Learning goals

By the end of the lab you will be able to…

Use logistic regression to explore the relationship between a binary response variable and multiple predictor variables
Conduct exploratory data analysis for logistic regression
Interpret coefficients of logistic regression model
Compare models and select the one that best fits the data

Data: General Social Survey

The General Social Survey (GSS) has been used to measure trends in attitudes and behaviors in American society since 1972. In addition to collecting demographic information, the survey includes questions used to gauge attitudes about government spending priorities, confidence in institutions, lifestyle, and many other topics. A full description of the survey may be found here.

The data for this lab are from the 2016 General Social Survey. The original data set contains 2867 observations and 935 variables. We will use and abbreviated data set that includes the following variables:

natmass: Respondent’s answer to the following prompt:

“We are faced with many problems in this country, none of which can be solved easily or inexpensively. I’m going to name some of these problems, and for each one I’d like you to tell me whether you think we’re spending too much money on it, too little money, or about the right amount…are we spending too much, too little, or about the right amount on mass transportation?”
age: Age in years.
sex: Sex recorded as male or female
sei10: Socioeconomic index from 0 to 100
region: Region where interview took place
polviews: Respondent’s answer to the following prompt:

“We hear a lot of talk these days about liberals and conservatives. I’m going to show you a seven-point scale on which the political views that people might hold are arranged from extremely liberal - point 1 - to extremely conservative - point 7. Where would you place yourself on this scale?”

The data are in gss2016.csv in the data folder.

Exercises

The goal of today’s lab is to use the GSS to examine the relationship between US adults’ political views and attitudes towards government spending on mass transportation projects.

Part I: Exploratory data analysis

Let’s begin by making a binary variable for respondents’ views on spending on mass transportation. Create a new variable that is equal to “1” if a respondent said spending on mass transportation is about right and “0” otherwise. Then make a plot of the new variable, using informative labels for each category.
Recode polviews so it is a factor with levels that are in an order that is consistent with question on the survey. Note how the categories are spelled in the data.
- Make a plot of the distribution of polviews.
- Which political view occurs most frequently in this data set?
Make a plot displaying the relationship between satisfaction with mass transportation spending and political views. Use the plot to describe the relationship the two variables.
We’d like to use age as a quantitative variable in your model; however, it is currently a character data type because some observations are coded as "89 or older".
- Recode age so that is a numeric variable. Note: Before making the variable numeric, you will need to replace the values "89 or older" with a single value.
- Then plot the distribution of age.

Part II: Logistic regression model

Briefly explain why we should use a logistic regression model to predict the odds a randomly selected person is satisfied with spending on mass transportation.
Let’s start by fitting a model using the demographic factors - age, sex, sei10, and region - to predict the odds a person is satisfied with spending on mass transportation. Make any necessary adjustments to the variables so the intercept will have a meaningful interpretation. Neatly display the model.
Interpret the intercept in the context of the data.
Consider the relationship between age and one’s opinion about spending on mass transportation. Interpret the coefficient of age in terms of the odds of being satisfied with spending on mass transportation.
Now let’s see whether a person’s political views has a significant impact on their odds of being satisfied with spending on mass transportation, after accounting for the demographic factors.

Conduct a drop-in-deviance test to determine if polviews is a significant predictor of attitude towards spending on mass transportation. State the null and alternative hypotheses in words, display all relevant code and output, and state your conclusion in the context of the problem.
Use the model selected in the previous exercise to describe the relationship (if any) between one’s political views and their attitude towards spending on mass transportation. Be sure your answer includes the interpretation of the model coefficients and associated hypothesis tests or confidence intervals used to support your response.

Submission

Select one team member to upload the team’s PDF submission to Gradescope.
Be sure to mark every team member’s name on Gradescope.
Associate the “Workflow & formatting” graded section with the first page of your PDF, and mark the page associated with each exercise. If any answer spans multiple pages, then mark all pages.

There should only be one submission per team on Gradescope.

Grading (50 pts)

Component	Points
Ex 1 - 10	40
Model diagnostics activity (individual score)	5
Workflow & formatting	5

Grading notes:

The “Workflow & formatting” grade is to assess the reproducible workflow and team work. This includes having at least one meaningful commit by each team member and updating the name and date in the YAML.

There should only be one submission per team on Gradescope.

Lab 06: Logistic regression