security, risk assessment, marketing and operations.

Prescriptive analytics is the stage of determining the best course of action in a given business situation. This includes knowing what may happen, why it may happen, and how to navigate it. Constantly updating information changes prescriptive analysis, allowing managers to maintain action plans for their organizations in real-time.

Mean, Median and Mode

Those who use statistical research in business should be familiar with how statistics are calculated, including how the mean, median and mode work together to create meaning from a set of numbers. The mean is an average of a set of numbers, the median is the middle number within a set of numbers and the mode is the most common number in a set.

Successful managers understand that these concepts work in concert to create an accurate picture of a business’s condition.

Responsibility With Statistics

According to Six Sigma Online, managers should be prepared when they use statistical research in business to explain the research to other stakeholders and vouch for its authenticity. It is important to know the source of the data and ask questions such as What does this research represent, and why was it generated? Was the person who compiled this data capable of doing so, and were they unbiased?

Studying Statistics

Computer software makes analytics very accessible. Desktop tools can help create reports, charts and graphs to represent information visually, which helps communicate its meaning.Business professionals must master all of the tools available to them, including statistical research in business, in order to help their organizations succeed.

Focusing on Big Picture

Statistical analysis of a representative group of consumers can provide a reasonably accurate, cost-effective snapshot of the market with faster and cheaper statistics than attempting a census of very single customer a company may ever deal with. The statistics can also afford leadership an unbiased outlook of the market, to avoid building strategy on uncorroborated presuppositions.

Evidence to Substantiate Positions

Statistics back up assertions. Leaders can find themselves backed into a corner when persuading people to move in a direction or take a risk based on unsubstantiated opinions. Statistics can provide objective goals with stand-alone figures as well as hard evidence to substantiate positions or provide a level of certainty to directions to take the company.

For example, you may find it easier to convince board members of the value of international expansion by providing data on the available market for products in a given country. Break down demographics, average income and competitor products in the country.

Making Connections Between Variables

Statistics can point out relationships. A careful review of data can reveal links between two variables, such as specific sales offers and changes in revenue or dissatisfied customers and products purchased. Delving into the data further can provide more specific theories about the connections to test, which can lead to more control over customer satisfaction, repeat purchases and subsequent sales volume. For example, a free gift with purchase offer may drive more sales than a discount period.

Ensuring Product Quality

Anyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. Statistics provide the means to measure and control production processes to minimize variations, which lead to error or waste, and ensure consistency throughout the process. This saves money by reducing the materials used to make or remake products, as well as materials lost to overage and scrap, plus the cost of honoring warranties due to shipping defective products.

 Additional Considerations when Using Statistics

Know what to measure, and manage the numbers; don’t let the numbers do the managing for you, or of you. Before using statistics, know exactly what to ask of the data. Understand what each statistical tool can and can’t measure; use several tools that complement one another. For example, don’t rely exclusively on an "average," such as a mean rating.

Customers using a five-point scale to rate satisfaction won’t give you a 3.84; that may indicate how the audience as a group clustered, but it’s also important to understand the width of the spread using standard deviation or which score was used by the greatest number of people, by noting the mode. Finally, double-check the statistics by perusing the data, particularly its source, to get a sense of why the audiences surveyed answered the way they did.

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THE PROCESS OF SAMPLING BUSINESS DATA

What is sampling? In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. Let’s look at sampling in more detail and discuss the most popular types of sampling used in market research.

It would be expensive and time-consuming to collect data from the whole population of a market. Therefore, market researchers make extensive of sampling from which, through careful design and analysis, marketers can draw information about their chosen market.

SAMPLE DESIGN

Sample design covers:

●Method of selection

●Sample structure

●Plans for analyzing and interpreting the results.

Sample designs can vary from simple to complex. They depend on the type of information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out; in simple terms the more the precision the market researcher requires, the more complex the design and the larger the sample size will be.

The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative. It may be necessary to draw a larger sample than would be expected from some parts of the population: for example, to select  more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection. This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.

  Defining The Population

The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible. This is to ensure that all elements within the population are represented.

The target population is sampled using a sampling frame.

Often, the units in the population can be identified by existing information such as pay-rolls, company lists, government registers, etc.

A sampling  frame could also be geographical. For example, postcodes have become a well-used means of selecting a sample.

Sample Size

If you’re conducting a survey, as ISS coaching in Lucknow, is, then you need to consider a few factors when determining sample size. For any sample design, deciding upon the appropriate sample size will depend on several key factors:

1.                  No estimate taken from a sample is expected to be exact: assumptions about the overall population based on thr results of a sample will have an attached margin of error.

2.                  To lower the margin of error usually requires a larger sample size: the amount of variability in the population , i.e., the range of values or opinions, will also affect accuracy and therefore size of the sample.

3.                  The confidence level is the likelihood that the results obtained from the sample lie within a required precision: the higher the confidence level, the more certain you wish to be that the results are not atypical. Statisticians often use a 95% confidence level to provide strong conclusions.

4.                  Population size does not normally affect sample size: in fact the larger the population size, the lower the proportion of that population needs to be sampled to be representative. It’s only when the proposed sample size is more than 5% of the population that the population size becomes part of the formulae to calculate the sampe size.

  Types of Sampling

There are many different types of sampling methods, here’s a summary of the most common:

 1.                  CLUSTER SAMPLING

 Cluster sampling also involves dividing the population into subgroups , but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

Units in the population can often be found in certain geographic groups or  “ clusters”

for example , primary school children in Derbyshire.

A random sample of clusters is taken , then all units within the cluster are examined.

Advantages

 ●Quick and easy

●Doesn’t need complete population information

●Good for face-to-face surveys

 Disadvantages

●Expensive if the clusters are large

●Greater risk of sampling error

 2.    CONVENIENCE SAMPLING

Uses those who are willing to volunteer and easiest to involve in the study.

Advantages

●Subjects are readily available.

●Large amounts of information can be gathered quickly.

 Disadvantages

●The sample is not representative of the entire population, so results can’t speak for them- inferences are limited.

●Prone to volunteer bias.

 3.    JUDGEMENT SAMPLING

 A deliberate choice of a sample- the opposite of random

Advantages

●Good for providing illustrative examples or case studies

Disadvantages

●Very prone to bias

●Sample often small

●Cannot extrapolate from sample

 4.   QUOTA SAMPLING

The aim is to obtain a sample that is “representative” of the overall population.

The population is divided (“stratified”) by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.

 Advantages

● Quick and easy way of obtaining a sample.

 Disadvantages

 ●Not random, so some risk of bias

●Need to understand the population to be able to identify the basis of stratification

 5. SIMPLY RANDOM SAMPLING

This makes sure that every member of the population has an equal chance of selection.

Advantages

●Simple to design and interpret

●Can calculate both estimate of the population and sampling error

 Disadvantages

 ●Need a complete and accurate population listing

●May not be practical if the sample requires lots of small visits over the country

 6.   SYSTEMATIC SAMPLING

After randomly selecting a starting point from the population between 1 and *n, every nth unit is selected.*n equals the population size divided by the sample size.

 Advantages

 ●Easier to extract the sample than via simple random

●Ensures sample is spread across the population

 Disadvantages

● Can be costly and time-consuming if the sample is not conveniently located.

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How to ace the JEE Main entrance exam?

With the Board exams getting over or in last phase of getting completed, this is the most important time for the next big step -- taking the various entrance exams. One of the most sought after exams for the 12th standard after Board exams for the Science stream is the JEE Main exams for Engineering and AIPMT for the Medical Stream. The JEE Main exam is being taken by more than 12 lakhs students with majority of them taking the paper based offline mode on 3rd April and a smaller chunk taking the online mode on 9th and 10 April. The number of takers have come down in comparison to last year as the state of Maharashtra have opted out of the JEE Main for filling in their engineering admission. The cutoff have seen a downward trend in the last couple of years and hopefully with the number of takers getting lesser and the number of students eligible for taking JEE Advanced being increased to 2 lakhs from the current 1.5 lakhs, there may be a dip in the cutoff mark if the same standard of difficulty is maintained with other parameters remaining the same. Get detailed information about the JEE Main exam pattern from ISS coaching in Lucknow in this article.

JEE Main Tips and Test taking strategies So what are the best possible way to clear the exam -- tips for last minute preparation. The paper being based on 11th and 12th syllabus, the students should look at giving equal emphasis on both the standards. With 12th board exam being just concluded or getting completed, the students can look at solving application oriented problems from the 12th topic in the beginning and look at the 11th standard topics subsequently.

Topics to Focus under Respective Subject 

How to prepare for JEE Main Physics? With certain topics in Physics like Mechanics, Heat and Thermodynamics, Electricity and Magnetism forming the major part in the last couple of years, the students can attempt these topics before looking at the other areas. 

How to prepare for JEE Main Mathematics? Similarly in case of Mathematics students can look at Algebra, Differential Calculus and Coordinate Geometry. 

How to prepare for JEE Main Chemistry? In case of Chemistry, application level questions are usually expected from Physical and Organic Chemistry. The paper in last couple of years had been more or less equally spread across the three areas of Organic, Inorganic and Physical Chemistry. Hence emphasis on all areas is needed.

Board Exam vs Entrance exam

 The students would need to shift the mindset from the board exam perspective where one is required to attempt all the questions to a mindset where the student can choose to leave a few questions or attempt lesser number of questions, as incorrect answer carries a negative mark. More often than not, students attempt all/higher number of questions and due to negative marking scheme, their final score becomes lesser than what they would have got if they had attempted fewer questions. One should have a clear goal or mentally prepared on how much they plan to attempt before they take the exam as it would give them some specific target/focus and would in turn improve their performances as a well directed mind always result in better performance. The cutoff being in the range of 30-40% of the total mark for the general category, one would need to look at solving correctly around 35-40 questions. However for admission to NITs, marks in excess of 50% of the total mark would be a better bet, with other factors like JEE paper being of similar standard as the last couple of years and a decent board marks -- which varies for different Board. Usually the paper consists of three category of questions - easy, medium and difficult. The challenge would be to identify easy questions and then move on the next level of question and so on. However, the identification of a question into a category would come by practice only. The thumb rule would be to identify topics in which one is strong and attempt questions on those topics/areas before moving on the next range of topics. One should look at solving a minimum of 30-40 questions and then look at attempting the other questions. With cutoff being in the total and not on individual subjects, students can look at attempting question in those subject which they feel they are confident. Students who are taking online mode will have an advantage of seeing the offline paper and hence a better idea than those attempting offline mode. Invest time in scanning at all the questions before answering. You do not need to attempt the questions in the same sequence as given. Please read the questions and look at all the possible answer choices given before attempting as one may get some idea looking at the answer choices. Similarly, you may not have to solve the entire steps to find the correct answer, you may use the approximation to arrive at an answer in case the answer choices options are not very close. Approximation saves a lot of time and time management is very critical in competitive exams as difference in even a mark may cost you a seat or a branch. 

 Do and Donts on the day of exam:

Please have a sound sleep and do not work on the last minute!!! Start early to reach the venue before time as we had cases of students reaching late and denied an entry in the exam hall. Please do not discuss preparation with anybody before the exams and compare your preparation with them. Best possible way would be to avoid conservation and focus your attention and energy on the impending exam. In the exam hall devote time to scan the paper before attempting and do not get influenced by the other person pace of marking in the OMR sheet. Do not bog down in one question while attempting and in case you feel, you are spending too much time, go to the next one. The challenge is not on the number of questions that one has but the time. You have 90 questions to be solved in 180 minutes and also you may not have to attempt/solve all the questions to clear the cutoff.

One needs to be conscious of the time more than the number of questions.

Having a clear plan on the time one should spend on each subject before attempting the paper would be better strategy than with no plan. Look at answer choices as you continue solving and going through the various steps. Also do not discuss the paper after the exams as it might influence you in the negative way and may affect your next entrance exam in case you are attempting other entrance exams as well. Most important thought one needs to have is that one exam does not decide one s career and do not get anxious and stressed before the exam and keep yourself relaxed as much as possible as our mind works best when one is relaxed. Wishing you all the best and happy examining! 

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KINDS OF DATA IN BUSINESS STATISTICS

Introduction

 Data types are important concepts in statistics, they enable us to apply statistical measurements correctly on data and assist in correctly concluding certain assumptions about it.

 ISS coaching in Jaipur will introduce you to the different data types which is significantly essential for doing Exploratory Data Analysis or EDA since you can use certain factual measurements just for particular data types. 

 Similarly, you need to know which data analysis and its type you are working to select the correct perception technique. You can consider data types as an approach to arrange various types of variables. 

 QUANTITATIVE DATA

Quantitative data seems to be the easiest to explain. It answers key questions such as “how many, “how much” and “how often”.

Quantitative data can be expressed as a number or can be quantified. Simply put, it can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented by a wide variety of statistical types of graphs and charts such as line, bar graph, scatter plot, and etc.

Examples of quantitative data:

§  Scores on tests and exams e.g. 85, 67, 90 and etc.

§  The weight of a person or a subject.

§  Your shoe size.

§  The temperature in a room.

There are 2 general types of quantitative data: discrete data and continuous data. 

 Discrete vs Continuous Data

As we mentioned above discrete and continuous data are the two key types of quantitative data.

In statistics, marketing research, and data science, many decisions depend on whether the basic data is discrete or continuous.

Discrete data

Discrete data is a count that involves only integers. The discrete values cannot be subdivided into parts.

For example, the number of children in a class is discrete data. You can count whole individuals. You can’t count 1.5 kids.

To put in other words, discrete data can take only certain values. The data variables cannot be divided into smaller parts.

It has a limited number of possible values e.g. days of the month.

Examples of discrete data:

§  The number of students in a class.

§  The number of workers in a company.

§  The number of home runs in a baseball game.

§  The number of test questions you answered correctly

Continuous data

Continuous data is information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value.

For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc.

You can record continuous data at so many different measurements – width, temperature, time, and etc. This is where the key difference from discrete types of data lies.

The continuous variables can take any value between two numbers. For example, between 50 and 72 inches, there are literally millions of possible heights: 52.04762 inches, 69.948376 inches and etc.

A good great rule for defining if a data is continuous or discrete is that if the point of measurement can be reduced in half and still make sense, the data is continuous.

Examples of continuous data:

§  The amount of time required to complete a project.

§  The height of children.

§  The square footage of a two-bedroom house.

§  The speed of cars.

 QUALITATIVE DATA

Qualitative data can’t be expressed as a number and can’t be measured. Qualitative data consist of words, pictures, and symbols, not numbers.

Qualitative data is also called categorical data because the information can be sorted by category, not by number.

Qualitative data can answer questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

§  Colors e.g. the color of the sea

§  Your favorite holiday destination such as Hawaii, New Zealand and etc.

§  Names as John, Patricia,…..

§  Ethnicity such as American Indian, Asian, etc.

More you can see on our post qualitative vs quantitative data.

There are 2 general types of qualitative data: nominal data and ordinal data. 

  Nominal vs Ordinal Data

Nominal data

Nominal data is used just for labeling variables, without any type of quantitative value. The name ‘nominal’ comes from the Latin word “nomen” which means ‘name’.

The nominal data just name a thing without applying it to order. Actually, the nominal data could just be called “labels.”

Examples of Nominal Data:

§  Gender (Women, Men)

§  Hair color (Blonde, Brown, Brunette, Red, etc.)

§  Marital status (Married, Single, Widowed)

§  Ethnicity (Hispanic, Asian)

As you see from the examples there is no intrinsic ordering to the variables.

Eye color is a nominal variable having a few categories (Blue, Green, Brown) and there is no way to order these categories from highest to lowest.

Ordinal data

Ordinal data shows where a number is in order. This is the crucial difference from nominal types of data.

Ordinal data is data which is placed into some kind of order by their position on a scale. Ordinal data may indicate superiority.

However, you cannot do arithmetic with ordinal numbers because they only show sequence.

Ordinal variables are considered as “in between” qualitative and quantitative variables.

In other words, the ordinal data is qualitative data for which the values are ordered.

In comparison with nominal data, the second one is qualitative data for which the values cannot be placed in an ordered.

We can also assign numbers to ordinal data to show their relative position. But we cannot do math with those numbers. For example: “first, second, third…etc.”

Examples of Ordinal Data:

§  The first, second and third person in a competition.

§  Letter grades: A, B, C, and etc.

§  When a company asks a customer to rate the sales experience on a scale of 1-10.

§  Economic status: low, medium and high.

 CONCLUSION

§  All of the different types of data have a critical place in statistics, research, and data science.

§  Data types work great together to help organizations and businesses from all industries build successful data-driven decision-making process.

§  Working in the data management area and having a good range of data science skills involves a deep understanding of various types of data and when to apply them.

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The Importance Of Statistics To Business In 2022

Statistical research gives managers the information they need to make informed decisions in uncertain circumstances. When managers analyze statistical research in business, they determine how to proceed in areas including auditing, financial analysis and marketing research.Future business professionals need to recognize the importance of statistics in creating accurate predictions. Companies that rely on analytics can be more effective when they work with the right statistics.Statistical research in business enables managers to analyze past performance, predict future business practices and lead organizations effectively. Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand. For anyone who’s new to the concept of importance of Statistics to industry and business, ISS coaching in Lucknow has prepared a brief intro on the topic.

A defining business trend in the Digital Age has been the growth in the volume and the use of quantitative data. Increasingly, decisions once based on management intuition and experience now rely on empirical evidence drawn from statistical data. As the volume of data sets grow larger, the term "big data" has now become entrenched in businesses worldwide, large and small. Statistical evidence can inform business leaders about how their companies perform, the effectiveness of their business operations and information about their customers.

Performance Measurement

The late management guru Peter Drucker once said that what gets measured in business is what gets done. With this in mind, many business leaders rely on key performance indicators, or KPIs, to measure how well their companies operate. The Balanced Scorecard Institute reported that KPIs enable companies to measure results and determine what successful operations look like. Examples of KPIs include quarterly profits, customer satisfaction, and project completion rates, all of which can be quantitatively measured. KPIs require reliable statistical data, which companies then analyze on a regular basis to determine if they are meeting success measures.

 Forecasting

Managers analyze past data to find statistical trends and make predictions about the future. For example, you might analyze the previous sales of all products sold to make estimates about the volume of future sales under specific economic conditions. In turn, these projections would then be used to set up production schedules.

As an example, consider the farmer who has to decide whether to plant soybeans or corn. Of course, the farmer wants to maximize the number of bushels produced under good or bad weather conditions; each weather condition has a certain probability of occurring. An analysis of historical data will show the volume of soybeans or corn produced over a range of weather patterns in a particular geographical area. From this statistical model, the farmer can make an informed decision about which product to plant.

Risk/Return on Investments

The objective of a new capital expenditure project is to optimize the return on the investment and minimize the risk. Statistical methods can allow a manager to evaluate the project under different economic environments, changing consumer preferences and strength of the competition.

Market Research

Companies use statistics in market research and new product development. They take random surveys of consumers to gauge the market acceptance and potential for a proposed product. Managers want to know if there will be enough demand for the product. Is there enough demand to justify spending money to develop the product and, ultimately, to build a plant to produce it? From the statistical analysis, a break-even model is constructed to determine the volume of sales necessary for the product to succeed.

Importance of Statistics in Industry

Statistics not only help measure business performance, but can also provide a means for boosting it. Management consulting giant McKinsey and Company calls statistical data a frontier for business innovation, reporting that, as companies collect and store more data, they can gain insight into such issues as employee sick days and product inventories, looking for ways to improve performance. Some firms even use data and statistics to experiment with ways to improve management decisions, McKinsey reported.

Companies in many industrial sectors rely on data and statistics for other purposes, too. McKinsey reported that some companies rely on data and statistics to enhance their abilities to compete with other firms. For other companies, statistics inform their efforts to develop better products and services. Some firms use data from sensors embedded in their products to offer such services as proactive maintenance, according to McKinsey.

The Importance of Statistics in Commerce

Effective collection and mining of statistical data can yield valuable insight for companies about the likes, dislikes and buying habits of their customers. Online retailer Amazon.com was one of the first to collect and track data on what its customers view and buy as they browse the company's website. From this, Amazon developed algorithms to predict what products customers might be interested in purchasing. Using data from a variety of different sources – like suppliers, social media, other websites and internet searches – companies can accurately segment their customer bases, precisely tailoring their services and products to satisfy these consumers and clients, and thus, make more sales.

Thanks to the internet, the world now produces about 1.7MB of new information per second, according to BigCommerce, with approximately 4.4 to 44 zettabytes (or 44 trillion gigabytes) available for statistical analysis in 2020.

Limitations of Using Statistics

While using statistics to make decisions is helpful, it has limitations. For example, the size of the sample used in market research is a factor. Larger samples would produce a better quality of results, but larger samples cost more money and are sensitive to the law of diminishing returns. This is the classic trade-off between the cost of getting more precise results against budget and time constraints.

Using historical data to construct statistical models for forecasting does not take into consideration any causal changes in the marketplace. Economic environments are constantly changing and so are consumer behaviors and tastes. Managers must have an awareness of these changes and incorporate them into their decisions.

When properly used, statistical methods make the decision-making process much easier. However, the application of statistics is both an art and a science and should not be used as the sole basis for making decisions. When interpreting the results of statistical analysis, exercise judgment based on your own real-life experience and other qualitative factors that are not incorporated into the mathematical model.

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Quick Facts – JEE Advanced Exam 2020

If you have written the JEE Main entrance test in 2016, its time that you know some important facts about the JEE Advanced and stay prepared for the entrance exam. ISS coaching in Lucknow presents in this article few quick facts for IITs or the ISM  JEE (Advanced) candidate:

 1.    2020 Number Of Papers

JEE Advanced Exam Pattern 2021 – Getting familiar with JEE Advanced exam pattern is one of the first things that candidates should do while preparing for the final entrance exam. Normally, the exam pattern is set and published by the exam conducting body in their brochure. IIT Delhi was the authority for JEE Advanced 2020. The good news for candidates is that the overall pattern of JEE Advanced remains more or less the same every year only with minor changes.

However, candidates should clearly understand the pattern of the exam where they will further get to know other details like the types of questions, marking scheme, exam duration and more.

We have provided important information about the JEE Advanced 2021 exam pattern on this page so that candidates can learn everything about the exam and how it is conducted.

Highlights Of JEE Advanced Exam Pattern

Some of the key highlights of the exam pattern are-

The main exam will be conducted     in computer-based mode.

JEE Advanced consists of 2     papers (Paper 1 and Paper 2) and candidates have to appear for both the     papers.

The question paper consists of     three parts: Physics, Chemistry and Mathematics.

There are MCQs and numerical     answer type questions which candidates have to attempt.

An interesting feature of the     JEE Advanced exam is its marking scheme which comprises full, partial and     zero marks scheme.

For effective preparation of examination, one should have a better understanding of how the JEE Advanced exam is conducted and how the question paper is set. Besides, knowing the JEE Advanced paper and exam pattern will allow the candidates to get detailed instruction about various things. 

We will further discuss the JEE Advanced paper pattern below.

JEE Advanced 2021 Exam Pattern

Normally, the questions asked in the JEE Advanced paper are quite tough and tricky. The exam is further designed to judge not just theoretical knowledge but also reasoning ability, comprehension skills and analytical power of the students. Therefore, it is important to get familiar with the exam and the question paper early on.

The exam is held in an online mode (computer-based test) where students need to have a basic knowledge about the working of a computer and the mouse. Candidates have to use the mouse to click on the right option as the answer.

As mentioned above, the exam consists of two papers viz. paper 1 and paper 2. JEE Advanced Paper 1 and Paper 2 further constitute a total of 54 questions each. The paper is also divided into three sections: Physics, Chemistry and Mathematics, each having 18 questions. In total, the exam is of 306 mark where each paper carries a total of 183 marks. The duration of each paper is 3 hours and is conducted in either Hindi or English medium. During the exam day, paper 1 usually begins at 09:00 am and goes on till 12:00 pm. Paper 2  starts from 2:00 pm to 05:00 pm.

  2.    Accepted Languages

With the Centre’s decision to offer JEE Main 2021 in more regional languages apart from the existing Gujrati, Hindi and English languages, aspirants who are proficient in their native tongue may stand a fair chance of gaining admission to the NITs, IIITs and the centrally funded technical institutes in the country. But will their IIT aspirations take a backseat if JEE Advanced is conducted only in English and Hindi?

 Sudhir K Jain, director, IIT Gandhinagar has a more positive take on the issue. “Students appearing for JEE Main and Advanced do a comprehensive preparation keeping in mind the technical stream they have chosen to study. Having an option to take JEE Main in regional languages  would give them some relief from anxiety at stepping stone, and it can build their confidence to progress further towards their next goal,” he tells Education Times.

“ We must prepare our next generation to aim high and for that, they have to come out of their comfort zone, no matter where they study. While initially, having a language of their choice would help, they should learn keeping the long-term goals and larger picture in mind. We have seen so many students from regional language backgrounds do equally well at our institute with the right kind of guidance, hand-holding, hard work and determination,” Jain says.

 3.    No. Of Attempts 

Unlike many other entrance exams, the number of attempts for JEE Advanced exam is limited. You can attempt for the exam only twice and that too consecutively.

  4.    Foreign Nationals Eligibility JEE Advanced 2021 Registration Fee For Foreign Nationals

The application fee details of JEE Advanced 2021 Registration form are given below:

Registration  Fee for Examination Centres in India

 Foreign  Nationals

Candidates from SAARC countries

USD 75

Candidates from Non-SAARC countries

USD 150

 Registration  Fee for Examination Centres in Foreign countries

 Foreign  Nationals

Candidates from SAARC countries

USD 75

Candidates from Non-SAARC countries

USD 150

Mode of Payment: Candidates can pay the JEE Advanced application fee through online mode only via:

o    Debit card

o    Credit card

o    Net banking

JEE Advanced 2021 Eligibility Criteria for Foreign Nationals

The details regarding the JEE Advanced 2020 eligibility criteria are mentioned below:

·         Age limit: Candidates should have born on or after 01 October 1995.

·         Number of Attempts: A candidate can attempt the JEE Advanced exam a maximum of two times in consecutive years.

·         Appearance in Class 12: A candidate should have appeared in Class 12 (or equivalent) examination for the first time in either 2019 or 2020.

Documents Required for Registration of Foreign Nationals

The documents required in JEE Advanced 2021 for foreign nationals while the registration process is given below:

·         Birth certificate

·         Class XII marks sheet/certificate for 75% cut-off (if available at the time of registration)

·         Identity proof

·         Citizenship certificate/Passport

·         Testimonial (if required)

JEE Advanced Foreign Centres

The JEE Advanced 2021 exam centres in foreign countries are mentioned below:

·         Dhaka (Bangladesh)

·         Dubai (UAE)

·         Kathmandu (Nepal)

·         Singapore

 5.    Defense Service (DS) category seats

 Not all defense service personnel s children are eligible for DS category seats. The DS category seats are reserved only for children whose parent was killed or permanently disabled in action during peacetime operations or war while in defense or paramilitary service. 

6.    Performance in Class XII

 Do you know what percentile you need to get admission in IIT or ISM? You would require to be in category wise top 20 percentile in 12th Standard or must have secured at least 75% aggregate marks for General and OBC-NCL or 70% for reserved categories such as SC, ST and PwD candidates.

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List of Top Statistics Colleges In India based on 2021 Ranking

India celebrated the 15th National Statistics Day on the birth anniversary of Prasanta Chandra Mahalanobis. He is best known for introducing ‘The Mahalanobis Distance,’ a statistical measure used in many software programs today, and for founding the Indian Statistical Institute (ISI).

Mahalanobis was instrumental in setting up the National Sample Survey to collect data on socio-economic parameters of the country. The Indian government has extensively used these tools to frame informed policies.

On the occasion of the ‘National Statistics Day,’ ISS coaching in Lucknow have curated a list of top statistics institutes in India. 

Indian Statistical Institute (ISI)

Indian Statistical Institute (ISI), founded in 1931, is headquartered in Kolkata. At present, it has four subsidiary centres in Delhi, Bengaluru, Chennai and Tezpur. 

ISI also has a network of Statistical Quality Control and Operations Research units at Coimbatore, Vadodara, Hyderabad, Giridih, Mumbai, and Pune, guiding industries in India and abroad, to develop quality management systems and solve critical problems related to quality, reliability and productivity. Lately, the government of India has been sponsoring multiple Covid-19 projects proposed by ISI researchers.

IIT Kanpur

The mathematics and statistics department at IIT Kanpur is one of the premier departments in the country, providing excellent teaching and research in mathematical sciences, including statistics. The programmes motivate research in mathematical science and train computational scientists working on challenging problems.

The placement of students in the MSc statistics programmes is almost 100% in the last ten years. 

IIT Bombay, IIT Tirupati, IIT Delhi, IIT Roorkee, IIT (BHU), and IIT Guwahati also offer statistics courses for students. 

St. Stephen’s College

Founded in 1881, St. Stephen’s College is one of the oldest colleges in Delhi. The college offers various mathematics and statistics courses, covering calculus, multivariable calculus, probability and statistics, software (Maxima/Mathematica), biostatistics, etc. In addition, the faculty at St. Stephen have done their research from prestigious institutions including Princeton, Rutgers, Oxford, Cambridge, IITs, IISc and JNU.

St. Xavier’s College Kolkata 

St. Xavier’s College was founded in 1860. The Department of Statistics was started back in the late fifties. The alumni have been placed in top MNCs in insurance, finance, risk management, portfolio analysis and development of statistical software.  St. Xavier’s College’s  Mumbai campus also offers various courses in statistics. 

Lady Shri Ram College for Women

Lady Shri Ram College for Women is one of the premier institutions of higher learning for women in India. The college offers various courses in statistics, marketing, finance, and strategy making. In addition, students are trained on applied statistics, statistical methods and analysis, and work in the areas such as surveys, econometrics, biostatistics, and operations research.

The department of statistics at Lady Shri Ram College for Women teaches students rigorous methods, tools and techniques to sift through a maze of data. In addition, students also conduct practicals based on computer language C and software packages like Excel, Word, and statistical packages for social sciences (SPSS). 

  Madras Christian College (MCC)

The department of statistics at MCC was established in 1968. Located in Chennai, the department offers statistics, computer applications, managerial economics and operational research. It has a well-established ‘Gift Sironomey Statistical Computing Laboratory.’ The department believes in practical training of its students by conducting public opinion, socio-economic and tribal surveys. 

Loyola College

The department of statistics at Loyola College was founded in 1982. With 14 faculty members, the department provides consultancy services to various institutions and companies, alongside teaching students different statistical methods for economics, biostatistics and more. It offers various UG and PG courses in statistics. 

SP College Pune

Savitribai Phule Pune University has one of the leading statistics departments in the country, and it is the only centre for advanced studies in statistics under the UGC and CAS scheme. The department of mathematics and statistics at SP College was established in 1953. However, the department of statistics was hived off in 1976. 

The students get placed in leading global software and pharmaceutical companies, including Pfizer, Novartis, Systat, Ideas etc. Some students have taken up jobs in RBI, Indian Statistical Services and Bureau of Economics and Statistics. 

Hindu College, New Delhi

 Late Shri Krishna Dassji Gurwale founds the College in the historic Kinari Bazar (Chandni Chowk), with prominent Delhi citizens as trustees. A boarding house serves as student residence since 1899. Being a heritage site, the present academic building of the College had only limited possibilities beyond its capacity of 1500 students (after expansion work in 1960). The present strength requires large-scale upgrading of the campus, and for this purpose an Infrastructure Development Vision Plan (Vision 2020) has been formulated. Vision 2020 projects a student strength of over 4,500 after 2016. One of Vision 2020’s chief priorities is provision of rooms for lectures, tutorials and laboratories. It has become imperative to have a larger academic block. This new block would offer the following advantages: 14 ‘smart classrooms’, 12 tutorial rooms, 8 department rooms, 4 lecture rooms, 4 laboratories, a staff room, and an office block. Its estimated cost is approximately Rs. 11.75 crores. 

Ramjas College

Ramjas College is a part of the University of Delhi.  It was established in 1917, by philanthropist and educationist Raj Kedar Nath, and is one of the oldest colleges in India.  It has been recognised as one of the more popular higher educational institutions in India.  The college offers around 20 undergraduate courses and has an excess of 10,000 students.  The college is known for excellence in Humanities, Business and Science studies.  The college has been ranked consistently in the India Today poll for best colleges in India.

Indian Institute of Technology (Banaras Hindu University), Varanasi

The Indian Institute of Technology (BHU), Varanasi, IIT (BHU), formerly known as the Institute of Technology, is a public engineering institution located in Varanasi, Uttar Pradesh. It was established in 1919 and is affiliated to the Indian Institute of Technology (IIT). There are around 1,057 seats in this college. It offers around 22 undergraduate courses in engineering and technology, healthcare and sciences. Each year, admission to this course is through the JEE entrance exam which is conducted in the month of April. The results for the same are declared in May. The students who clear the JEE main exam must then appear for the JEE advanced which is conducted in the month of June. Results are declared in July and classes commence from August onwards.

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Current Issues Involving Statistical Services In India

A series of economic shocks — including the demonetisation of high denomination currency notes in 2016, the introduction of Goods and Services Tax in 2017 and the COVID-19 lockdown — have exposed the growing incapacity of India’s official statistical system to meet the requirements of policymaking. Acknowledging that the credibility of India’s statistical system has come under intense scrutiny in recent years because of apprehensions raised over the procedural lapses in the release of the gross domestic product (GDP) data, methodology for computing national income series with a new base year, delay in the release of data like Periodic Labour Force Survey for 2017-18, withholding of the Annual Consumption Expenditure Survey for 2017-18 and the Mudra survey in recent times, ISS coaching in Lucknow suggests that the government should  begun discussions on a much-delayed revamp of official statistics.

Some of the major challenges facing the official statistical system are as follows. First, key government databases on population, household consumption, employment and national income have been in disarray for more than a decade due to delayed release and politicisation.

Second, the official statistical system — originally designed for a planned economy — has not adapted to the changing realities of post-liberalisation and the digital era. The inability of official statistics to adequately capture changing patterns of migration, employment and consumption is a case in point.

Third, the official statistical system has to compete with private survey agencies such as the Centre for Monitoring Indian Economy, NGOs such as Pratham and philanthropic organisations such as Tata Trusts, all of which have acquired the capacity to conduct their own large-scale surveys. Big data sources held by private entities are further undermining the government’s monopoly over data generation by making high volumes of data available at higher frequency.

Notwithstanding the emergence of non-governmental sources of data and newer types of data, conventional government statistics such as the population census will continue to occupy a key position in policymaking and public life for the foreseeable future. India’s Census Act was enacted in 1948, two years before the constitution was adopted. Several constitutional provisions link the census to power and resource sharing at different levels of government, as well as to various affirmative action policies.

Recent controversies have shown that the use of census data in policymaking remains politically sensitive and the quality of data has a significant bearing on decision-making. The choice of reference year for the delimitation of constituencies in the union territory of Jammu and Kashmir and a few north-eastern states has proven contentious. This is because of an uneven change in the relative population shares of various ethnic communities that is partly explained by poor quality census data.

The mandate of the Fifteenth Finance Commission — set up by the union government to give recommendations for devolution of taxes among other matters — to change the reference year and weight of population figures used in federal redistribution has also been questioned. It is likely to reduce the shares of more urbanised and economically vibrant southern states due to their lower fertility rates and poor accounting of the migrant population.

The most recent protests surrounding the hugely controversial Citizenship Amendment Act and the decision to link the National Register of Citizens with the decennial census have demonstrated that large sections of society continue to see a fair and conventional census as a guarantor of their constitutional rights.

Concerns raised about Indian statistical system in recent times includes

• Institutional and structural issues:

-          The restructuring order is silent on both the CSI and NSC.

-          NSSO has become the part of the general bureaucracy and ceases to exist as an autonomous body.

-          Timely releases: there are no specific timelines for release of labour force statistics and consumption expenditure surveys.

-          Lack of skilled manpower and resources to improve capabilities, review of data collection, collation and aggregation to ensure quality, timeliness and credibility of the collected statistical output.

 Despite its continued relevance, the official statistical system requires substantive reforms. Recent changes to the system mostly involve mechanical solutions to fix data quality through the introduction of newer data collection tools such as Computer Assisted Personal Interviewing. But technological and legalistic solutions will only go so far in solving existing problems.

India’s ‘data deficit’ is embedded in a mutually constitutive relationship with its fraying democracy and deficient development in several respects. First, growing political interference has been a major problem facing official statistics on religion, caste, employment, consumption and national income.

Second, most government bodies entrusted with collecting data are struggling with a shortage of trained manpower to handle the volume of conventional data.

Third, biometric databases built by the government are deficient in underdeveloped areas where conventional databases have also been incomplete or flawed.

Fourth, adding punitive provisions to the laws governing the collection of data has in the past been followed by some of the most egregious instances of politically-motivated data manipulation.

A multi-pronged strategy is needed to holistically address India’s ‘data deficit’.

The government should start by helping to strengthen the independence of the judiciary, press freedom, the Right to Information Act and the autonomy of government statistical agencies. All of these are essential to ensuring the timely release and critical examination of data.

Statistical agencies should sensitise non-governmental stakeholders to secure their cooperation in the field to reduce non-response rate. This would also serve as a check on interference driven by political and economic considerations.

Statistical agencies should also trim unwieldy questionnaires to better utilise scarce resources and improve data quality. Nearly half of the questions in the census’ ‘House listing and Housing Schedule’ and a quarter in its ‘Household Schedule’ seek information on household amenities and assets, and employment, respectively. These questions can be dropped without impairing the constitutional and policy obligations of the census.

Existing sources of data should be used intensively. The continued neglect of state counterparts of the national sample surveys is a case in point. Governments at different levels should better utilise administrative statistics instead of adding to the growing pool of sample surveys of questionable quality. Moreover, digitisation of administrative statistics will improve access to data and allow for greater scrutiny and use of that information.

Lastly, the government should shed its self-image as the only source of large-scale data. It must engage non-government players as sources of ideas and solutions, particularly in the case of big data, because its manpower and technological constraints cannot be overcome overnight.

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HOW IS STATISTICS USED IN EDUCATION?

The concept of statistics does not exist in oblivion as it has its origin traceable to the existence of man and education. Every discipline has some unique and special importance in any field of learning. The Importance of Statistics in Education is referred to as ‘the essence and significance of statistics to every field of learning’ concerning mathematics, education, business, science, computer, history, etc., most especially the importance and role of statistics in educational research.

Statistics is a concept that is used most often in every field such as Economics, Marketing, and Programming. It works hand-in-hand with Mathematics. Hence, running away from Statistics is like running away from mathematics, you will be tired at a moment. ISS coaching in Lucknow brilliantly discusses in this article the role, importance, relevance, and significance of Statistics in Education, and other fields of learning in extension.

 Definition of Statistics

Statistics is ‘the science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data.’

Statistics is a form of mathematical analysis that uses quantified models, representations, and synopses for a given set of experimental data or real-life studies. Statistics practically studies methodologies to gather, review, analyze and draw conclusions from data. 

In developing methods and studying the theory that underlies the methods, statisticians draw on a variety of mathematical and computational tools to get a more accurate result. These various definitions of statistics would be evident in the discourse of the importance of statistics in education below.

 Ten Importance of Statistics in Education

1. Statistics helps in the collection and presentation of data in a calculated and systematic manner

Statistics in education helps in the collection and presentation of data in a well-arranged manner. Simply put, Statistics in educations helps in the orderly arrangement of both processed and unprocessed data.

Data are sets of facts that provide a partial picture of reality, with certain purposes. And, no matter the method of its collection, questions regarding the nature of the information that the data are conveying, how the data can be used, and what must be done to include more useful information must constantly be put into consideration.

Since most data are available to researchers in a raw format, they must be summarized, organized, and analyzed to usefully derive information from them. This entails that unorganized or unanalyzed data are prone to unreliable and false output or results.

Furthermore, each data set needs to be presented in a certain way depending on what it is used for. Planning how the data will be presented is essential before appropriately processing raw data. All these can be complex, but statistics helps to analyze, digest, and present them in a simple empirical manner.

2. Statistics makes the teaching and learning process more efficient

Statistics in Education make the teaching and learning process more efficient in practice. Statistics in Education, with special considerations to measurement and evaluation of concepts, are essential parts of the teaching and learning process.

In this process, scores are being obtained and interpreted to make decisions. Statistics, therefore, enables one to study these scores objectively. It makes the teaching-learning process more efficient.

3. Statistics helps in the provision and presentation of the exact type of description

Statistics in Education helps in the provision of the exact type of description. It practically helps teachers to give an accurate description of data. This could be found in the cases of the administration of a pupil or observation of a child. For any description of an idea to be veritable and convincing, the introduction of statistics could serve as an auxiliary in the process of analysis and presentation.

Statistics gives a piece of clear information about anything that can be described. Hence, whenever Statistical approaches are employed, they automatically make the outcome or result to be simpler and easier to be understood, even by a layman. 

Every data in statistics is described with property and values. The properties and values are assigned to a particular description that will enable a layperson to assimilate without much ado.

For example, the Google Search Console and Google Analytics are great tools that use Statistics to show a user the happenings around his or her website. The statistics could show the user how much traffic comes to the site, location of visitors, time, and a lot of other presentations of data and descriptions. But, all this complex information can easily be observed by a layman because of the statistical approach in the manner of description and presentation.

Google Search Console is a great type of tool that majorly uses statistics for its presentation and analysis of complex data.

4. Statistics serves as a reliable source of history in education

Statistics is one of the most reliable methods of verifying any given history. This is because statistical documentation is always empirical and easy for understanding. Thus, statistical approaches employed in the description and analysis of data, concepts, etc., a ton of years ago, can be resurrected and positioned as an empirical source for a piece of reliable information about any history in the contemporary time.

5. Statistics helps in the Summary of Results

Statistics in education is important because it enables the calculation and adequate summary of the results in a meaningful and convenient form; statistics gives an order to the data.

Again, statistics helps one to make data precise, concise, and meaningful and to express it in a way that the persons involved will understand easily.

Statistics presents clear and less ambiguous data. When the Statistical approach is introduced in the records or summary of results, it makes it easier and more empirical to be observed.

6. Statistics helps in the process of achieving an accurate prediction

Statistics show its importance in calculation and thoughts.

Sometimes, because of a lack of technical knowledge, a teacher becomes vague in describing the pupils’ performance. But statistics aids him or her to describe the performance by using proper language and symbols. With the help of these statistical approaches, a definite and exact interpretation emerges.

Again, statistics helps to guide one in any thinking activities. When one thinks systematically through a calculated analysis and statistics, he or she thinks rightly and is likely to arrive at positive results quickly.

This could also be observed when a teacher predicts the future performance of the pupils: statistics enables the teacher to predict how much of a thing will happen under conditions we know and have measured.

For example, the teacher can predict the probable score of a student in the final examination from his entrance test score. But the prediction may be incorrect due to different factors. Statistical methods tell about how much margin of error to allow in making predictions.

7. Statistics helps in the analysis of some causal factors

Statistics enables teachers to analyze some of the causal factors underlying complex and otherwise bewildering (confusing) events: it is a common factor that the behavioral outcome is a result of numerous causal factors.

The reasons why a particular student performs poorly in a particular subject could be numerous and vary from a student to another. So with the appropriate statistical methods, one can keep these extraneous variables constant and can observe the cause of failure of the pupil in a particular subject.

8. Statistics helps in the hospital analysis

Statistical analysis is very necessary for the Hospital for the best test results. Imagine where a doctor predicts a disease based on statistical analysis, he is likely to present the best result out of it.

Suppose a patient has malaria according to the survey when statistical analysis is introduced, it will go a long way to present the accurate level of malaria in his or her body. Again, the already recorded statistics from history could give a hint on other diseases that are likely to arise.

A detailed statistical analysis will also dictate the effects malaria will cause and possible causes of the disease.

9. Statistics assists significantly in the collection of data and information

Statistics aids in the prediction of future events. In a class of 89 students, their performance in continuous assessments and tests could determine who will take the First position or who will come out with the overall best result. It could even predict the most intelligent student among the 89 students in the class. This is what Statistics can do.

On the other hand, before a general election in a country, the statistical analysis of the political campaigns and awareness could make one predict correctly who will win the election. Here Statistics plays a very vital role.

10. Statistics makes studies to be highly responsive and empirical

The role of statistics in the collection of data and information is a very sensitive one. With good statistical data, one can access a variety of information. With this, one can easily handle and manipulate the data accordingly. Think of Economics as a field of study, statistics is a major activity that handles economical researches, both in practical and in theory.

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What Are The Important Topics For IIT JEE In 11th And 12th Standard?

With the passion to pursue engineering from a premier institute like IIT, a lot of students aim at cracking the IIT JEE to live this dream right from their 11th standard. Every year, about 1.2 million candidates appear for the JEE Main exam, out of which, a fraction of students qualify to appear for the JEE Advanced exam.  Considered as one of the most difficult entrance exams across the globe, not many know how to prepare for JEE and lack correct guidance. ISS coaching in Lucknow will discuss in this article, how one needs to divide and dedicate time for important topics in 11th standard to score maximum marks in IIT JEE.

IIT JEE consists of thirty multiple choice questions from Maths, Physics and Chemistry. With equal weightage given to all three subjects, one should know how to prepare for IIT and what topics to emphasize on to be able to answer most questions correctly in the assigned time.

To help students understand and prepare better, we have curated a list of important topics for JEE Mains from each subject for them to prioritize topics based on it.. Have a look.

Mathematics

Generally, the weightage of topics of class 11 mathematics is 40% to 50% in JEE.

·         Probability: Being one of the most important topics, one needs to cover Conditional Probability, Law of Total Probability and Bayes theorem in detail to score maximum marks in this subject.

·         Coordinate Geometry: One needs to be thorough with Circle

·         Logarithm: Basic Logarithm questions are only asked

·         Permutation and Combination: Important topics to cover here are circular permutation, Integral solution of linear equation and Division/ Arrangement of Groups

·         Quadratic Equation: One needs to focus on roots of an equations coefficients and most importantly on roots of an equation.

·         Complex Number

·         Conic Section

·         Circle

·         Calculus

·         Vector & 3 D

·         Trigonometric Equation

·         Properties of Triangles

·         Quadratic Equation

·         Sequence and Series

 Physics

In Physics, the weightage of the syllabus of class 11 and 12 is almost equal -- it is around 50% for each. Some important chapters of Physics in class 11 include Waves, Simple Harmonic Motion, Units and Dimensions, Rotational Motion, and Newton’s Laws of Motion. The class 12 Physics topics that carry heavy weightage in JEE include Electrostatics, Magnetism, Current Electricity, Optics, Modern Physics.

·         Units & Dimension: All concepts under this topic needs to be covered.

·         Rotational Motion: One should draw their focus on the concept of rigid body dynamics.

·         Kinematics of SHM: Questions asked on this are to test the understanding of Simple Harmonic Motion and one can expect around 3 questions from this concept.

·         Newton s Law of Motion: Application of the three laws of motion needs to be understood and effectively used in solving the questions.

Chemistry

In JEE chemistry, generally, the weightage of the class 12 syllabus is more than that of class 11. Generally, the weightage of the class 11 chemistry syllabus in JEE is around 30% to 40%. Many topics that are taught in class 11 are the basic ones and serve as a foundation for a deeper understanding of many class 12 topics. So, even though the weightage of the class 11 syllabus is slightly on the lower side, the topics taught in class 11 should not be ignored or taken for granted.

·         Chemical Equilibrium: One needs to focus on concepts like Law of Mass Action, Acids and Bases and Solubility product.

·         Atomic Structure: Atomic structure preparation and atomic mass concepts are important . Theories like the Thomson, Bohr and Rutherford should be concentrated on.

·         Stoichiometry: Focusing on topic like the Mole and equivalent concept is very important in class 11th.

·         Gaseous State: States of matter, compressibility factor and van der Waals equation are concepts to be focused on.

·         Chemical Bonding: One should focus on periodicity concept.

·         Organic Chemistry: Basic concepts of Organic Chemistry from 11th standard is usually asked. Hence one shouldn’t t ignore it and emphasize on the basics.

·         Electrochemistry

·         Coordination compound

·         Salt analysis

·         Ionic equilibrium

·         Thermodynamics & thermochemistry

·         Aldehydes and ketones

·         Aromatic hydrocarbons

·         GOC isomerism

·         Liquid solutions

·         Alkyl halides and aryl halides

JEE Main Important Topics 2021- Best Books to Cover

Students should cover all the JEE Mains 2021 important topics and chapters from the below best-recommended books by various subject experts, previous year JEE Main toppers and many test takers. Students are advised to follow only one or two books for each subject and do not refer to so many books. Beside all the recommendations, NCERT books is highly recommended for JEE Main preparation.

JEE Main Best Books 2021

I.        Subject : Mathematics

Recommended Books:

1)     NCERT Class 11 and 12 Textbooks

2)     Differential and Integral Calculus by Amit M Aggarwal

3)     Trigonometry and Coordinate Geometry by SL Loney

4)     Complete Mathematics for JEE Main by TMH Publication

5)     Algebra by Dr.SK Goyal

 II.     Subject : Physics

Recommended Books:

1)      NCERT Class 11 and 12 Textbooks

2)      Concepts of Physics by HC Verma (Volume 1 and 2)

3)      Fundamentals of Physics by Halliday, Resnick & walker

4)      Problems in General Physics by I.E Irodov

 III.   Subject : Chemistry

Recommended Books:

1)       NCERT Class 11 and 12 Textbooks

2)       Organic Chemistry by OP Tandon

3)       Physical Chemistry by P Bahadur

4)       Inorganic Chemistry by JD Lee

5)       Modern Approach to Chemical Calculations by RC Mukherjee

 JEE is a very tough competition, where the difference of even 1 mark can cause a lot of damage to one’s rank. Hence, it is important to be equally proficient in all the topics, both from class 11 and class 12. Even though it may seem like that a majority of JEE questions are from the class 11 syllabus, however, you must notice that JEE questions generally involve a mix of several concepts, and hence, you should be comfortable with all the concepts involved to apply them in a single question. Generally, in class 11, the foundations of several advanced concepts are laid. Hence, you should pay equal attention to the syllabus of class 11 and 12, both.

We advice students to be through with all three subjects mentioned above and give additional attention to these important topics of class 11 for IIT JEE mains to score maximum marks. Good Luck!

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What Do Statisticians Do? Roles, Responsibilities, and Career Paths

Statisticians have long played a role in research and academia. Recently, however, there has been a spike in demand for statisticians in business, due to the proliferation of data generation and collection across industries and because businesses are now realizing the value of data-driven decision making.

With this increased demand in mind, it’s understandable that more and more professionals are considering careers as statisticians.

Unfortunately, the term “statistician” is rather vague, and many people are unsure what, exactly, these professionals actually do. Here, ISS coaching in Lucknow explore the responsibilities of a statistician, the education and skills typically required to excel in the role, and offer some alternative career paths for those who want to work with data.

What is a statistician?

At a high level, statisticians are professionals who apply statistical methods and models to real-world problems. They gather, analyze, and interpret data to aid in many business decision-making processes. Statisticians are valuable employees in a range of industries, and often seek roles in areas such as business, health and medicine, government, physical sciences, and environmental sciences.

According to the Bureau of Labor Statistics, which groups statisticians with mathematicians, the job outlook for the industry is positive. Overall employment for mathematicians and statisticians is expected to grow 30 percent from 2018 to 2028—nearly five times as fast as growth for all occupations.

Much of this projected growth will result from businesses collecting an increasing amount of data from an ever-widening number of sources. In order to analyze and interpret this data, businesses and organizations will need to hire more people specifically trained in such analysis.

The BLS also reports that the median annual wage for statisticians was $91,160 per year as of 2019.

 Roles and Responsibilities of a Statistician

The specific tasks that statisticians are expected to complete on a daily basis will naturally vary and depend on the specific industry and organization in which they work.

Generally speaking, in the private sector, statisticians often work to interpret data in a way that can inform organizational and business strategies; for example, by understanding changes in consumer behavior and buying trends. In the public sector, on the other hand, analyses will often be focused on furthering the public good; for example, by collecting and analyzing environmental, demographic, or health data.

Regardless of whether a statistician works in the public or private sector, their daily tasks are likely to include:

·         Collecting, analyzing, and interpreting data

·         Identifying trends and relationships in data

·         Designing processes for data collection

·         Communicating findings to stakeholders

·         Advising organizational and business strategy

·         Assisting in decision making

Required Skills for Statisticians

In order to be successful, statisticians typically have a unique combination of technical, analytical, and leadership skills. These include:

·         Analytical skills: First and foremost, statisticians must be experts in statistical analysis. They must have a keen eye for detecting patterns and anomalies in data.

·         Technical skills: To effectively collect and manipulate the data that informs their actions, statisticians must leverage computer systems, algorithms, and other technologies, meaning technical proficiency is critical.

·         Communication skills: Although statisticians are experts in mathematics and statistics, they must also exhibit strong communication skills to effectively communicate the findings of their analysis with others in their organization. This includes both verbal and written communication, as well as the ability to present data in easy-to-understand, visual ways.

·         Leadership skills: Truly effective statisticians must be able to think critically about the data that they are analyzing through the lens of key stakeholders and executives. Learning to think like a leader can help statisticians identify trends and data points that can make a big difference in their organizations.

Education for Statisticians

Many entry-level statistician roles require candidates to hold a master’s degree, usually in statistics or mathematics.

However, those who demonstrate proficiency in both statistical analysis as well as another subject area—for example, economics and econometrics, computer and material science, or biology—can have a distinct competitive advantage when seeking employment in a specialized industry.

Students are encouraged to take classes in computer sciences as well, which has important applications on the job. Those specifically considering a career in research or academia are typically required to earn a PhD.

Alternative Career Path: Professions Similar to Statisticians

For individuals who enjoy working with data or who exhibit all of these skills but don’t want to become a statistician, there are similar career paths that will still allow you to put your skills and passions to use. Two possible alternative career paths include becoming a data analyst or becoming a data scientist.

Statistics vs. Data Analytics

Similar to statisticians, data analysts identify and communicate data-driven insights that allow business stakeholders to make better-informed decisions. Nearly all industries have a need for skilled data analysts, at least to some degree. Industries with the greatest demand tend to be sales, marketing, healthcare, and various sciences.

Data analysts typically work with teams to complete projects or address problems as needed. Since most work is done on a computer, there are possibilities to work remotely in this field.

Although at first glance it may seem statistics and data analytics are one and the same, there are some major differences that set these careers apart.

According to Thomas Goulding, professor for Northeastern University’s Master’s in Data Analytics program, statisticians are more immersed in the mathematics and computational aspects of data. Data analysts should also have a strong feel for statistics, but their real skills focus on being able to use tools to extract information from the data. He points out that analysts are charged with cleaning, formatting, and integrating data so that it can be input into software to be analyzed.

In contrast, statisticians must be devoted to the computational nature of their work and be highly confident in their ability to solve complex mathematical equations.

Statistics vs. Data Science

Another possible alternative to a career in statistics could be to pursue a role in data science. While there is a fair amount of overlap between the fields of statistics and data science, there are several important distinctions. For example, whereas statisticians use mathematical analysis to solve real-world problems, data scientists take a multidisciplinary approach which is more focused on computing techniques in order to extract insights from data.

It’s also important to understand the difference between data science and data analytics. Unlike data analysts who interpret and draw conclusions from data sets, data scientists design processes for modeling data. A large difference between the two lies in the data scientist’s need for advanced coding skills.

Data science is a rapidly growing field that has caught the attention of those looking to break into a career that combines mathematical and statistical analysis, coding skills, and substantive expertise. Since this role is considered more senior than data analysts, it is common for employers to seek candidates with a graduate degree in data science or a related field. 

 Choosing Your Right Fit

If you’re considering earning an advanced degree to further your career, it’s important that you choose the degree that will best prepare you for the career that you want to pursue. Consider your personal and professional goals to determine which path you want to follow.

Many renowned institute prepares students for a successful career in this field by offering courses in statistics, mathematics, analytics systems technology, business intelligence, advanced analytics, business process and management, business analytics agility, communicating with data, and more. Upon graduating, students possess a portfolio of professional samples that demonstrate their range and depth of skills through their participation in institute’s renowned experiential network.

The data analytics program is suitable for people who are analytical thinkers and problem solvers and can be valuable to those with virtually any background.

On the other hand, if you aspire to become a data scientist, consider the benefits that earning an MS in Data Science can have on your career. Not only do employers value advanced degrees when recruiting for these positions, but choosing a program that is rooted in experiential learning can provide you with the hands-on learning and skills you will need to excel in the workplace.   

No matter which path you choose, be sure to evaluate both your personal and professional goals to help you decide which career path is right for you.