Editing Statistics and Statistical Programming (Winter 2021)/Problem set 16
From CommunityData
The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then publish the changes below to finish undoing the edit.
Latest revision | Your text | ||
Line 8: | Line 8: | ||
# Interpret the results with a particular focus on the relationship between price and two of the predictors: <code>cond_new</code> and <code>stock_photo</code>. Be sure to explain what the results mean for those predictors in terms of the underlying variables (i.e., don't just talk about coefficients as such, but translate them back into the values/scales of the variables). | # Interpret the results with a particular focus on the relationship between price and two of the predictors: <code>cond_new</code> and <code>stock_photo</code>. Be sure to explain what the results mean for those predictors in terms of the underlying variables (i.e., don't just talk about coefficients as such, but translate them back into the values/scales of the variables). | ||
# Based on the results of this model, how would you advise a prospective vendor of a used copy of ''Mario Kart'' to maximize the auction price they might receive for the game on eBay? | # Based on the results of this model, how would you advise a prospective vendor of a used copy of ''Mario Kart'' to maximize the auction price they might receive for the game on eBay? | ||
=== PC2: Analyze and interpret a simulated study of education and income === | === PC2: Analyze and interpret a simulated study of education and income === | ||
The second part of this problem set poses an open-ended set of questions about a simulated dataset from an observational study of high school graduates' academic achievement and subsequent income. You can '''[https://communitydata.science/~mako/2021-COM520/grads.rds download the data here]'''. | The second part of this problem set poses an open-ended set of questions about a simulated dataset from an observational study of high school graduates' academic achievement and subsequent income. You can '''[https://communitydata.science/~mako/2021-COM520/grads.rds download the data here]'''. I have provided some information about the "study design" below ('''reminder/note: this is not data from an actual study'''): | ||
I have provided some information about the "study design" below ('''reminder/note: this is not data from an actual study'''): | |||
:: You have been hired as a statistical consultant on a project studying the role of income in shaping academic achievement. Data from twelve cohorts of public high school students was collected from across the Chicago suburbs. Each cohort incorporates a random sample of 142 students from a single suburban school district. For each student, researchers gathered a standardized measure of the students' aggregate GPA as a proxy for their academic achievement. The researchers then matched the students' names against IRS records five years later and collected each student's reported pre-tax earnings for that year. | :: You have been hired as a statistical consultant on a project studying the role of income in shaping academic achievement. Data from twelve cohorts of public high school students was collected from across the Chicago suburbs. Each cohort incorporates a random sample of 142 students from a single suburban school district. For each student, researchers gathered a standardized measure of the students' aggregate GPA as a proxy for their academic achievement. The researchers then matched the students' names against IRS records five years later and collected each student's reported pre-tax earnings for that year. | ||
Line 44: | Line 31: | ||
=== Trick-or-treating all over again === | === Trick-or-treating all over again === | ||
The final questions revisit the trick-or-treating experiment we analyzed a few weeks ago | The final questions revisit the trick-or-treating experiment [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|we analyzed a few weeks ago]]. | ||
Load up the dataset. For this exercise we're going to fit a few versions of the following model. | Load up the dataset. For this exercise we're going to fit a few versions of the following model. | ||
Line 56: | Line 43: | ||
* The outcome is dichotomous, so you can/should use logistic regression to model this data (we can discuss this choice in class). You may want to evaluate whether the conditions necessary to do so are met. | * The outcome is dichotomous, so you can/should use logistic regression to model this data (we can discuss this choice in class). You may want to evaluate whether the conditions necessary to do so are met. | ||
* You may want/need to convert some of these variables to appropriate types/classes in order to fit a logistic model. I also recommend at least turning <code>year</code> into a factor and creating a | * You may want/need to convert some of these variables to appropriate types/classes in order to fit a logistic model. I also recommend at least turning <code>year</code> into a factor and creating a centered version of the <code>age</code> variable (we can discuss this in class too). | ||
* Be sure to state the alternative and null hypotheses related to the experimental treatment under consideration. | * Be sure to state the alternative and null hypotheses related to the experimental treatment under consideration. | ||
* It's a good idea to include the following in the presentation and interpretation of logistic model results: (1) a tabular summary/report of your fitted model including any goodness of fit statistics you can extract from R; (2) a transformation of the coefficient estimating treatment effects into an "odds ratio"; (3) model-predicted probabilities for prototypical study participants. (''please note that examples for all of these are provided in this week's tutorial'') | * It's a good idea to include the following in the presentation and interpretation of logistic model results: (1) a tabular summary/report of your fitted model including any goodness of fit statistics you can extract from R; (2) a transformation of the coefficient estimating treatment effects into an "odds ratio"; (3) model-predicted probabilities for prototypical study participants. (''please note that examples for all of these are provided in this week's tutorial'') |