#posthoc

[thefourthrabbit]

The closet.

This room is actually refurbished from a bedroom, which explains why there is a closet within a closet.

[eonars]

poetic imagery tonight of me at my desk with my head in my hands pressing on my eyes while yandel and shaggy blasts through my headphones and my color changing rgb keyboard is going rave mode under my face

[triviallytrue]

can you say more about 'not being a left unity guy' and what that means to you? what's your vision of effective opposition to the far right without left unity?

so left unity means a million different things in a million different scenarios but:

"the left" is a posthoc label applied to a bunch of different people that all want wildly different things

one of the only real commonalities is how much everybody loves infighting. some of this is familiarity breeding contempt and the tyranny of small differences, and some is genuinely irreconcilable views of how society should be run

as a result i don't really think "unity" or anything approaching that is possible or even really desirable.

i think a good principle at all times is "if it looks like a fascist is going to take power, you need to shut up and get behind whichever force is best equipped to stop them, regardless of the circumstances"

however more often than not this involves allying with the center as opposed to other portions of the left, so we aren't even really talking about left unity at this point

[blazehedgehog]

"Shadow was made based on Gerald studying the ancient echidna prophecy in Hidden Palace Zone." careful, you're getting fandom theories and headcanon mixed with the actual text of what you're talking about. there's never been a mention of Gerald working from Hidden Palace- all the ruins we see in SA2 are SA1 ruins. Even if Ian Flynn now retcons that in in some supplemental material, it'll still be a posthoc retcon.

Oh sorry I didn't see the sign over there

Not to be rude or anything, but I don't work for Sega or IDW or anyone official.

It's pretty clear Shadow is based on the look of Super Sonic. We never see Gerald investigate any ancient echidna stuff, but Maria does tell us some things about how the Artificial Chaos were created in the Shadow the Hedgehog flashback levels, confirming the obvious: that they were based on "the God of an ancient culture." (So, you know, Artificial Chaos were based on Chaos)

Now, what we see, both in Hidden Palace and Lost World, are murals depicting two things: a prophecy foretelling a fight between Eggman and Super Sonic, and Perfect Chaos. We never see any murals of lower forms of Chaos, but the Artificial Chaos are obviously based on something below Perfect Chaos.

When Chaos transforms for the first time, Eggman mentions "it's just as the stone tablets predicted." So there was possibly information on what lower forms of Chaos looked like. Where did he get them? Did he inherit them from Gerald?

Maybe he got them from The Lost World ruins. I mean, it's right outside of Final Egg, so maybe. Final Egg is also in the heart of a vast jungle, and from within Lost World itself, we can even see more ruins far in the distance. And if Eggman got them from Gerald's estate, who even knows where he got them from.

So there was detailed documentation about these things probably in multiple locations from both before and after the event that nearly wiped out Knuckles' ancestors and lead to the creation of Angel Island. After all, Lost World and Final Egg (plus the jungle surrounding it) aren't part of Angel Island -- only Icecap and Red Mountain are. (And a datamined list suggests Mushroom Hill was originally going to be represented in some form, too)

Through deduction, we can assume there's probably more than one Super Sonic mural. Maybe by different artists! Maybe even statues! Just like there are many different ancient hieroglyphs depicting Anubis in different shapes, styles, and forms.

It's splitting hairs. Live a little. It is an extremely educated guess that Shadow the Hedgehog is meant to be an interpretation of Super Sonic, wherever in ancient echidna culture that came from. The suggestion is pretty clear that Gerald was looking at that material around the same time both Biolizard and Shadow were created. It makes a hell of a lot more sense than "aliens from space made a blood pact."

[voxofthevoid]

how would you describe your writing style? I've been reading your works since last year and I'm always so hooked with every word, I want to know how you personally interpret your own style because it's just so beautifully paced and written :DD

Thank you, anon! That's very flattering 🥺❤️

And gooood question actually. I've never really tried to describe it, aside from a general overview of the techniques I favor.

Emotion-heavy and highly internal: There's very little plot in my stories, and even when there is, it tends to be less about the events and more about how the characters are feeling and reacting to them. The bulk of the stories is comprised of character interactions and interior monologue.

Descriptive in a way that focuses on the senses: The specific senses I'm focusing on depends on the scene, but touch and sight tend to dominate, in porn and otherwise. These do the heavy lifting when it comes to setting the scene.

Extensive use of figurative language: Alliteration and assonance are my bread and butter, but named devices aside, I'm prone to building imagery and using those to convey mental states, relationships, general motifs, etc. Plus, it's fun to play with words like that.

That's the core of it, I think! It's not a style I consciously developed, so this is more of a posthoc analysis. The style isn't static either, though at this point, I'm building on these core elements more than making any major shifts or changes. I don't want to stagnate, but having a foundation I'm happy with is a good feeling. I'd like to tackle proper stream of consciousness someday, but the few times I've tried, it's been...slippery.

Thanks for asking this, anon! I had fun figuring out the answer.

[shamira22]

1. **Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.Here is an example using Python:```pythonimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport scipy.stats as stats# Load your datasetdf = pd.read_csv('your_dataset.csv')# Display the first few rows of the datasetprint(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variablemodel = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc testfrom statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B."7. **Submit Your Assignment**: - Ensure you follow all submission guidelines provided by Coursera.If you need specific help with your dataset or any part of the code, feel free to ask!

[atthecenterofeverything]

something i will always fail to understand about this website is how hard it is for so many people on here to learn to question the “it was better before” instinct - nostalgia being one of its manifestations but not the only one. it’s a failure to historically interrogate processes and phenomena of the past, and a form of exceptionalism - things were not perfect back then but at least we had x, we need to bring y back, this was fucked up but it was at least better than what we have now, i would take (bigotry of the past) over (bigotry of the present) any day, etc.

I would say there’s 3 main manifestations of this issue - 1. justifying aesthetic choices as a posthoc political position - e.g. these endless posts decrying modern beige mcmansions and praising 70s maximalist architecture, as if one was “soulless capitalism” and the other wasn’t, instead of two equal manifestations of wealth and exploitation with different surfaces, 2. decrying the loss of imperial privileges as something to aspire to return to for citizens of the global north and especially the US - e.g. “we need to bring back the dollar menu!” or praising a time where a (white, middle-class, suburban) family could afford a home on a single income as something to aspire to; and 3. blatant misreading of a situation, lack of knowledge about a movement, or thinking historical processes were caused either by One Person (e.g. JK Rowling did unimaginable damage to feminism!) or by superstructural consequences (“stranger danger was such a damaging ideology for public life in the US”).

and then of course the plain and simple claim that is not only impossible to measure or prove but also refers to something that was demonstratively never a reality: “reading comprehension is at an all time low”, “media literacy is in shambles”, etc. when was this time when reading comprehension was high, and how would you define it in a way that applies to all the times it is used on this website?

and this is such a widespread and blatantly reactionary mindset. there is at least a bit of pushback now against the idealization of 60s gay bar culture and 80s tme lesbianism on here now, but this desire for a simpler, better, idealized past seeps into so many other spheres; the constant decrying of “ipad babies” and the widespread cognitive social decline this would supposedly cause, an idea as transparently fascistic as it gets; the longing for web 2.0 as a simpler, safer, more beautiful time; and of course the endless stream of “it’s not that deep”  comments facing anyone who questions this instinct or points at this pattern.a good way to counter this impulse is, whenever you encounter a claim about x being in decline, or y having been greatly damaged by something, or wanting to have z, which was imperfect but better than it is now, asking yourself- what specific superior historical period am I referring to? what aspects, trends or phenomena of this period am I praising, and how do  I know about those? what other forces were necessary to make this possible? what groups, countries, or classes were excluded from accessing those trends? do I implicitly base my vision and understanding of the past of my memories of being 10 years old or of rose-tinted glasses accounts from reactionaries? am I identifying correctly the cause-consequences relationship in this evolution?

[sunskate]

I don’t doubt that the environment was toxic at Canton.

But I’d like to gently push back on the idea that Marina was giving them lesser material or wasn’t invested in giving them material that could win. They might feel that way and I don’t blame them because it’s all wrapped up in the negativity of that era but it doesn’t match what was actually put on the ice. Carmen was groundbreaking, has influenced many a program since, and was beyond difficult. Too difficult - they never skated it cleanly. But had they gone clean that season and won Worlds, they’d have had the momentum into Sochi. The music of Seasons never clicked in their minds, but the actual skating content is more sophisticated and more nuanced than any other free program they’ve done, and far far more intricate than D/W’s programs. Marina put in a counter clockwise circular step just to prove they could do it and nobody else could! The transitions are stunning! And V/M say themselves they should’ve stuck with Carmen for crowd appeal - a Marina program. So it’s not like that quad they were stuffed by bad material. They were stuffed by inconsistency, the feeling that they’d had their turn, and the USA desperately wanting a champion.

The fact that Marina didn’t manage their emotional distress is her fault. But *both* their 2014 programs WERE good enough to win gold, and should’ve won. The judges didn’t go with it because ice dance is a fake sport - not because D/W skated better or because their programs were better. But the judges failing to go with it now gets used as the posthoc justification by the V/M fandom for stating unequivocally that Marina didn’t give them the material. Except she did. Moonlight Sonata wouldn’t suddenly become a superior piece of choreography to Moulin Rouge if Gabriella’s costume hadn’t split and V/M only got silver in 2018. The same principle applies here.

i didn't say she gave them bad material. i think it's more like Marina put a finger on the DW side of the scale and gave them the A+ package and VM A- in the FD. it's not the choreo that's the issue, and certainly not the performance. it's the concept and the music. at the end of the day, difficulty and intricacy don't win the Olympics. you can't get higher than level 4, and you don't get a GOE or PCS bonus for that unless there's a big impact from it emotionally or through a wow factor

the comparison to 2018 is useful because again, VM had the same coach/choreographer as their rivals - Marie-France choreographed Moonlight Sonata too, and clearly both were vehicles that could have won their team gold - it almost went the other way

if Marie-France gave PC a program not to Beethoven but to Weber or Salieri - that would be like what Marina did to VM - Glazunov and Scriabin are respectable and even admired but Moonlight Sonata and Scheherazade are in the standard repertoire and played frequently all over the world for a good reason, while the music for Seasons is really not

i'm not saying whether the judges would have gone for VM or not. more that Marina didn't give VM the best FD vehicle to persuade them

[nightpool]

okay so obviously the US health care system is awful and the FDA is probably marginally harmful and biohackers are cool and a single small-sample posthoc medical study isn't really science and anecdotes are not data and I REALLY just try not to have any opinions about nootropics whatsoever and okay has everybody stopped reading now? good? "CEO of the second largest blockchain exchange was abusing off-label prescription memory drugs that turn you into a compulsive gambler and binge shopper" is just. it's absolutely next level. you could never write this in a million years.

[regressingnow]

Alcoholism and Major Lifetime Depression : W2 Data Analysis Tools

For the second week’s assignment of Data Analysis Tool on Coursera, we would continue to be working with NESARC’s dataset which contains information on alcohol and drug use and disorders, related risk factors, and associated physical and mental disabilities.

We would be studying the effect of Major Depression in the life of an individual on their alcohol consuming status. We'd be performing an Chi-Square test of Independence test between a categorical explanatory variable (alcohol drinking status ), and a categorical response variable (presence of major lifetime depression). We'll also be restricting the test to include only adults of age between 18-40.

The explanatory variable has 3 groups

Current Drinker

Ex Drinker

Lifetime Abstainer

The response variable has 2 groups.

0. No Lifetime Depression

1. Has Lifetime Depression

The null hypothesis is that there is no association between the drinking status of an individual and the presence of Major Lifetime Depression

Running a Chi-Square Test of Independence between the data for two variables, we get :

In the first table, the table of counts of the response variable by the explanatory variable, we see the number of individual under each consumer group (1,2, or 3), who do and do not have major lifetime depression. That is, among current drinkers, 10472 individuals do not have a Lifetime depression, while 2768 individuals do suffer from depression.

The next table presents the same data in percentages of individuals with or without lifetime depression under each alcohol consumer group. So 79% of current drinkers do not have major lifetime depression, while 21% do.

The graph below also conveys the same, just for the proportion of individuals under each alcohol consumer group who have Major Lifetime Depression. So, 21% of current drinkers and 20% of Ex-Drinkers have Major Lifetime Depression, while only 11 % of Lifetime abstainers have suffer from depression.

The Chi-Square Value from the test is large, about 168, while the p-value is very small (<< 0.0001), which tells us that the presence of Major Lifetime Depression and the Alcohol-Consuming Status of an individual are significantly associated.

The explanatory variable has 3 categories, and by observing the plot we can infer say that the Life-Time Abstainers had a significantly lower rate of life-time depression diagnosis compared to the current-drinkers and ex-drinkers. To quantitatively verify the same, and to avoid a type 1 error, we'll use the Bonferroni Adjustment Posthoc test.

Since we need to make only three pairs of comparisons, we would evaluate significance at the adjusted p-value of 0.017 (0.05/3).

Now, running a chi-square test between just the group 1 and 2 of Alcohol-Consumer Status we get a low Chi-Square value of 0.211 and a large p-value 0.64 >> 0.017. We hence will accept the null-hypothesis that there is no significant difference in the rates of Major Lifetime Depression among current-drinkers and ex-drinkers.

Running a chi-square test between just the group 1 and 3 of Alcohol-Consumer Status we get a high Chi-Square value of 165 and a low p-value << 0.017. We hence will reject the null-hypothesis that there is no significant difference in the rates of Major Lifetime Depression among current-drinkers and life-time abstainers.

Finally, using a chi-square test between just the group 2 and 3 of Alcohol-Consumer Status we get a high Chi-Square value of 89 and a low p-value << 0.017. We hence will once again reject the null-hypothesis that there is no significant difference in the rates of Major Lifetime Depression among Ex-Drinkers and life-time abstainers.

Thus, using the Bonferroni Adjustment, we can conclude that there is a significant difference in the occurrence of major life-time depression between Lifetime alcohol Abstainers as compared to current-drinkers or ex-drinkers. However, the rate of depression is not significantly different between current-drinkers and ex-drinkers.

Python Code

@author: DKalaikadal159607 """

import pandas import numpy import scipy.stats import seaborn import matplotlib.pyplot as plt

data = pandas.read_csv('nesarc.csv', low_memory=False)

#new code setting variables you will be working with to numeric

data['MAJORDEPLIFE'] = pandas.to_numeric(data['MAJORDEPLIFE'], errors='coerce') data['CONSUMER'] = pandas.to_numeric(data['CONSUMER'], errors='coerce') data['AGE'] = pandas.to_numeric(data['AGE'], errors='coerce')

#subset data to young adults age 18 to 40

sub1=data[(data['AGE']>=18) & (data['AGE']<=40)]

#make a copy of my new subsetted data

sub2 = sub1.copy()

#contingency table of observed counts

ct1=pandas.crosstab(sub2['MAJORDEPLIFE'], sub2['CONSUMER']) print (ct1)

colsum=ct1.sum(axis=0) colpct=ct1/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs1= scipy.stats.chi2_contingency(ct1) print (cs1)

seaborn.catplot(x="CONSUMER", y="MAJORDEPLIFE", data=sub2, kind="bar", ci=None) plt.xlabel('Alcohol Consumer Status') plt.ylabel('Proportion with Major Depression')

recode2 = {1: 1, 2: 2} sub2['COMP1v2']= sub2['CONSUMER'].map(recode2)

#contingency table of observed counts

ct2=pandas.crosstab(sub2['MAJORDEPLIFE'], sub2['COMP1v2']) print (ct2)

#column percentages

colsum=ct2.sum(axis=0) colpct=ct2/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs2= scipy.stats.chi2_contingency(ct2) print (cs2)

recode3 = {1: 1, 3:3 } sub2['COMP1v3']= sub2['CONSUMER'].map(recode3)

#contingency table of observed counts

ct3=pandas.crosstab(sub2['MAJORDEPLIFE'], sub2['COMP1v3']) print (ct3)

#column percentages

colsum=ct3.sum(axis=0) colpct=ct3/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs3= scipy.stats.chi2_contingency(ct3) print (cs3)

recode4 = {2: 2, 3: 3} sub2['COMP2v3']= sub2['CONSUMER'].map(recode4)

#contingency table of observed counts

ct4=pandas.crosstab(sub2['MAJORDEPLIFE'], sub2['COMP2v3']) print (ct4)

#column percentages

colsum=ct4.sum(axis=0) colpct=ct4/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs4= scipy.stats.chi2_contingency(ct4) print (cs4)

[coursera1]

1

ANOVA Analysis on the Effect of Teaching Methods on Test Scores

In this analysis, we examine whether there are significant differences in students' test scores based on different teaching methods: Traditional Lecture, Interactive Learning, and Online Learning. We use a one-way ANOVA to test for differences among the groups and conduct post hoc pairwise comparisons if the overall ANOVA is significant.

Create a data frame with test scores and teaching method

data <- data.frame( scores = c(78, 85, 82, 90, 87, 84, 92, 88, 91, 75, 80, 78, 76, 79, 77), method = factor(c(rep("Traditional", 5), rep("Interactive", 5), rep("Online", 5))) )

Run one-way ANOVA

anova_result <- aov(scores ~ method, data = data)

Display ANOVA table

summary(anova_result)

Post hoc pairwise comparisons using Tukey's HSD

posthoc <- TukeyHSD(anova_result)

Display post hoc results

posthoc

Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = scores ~ method, data = data) $method diff lwr upr p adj Interactive-Traditional 8.8 5.771838 11.828162 2.21e-05 Online-Traditional 1.4 -1.628162 4.428162 0.470873 Online-Interactive -7.4 -10.428162 -4.371838 4.39e-05

Interpretation:

The ANOVA results show a significant effect of teaching method on test scores (F(2,12) = 35.03, p < 0.001). This indicates that at least one teaching method leads to significantly different test scores compared to others.

Post hoc pairwise comparisons using Tukey's HSD test reveal that:

Interactive vs. Traditional: Students in the Interactive Learning group scored significantly higher than those in the Traditional Lecture group (mean difference = 8.8, p < 0.001).

Online vs. Traditional: There is no significant difference between the Online Learning and Traditional Lecture groups (mean difference = 1.4, p = 0.471).

Online vs. Interactive: Students in the Interactive Learning group scored significantly higher than those in the Online Learning group (mean difference = 7.4, p < 0.001).

These results suggest that Interactive Learning is more effective in improving students' test scores compared to both Traditional Lecture and Online Learning methods.

[ramanidevi16]

Running on Analysis of Variance:

Python Code:

1. **Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).

2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.

3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.

4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.

Here is an example using Python:```pythonimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport scipy.stats as stats# Load your datasetdf = pd.read_csv('your_dataset.csv')# Display the first few rows of the datasetprint(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variablemodel = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc testfrom statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B."7. **Submit Your Assignment**: - Ensure you follow all submission guidelines provided by Coursera.If you need specific help with your dataset or any part of the code, feel free to ask!

[xxxjarchiexxx]

i find arguments against moralism at times to fall apart-- i don't disagree that morals are subjective and easy to posthoc justify but that doesnt make u immune to morality or above having it. even explanations that address this end up saying something along the lines of "well xyz act is valuable because its beneficial for people", but even that avoids the question of WHY do you want to benefit people or help people who are not you? where does altruism come from if there is no difference if someone lives or dies if they are not in your life or impacting your life in any way?

is this something that makes sense for people with empathy + sympathy that function regularly? this is a genuine question, bcuz if i didn't have a personal moral code i cared about, i don't think i would be capable of caring for/about the material status of others who don't impact my life in any way. so if you care while also not thinking helping people is moral then like. why?

im open to being wrong 100%, bcuz i do agree with many critiques of moralism and i agree with materialism as a foundation for all social analysis but often time as these critiques conclude with "and we must help people anyway", i dont understand the motivation or reasoning behind that conclusion and it always sounds like an abstraction away from moralism

[krishnamanohari2108]

1. **Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.Here is an example using Python:```python import pandas as import statsmodels.api as from statsmodels.formula.api import import scipy.stats as stats# Load your dataset ddf = pd.read_csv('your_dataset.csv')# Display the first few rows of the data set print(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variable model = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc test form statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B."7. **Submit Your Assignment**: - Ensure you follow all submission guidelines provided by Coursera.If you need specific help with your dataset or any part of the code, feel free to ask!

[varsha172003]

Select Your Data Set and Variables:

Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).

Load the Data into Python:

Use libraries such as pandas to load your dataset.

Check Data for Missing Values:

Use pandas to identify and handle missing data.

Run the ANOVA:

Use the statsmodels or scipy library to perform the ANOVA.

Here is an example using Python:import pandas as pd import statsmodels.api as sm from statsmodels.formula.api import ols import scipy.stats as stats # Load your dataset df = pd.read_csv('your_dataset.csv') # Display the first few rows of the dataset print(df.head()) # Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variable model = ols('score ~ C(group)', data=df).fit() anova_table = sm.stats.anova_lm(model, typ=2) print(anova_table) # If the ANOVA is significant, conduct post hoc tests # Example: Tukey's HSD post hoc test from statsmodels.stats.multicomp import pairwise_tukeyhsd posthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05) print(posthoc)

Interpret the Results:

The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means.

For post hoc tests, the results will show which specific groups are different from each other.

Create a Blog Entry:

Include your syntax, output, and interpretation.

Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B.

[shwetha18112002]

Running and analysing data

1. Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).

2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.

3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.

4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.Here is an example using Python:```pythonimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport scipy.stats as stats# Load your datasetdf = pd.read_csv('your_dataset.csv')# Display the first few rows of the datasetprint(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variablemodel = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc testfrom statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```

5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.

6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B.