In emeyers/SDS100: Tools for the class Introductory Statistics

knitr::opts_chunk$set(echo = TRUE)

library(SDS100)

Should you choose B on a multiple choice test?

Multiple-choice questions on Advanced Placement exams have five options: A, B, C, D, and E. A random sample of the correct choice on 400 multiple-choice questions on a variety of AP shows that B was the correct answer 90 of the 400 questions. Does this provide evidence that B occurs at a different rate than what is expected if each question is equally likely?

Please run a hypothesis test to find out!

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# step 1: state null and alternative hypotheses





# step 2: compute observed statistic




# step 3: create the null distribution






# plot the null distribution









# step 4: calculate the p-value








# 5. decision?





# FYI: You don't need to look at the actual data to solve this problem, 
# but if you'd like it can be loaded using:
#  APMultipleChoice <- Lock5Data::APMultipleChoice



# Bonus, can you create a 95% CI for the proportion of answers that are B? 

answers <- Lock5Data::APMultipleChoice$Answer


# Create the bootstrap distribution






# visualize the bootstrap distribution 




# calculate the standard error and create a confidence interval

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Do private colleges have the same graduation rates as public colleges?

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# State the null and alternative hypotheses in symbols and words






# step 2: calculate the observed stat and visualize the data

college <- read.csv("https://www.lock5stat.com/datasets3e/CollegeScores4yr.csv")

public <- subset(college, Control == "Public")
private <- subset(college, Control == "Private")

public_CompRate <- public$CompRate
private_CompRate  <- private$CompRate



# What's a good way to visualize the data for our question of interest? 





# calculate the observed statistic




# step 3:









# visualize the null distribution 









# Step 4: calculate the p-value









# step 5: Conclusion?

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Do movies that pass the Bechdel test have the same average budgets as movies that fail?

$\$

# step 1:












# step 2: observed stat



library(fivethirtyeight)

bechdel <- bechdel


passed <- subset(bechdel, binary == "PASS")
failed <- subset(bechdel, binary == "FAIL")

passed_budget <- passed$budget_2013
failed_budget <- failed$budget_2013


# What's a good way to visualize the data for our question of interest? 














# step 3:












# step 4:










# step 5: Conclusion?