library(tidyverse) #used for visualization, summarization, and basic wrangling
library(ggridges) #used for making density ridge plots
library(broom) #used for "prettier" and easier to work with model output
library(fivethirtyeight) #datasets

Bechdel Test

Before we jump into the statistics, take time to read this fivethirtyeight article. We are going to be using more or less the same data they used in that article. The cartoon by Alison Bechdel that test comes from is shown here:

Throughout the analysis, you should use the bechdel_1990up dataset that removes missing values and only includes movies that were made in 1990 or later.

bechdel_1990up <-
  bechdel %>% 
  filter(year>1989, 
         !is.na(budget_2013), 
         !is.na(intgross_2013))

Exploratory Work

  1. The code below summarizes the percentage of films that pass for each of the 24 years that we have data. It also counts the number of films by year. Use this code and pipe into a ggplot to create a graph of pass_pct by year. Represent it as both a point and a line and size the points by n. (HINT: remember the size aesthetic). Describe the trend you observe. (Remove the eval=FALSE piece if you want this to run when you knit it)
bechdel_1990up %>% 
  group_by(year) %>% 
  summarize(pass_pct = mean(binary=="PASS"),
            n = n())

  1. Create a graph that shows the distribution of intgross_2013, the film’s international gross in 2013 inflation adjusted US dollars. Describe the distribution.

  2. Now compare the distribution of intgross_2013 by binary (PASS if the film passed the test and FAIL if it did not). Describe the differences, if any.

  3. Also create a graph that compares the distribution of budget_2013, the film’s budget in 2013 inflation adjusted US dollars, by binary. Describe the differences, if any.

  4. Create a plot that examines the relationship between budget_2013 and intgross_2013 (use intgross_2013 as the response variable). Color the points by binary. Describe what you observe.

Fitting and Interpreting Models

According to the fivethirtyeight article, there is a perception in Hollywood that “films featuring women do worse at the box office”. We aim to investigate this using the bechdel_1990up data. We have already started this investigation in the previous section with some exploratory work. Now we will fit some models to help solidify our observations.

  1. Fit a model that uses budget_2013 to explain intgross_2013. Interpret both coefficients in the context of the data.

  2. Use the model to find the fitted value and the residual for the movie “Cloudy with a Chance of Meatballs”.

  3. Test if budget_2013 has a linear relationship with intgross_2013.

  4. Fit a model that uses just the binary bechdel test results to explain intgross_2013. Interpret both coefficients in the context of the data.

  5. Use the model to find the fitted value and the residual for the movie “Cloudy with a Chance of Meatballs”.

  6. What is the hypothesis test actually testing in the binaryPASS row of the model table? So, what do you conclude?

  7. Fit another model that uses both budget_2013 AND binary to predict intgross_2013.

  8. Interpret all three coefficients in the context of the data. How does the binaryPASS coefficient in this model compare to its coefficient in the model with only that variable? Explain why.

  9. Represent the previous model on a graph with intgross_2013 on the y-axis and budget_2013 on the x-axis.

  10. Use the anova method to test if binary is useful in the model that already includes budget_2013.

  11. Find the fitted value and the residual for the movie “Cloudy with a Chance of Meatballs”.

  12. Try building other models that use more variables. Can you find any more useful variables?