#variable data

[wetslug]

ppl who r using poll results as a way to Prove Something about society or come to any conclusion.. i hope you are aware that tumblr users are one of the most biased population groups you could conceivably find. gob bless

[imrovementcompany]

Statistical process control (SPC). Process control and process capability

Process capability Process Capability (Cp) is a statistical analysis of a process’s capacity to manufacture parts reliably within predetermined parameters. Depending on the stage of the process and the method used to calculate the standard deviation or sigma value, we can calculate Cp (Process Capability), Cpk (Process Capability Index), or Pp (Preliminary Process Capability), and Ppk…

[loveletterworm]

Unimportant but slightly funny thing i just found and had never seen mentioned before: The variable used for displaying what chapter you started your deltarune save in on the light world’s stats screen (oddly not the dark world’s...?) is called “krispreservationsociety” (society for the preservation of krises)

i actually somehow never noticed this was a thing that displayed at all this whole time i suppose i just never looked at this screen in chapter 2...

[merakiui]

would u still happen to have ur twst dick size tier list (thats so awkward to actually type omg) 😭😭 I've been thinking about it and I know you've done it before but it was a few months back iirc </3

also ... on the tier list note, have u done a "chest or ass (or a third thing)" chart on the twst guys yet 👀

Here it is!!! :D and a chest or ass list!!! Omg that's very fun. I should preface this by saying that, while it isn't noted on the list, Jade also likes a secret third thing (footjobs), but you did not hear that from me........ 🤫

In any case, here is my very humble opinion:

[adtothebone]

Scatter Plot Cats (ai) These really shouldn’t have come out nearly as good as they did. ( 1-4 of 8 )

[cicadas]

Yall ever think about how the percentage sign % is just the divided sign ÷ turned on the side kinda? BecAUSE I DO !!! But I took statistics too many times so I'm like probably completely insane at this point. Understanding averages and percentages and probability and shit does something to a mf

[nintendont2502]

python save me. save me python

[snarp]

COVID's long-term effect upon my respiratory system means that I'm probably going to have to acquire and learn to use a bong. I wonder if this is a studiable phenomenon.

[comfortlesshurt]

heehee hoohoo i have plans now that i've got a solid control set of data

[torchickentacos]

I think one of the worst feelings is when you have a special interest in something you're decidedly not intelligent enough to fully understand, so you just sort of emptily rotate it in your head for a bit without actually GOING anywhere with it

[zvaigzdelasas]

.

[wetslug]

OK for those following my mood tracking trial (to see if i actually still benefit from ssris or not), ive just finished weaning off meds -> being 6 weeks completely unmediated so heres my data:

average mood: 5.8/10 (based on multiple entries throughout day) overall daily average mood: 2.7/4 (based on my assessment at end of time so more indicative of how the whole day was) sleep quality: 3.5/5 time asleep: 8.5 hours average energy: 2.4/5

SYMPTOM SPECIFIC overall frustration: 1.7/4 days my frustration was high (3 or 4): 6/30 = 20% overall anxiety: 1.1/4 days my anxiety was high (3 or 4): 3/31 = 10% overall irritability: 1.5/4 days my irritability was high (3 or 4): 7/31 = 23% motivation for extra (ie chores, outings, hangouts) tasks: 1.5/4 moviation for daily (unavoidable stuff like work, showering) tasks: 1.8/4

[juliebowie]

Understanding Different Types of Variables in Statistical Analysis

Summary: This blog delves into the types of variables in statistical analysis, including quantitative (continuous and discrete) and qualitative (nominal and ordinal). Understanding these variables is critical for practical data interpretation and statistical analysis.

Introduction

Statistical analysis is crucial in research and data interpretation, providing insights that guide decision-making and uncover trends. By analysing data systematically, researchers can draw meaningful conclusions and validate hypotheses. 

Understanding the types of variables in statistical analysis is essential for accurate data interpretation. Variables representing different data aspects play a crucial role in shaping statistical results. 

This blog aims to explore the various types of variables in statistical analysis, explaining their definitions and applications to enhance your grasp of how they influence data analysis and research outcomes.

What is Statistical Analysis?

Statistical analysis involves applying mathematical techniques to understand, interpret, and summarise data. It transforms raw data into meaningful insights by identifying patterns, trends, and relationships. The primary purpose is to make informed decisions based on data, whether for academic research, business strategy, or policy-making.

How Statistical Analysis Helps in Drawing Conclusions

Statistical analysis aids in concluding by providing a structured approach to data examination. It involves summarising data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation). By using these summaries, analysts can detect trends and anomalies. 

More advanced techniques, such as hypothesis testing and regression analysis, help make predictions and determine the relationships between variables. These insights allow decision-makers to base their actions on empirical evidence rather than intuition.

Types of Statistical Analyses

Analysts can effectively interpret data, support their findings with evidence, and make well-informed decisions by employing both descriptive and inferential statistics.

Descriptive Statistics: This type focuses on summarising and describing the features of a dataset. Techniques include calculating averages and percentages and crating visual representations like charts and graphs. Descriptive statistics provide a snapshot of the data, making it easier to understand and communicate.

Inferential Statistics: Inferential analysis goes beyond summarisation to make predictions or generalisations about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis. This type of analysis helps conclude a broader context from the data collected from a smaller subset.

What are Variables in Statistical Analysis?

In statistical analysis, a variable represents a characteristic or attribute that can take on different values. Variables are the foundation for collecting and analysing data, allowing researchers to quantify and examine various study aspects. They are essential components in research, as they help identify patterns, relationships, and trends within the data.

How Variables Represent Data

Variables act as placeholders for data points and can be used to measure different aspects of a study. For instance, variables might include test scores, study hours, and socioeconomic status in a survey of student performance. 

Researchers can systematically analyse how different factors influence outcomes by assigning numerical or categorical values to these variables. This process involves collecting data, organising it, and then applying statistical techniques to draw meaningful conclusions.

Importance of Understanding Variables

Understanding variables is crucial for accurate data analysis and interpretation. Continuous, discrete, nominal, and ordinal variables affect how data is analysed and interpreted. For example, continuous variables like height or weight can be measured precisely. In contrast, nominal variables like gender or ethnicity categorise data without implying order. 

Researchers can apply appropriate statistical methods and avoid misleading results by correctly identifying and using variables. Accurate analysis hinges on a clear grasp of variable types and their roles in the research process, interpreting data more reliable and actionable.

Types of Variables in Statistical Analysis

Understanding the different types of variables in statistical analysis is crucial for practical data interpretation and decision-making. Variables are characteristics or attributes that researchers measure and analyse to uncover patterns, relationships, and insights. These variables can be broadly categorised into quantitative and qualitative types, each with distinct characteristics and significance.

Quantitative Variables

Quantitative variables represent measurable quantities and can be expressed numerically. They allow researchers to perform mathematical operations and statistical analyses to derive insights.

Continuous Variables

Continuous variables can take on infinite values within a given range. These variables can be measured precisely, and their values are not limited to specific discrete points.

Examples of continuous variables include height, weight, temperature, and time. For instance, a person's height can be measured with varying degrees of precision, from centimetres to millimetres, and it can fall anywhere within a specific range.

Continuous variables are crucial for analyses that require detailed and precise measurement. They enable researchers to conduct a wide range of statistical tests, such as calculating averages and standard deviations and performing regression analyses. The granularity of continuous variables allows for nuanced insights and more accurate predictions.

Discrete Variables

Discrete variables can only take on separate values. Unlike continuous variables, discrete variables cannot be subdivided into finer increments and are often counted rather than measured.

Examples of discrete variables include the number of students in a class, the number of cars in a parking lot, and the number of errors in a software application. For instance, you can count 15 students in a class, but you cannot have 15.5 students.

Discrete variables are essential when counting or categorising is required. They are often used in frequency distributions and categorical data analysis. Statistical methods for discrete variables include chi-square tests and Poisson regression, which are valuable for analysing count-based data and understanding categorical outcomes.

Qualitative Variables

Qualitative or categorical variables describe characteristics or attributes that cannot be measured numerically but can be classified into categories.

Nominal Variables

Nominal variables categorise data without inherent order or ranking. These variables represent different categories or groups that are mutually exclusive and do not have a natural sequence.

Examples of nominal variables include gender, ethnicity, and blood type. For instance, gender can be classified as male, female, and non-binary. However, there is no inherent ranking between these categories.

Nominal variables classify data into distinct groups and are crucial for categorical data analysis. Statistical techniques like frequency tables, bar charts, and chi-square tests are commonly employed to analyse nominal variables. Understanding nominal variables helps researchers identify patterns and trends across different categories.

Ordinal Variables

Ordinal variables represent categories with a meaningful order or ranking, but the differences between the categories are not necessarily uniform or quantifiable. These variables provide information about the relative position of categories.

Examples of ordinal variables include education level (e.g., high school, bachelor's degree, master's degree) and customer satisfaction ratings (e.g., poor, fair, good, excellent). The categories have a specific order in these cases, but the exact distance between the ranks is not defined.

Ordinal variables are essential for analysing data where the order of categories matters, but the precise differences between categories are unknown. Researchers use ordinal scales to measure attitudes, preferences, and rankings. Statistical techniques such as median, percentiles, and ordinal logistic regression are employed to analyse ordinal data and understand the relative positioning of categories.

Comparison Between Quantitative and Qualitative Variables

Quantitative and qualitative variables serve different purposes and are analysed using distinct methods. Understanding their differences is essential for choosing the appropriate statistical techniques and drawing accurate conclusions.

Measurement: Quantitative variables are measured numerically and can be subjected to arithmetic operations, whereas qualitative variables are classified without numerical measurement.

Analysis Techniques: Quantitative variables are analysed using statistical methods like mean, standard deviation, and regression analysis, while qualitative variables are analysed using frequency distributions, chi-square tests, and non-parametric techniques.

Data Representation: Continuous and discrete variables are often represented using histograms, scatter plots, and box plots. Nominal and ordinal variables are defined using bar charts, pie charts, and frequency tables.

Frequently Asked Questions

What are the main types of variables in statistical analysis?

The main variables in statistical analysis are quantitative (continuous and discrete) and qualitative (nominal and ordinal). Quantitative variables involve measurable data, while qualitative variables categorise data without numerical measurement.

How do continuous and discrete variables differ? 

Continuous variables can take infinite values within a range and are measured precisely, such as height or temperature. Discrete variables, like the number of students, can only take specific, countable values and are not subdivisible.

What are nominal and ordinal variables in statistical analysis? 

Nominal variables categorise data into distinct groups without any inherent order, like gender or blood type. Ordinal variables involve categories with a meaningful order but unequal intervals, such as education levels or satisfaction ratings.

Conclusion

Understanding the types of variables in statistical analysis is crucial for accurate data interpretation. By distinguishing between quantitative variables (continuous and discrete) and qualitative variables (nominal and ordinal), researchers can select appropriate statistical methods and draw valid conclusions. This clarity enhances the quality and reliability of data-driven insights.

[unnonexistence]

ok we are doing Pacific Rim Data Visualization live on my blog tonight because i can't help myself

I'm going to put this post in the main pacrim tag in case anyone is interested, but i don't want to annoy anyone, so everything else is just going to be in my #unscientific aside tag. okay? okay.

basically i am making graphs of stuff hermann would have been making graphs of (& predictive models about).

so i'm thinking one nice way to show the increasing frequency of kaiju attacks over the years would be a bar graph along these lines:

2013-2025 is a pretty good time span in terms of number of bars, so it'll look nice, and it'll clearly show the increase over time. i might leave 2025 out because the movie takes place in january 2025 so it'll look funny to leave it in (only 1 attack pre-movie, i think).

now this would be a very broad-strokes graph. this is the kind of graph that just kind of says "hey there have been more and more giant monsters emerging from the ocean lately. that's probably bad". i want to do something with hermann's predictive models, & this isn't going to work for that.

i could do a more specific one, e.g. by month, but even for 2024 when there are a stupid number of attacks in the timeline, that's not ideal. some months are only going to have one or two & it'll look funny. also, his model gets down to "number of minutes between attacks", which is way too specific to use a bar graph.

for the predictive model, when i think about "increasing frequency of kaiju attacks" & hermann's speech about it*, what I picture is a line something like this:

ignore the scale, i just put whatever. the point is Number Go Up.

the problem though is that means the y axis needs to be number of kaiju attacks, and the x axis needs to be time. which like... idk how to make that work? this is probably just because idk what i'm doing, but like... the attacks are happening at distinct points in time. how are we defining "number of attacks"? it would have to be number of attacks over some timespan, right? i guess it could be Total Number Ever but that seems weird.

this maybe should be a dot plot of some sort, but the thing with the axes is throwing me off. I'm going to work on some of the actual data stuff and report back, but at some point i probably need to do some research about the types of graph i can make & what they're used for.

if anyone has ideas or otherwise wants to talk about this PLEASE let me know. nerd bullshit is more fun with other people.

*you know, the one that got meme'd? "In four days, we could be seeing a kaiju every eight hours until they are coming every four minutes."

[adtothebone]

Scatter Plot Cats (ai) These really shouldn’t have come out nearly as good as they did. ( 5-8 of 8 )

[asexualactivities]

This is all going to turn into an electrical engineering project at some point, isn't it?