Statistics 512 Problem Set 7: Analyzing Diagnostics for a Regression Model - Prof. Kristof, Assignments of Statistics

In this problem set for statistics 512, students are required to analyze diagnostic plots and values for a regression model using the cs dataset. They must identify and explain issues such as outliers, influential observations, and multicollinearity based on studentized residuals, studentized-deleted residuals, cook's d, tolerance or vif, and partial residual plots. No tables of values for all individuals are to be included, only plots and verbal summaries.

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Pre 2010

Uploaded on 07/30/2009

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Statistics 512: Problem Set No. 7
Due October 24, 2008
For this problem use the CS dataset examined in previous problem sets, and use the model which
uses only HSM and HSE as explanatory variables to predict the response GPA. On Homework 6,
Problem 3 you examined some visual diagnostics (for your chosen model), which should have include
plots of Y vs. X, residuals, etc. Now, additionally examine other diagnostics such as studentized
and studentized-deleted residuals, Cook’s D, tolerance or vif, and partial residual plots. Explain any
problems such as outliers, highly influential observations or multicollinearity that these diagnostics
point out. (Do not include in your output any tables of values for all 224 individuals. Use plots
and verbal summaries instead. You may include values for a few selected individuals if you wish.)
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Statistics 512: Problem Set No. 7 Due October 24, 2008

For this problem use the CS dataset examined in previous problem sets, and use the model which uses only HSM and HSE as explanatory variables to predict the response GPA. On Homework 6, Problem 3 you examined some visual diagnostics (for your chosen model), which should have include plots of Y vs. X, residuals, etc. Now, additionally examine other diagnostics such as studentized and studentized-deleted residuals, Cook’s D, tolerance or vif, and partial residual plots. Explain any problems such as outliers, highly influential observations or multicollinearity that these diagnostics point out. (Do not include in your output any tables of values for all 224 individuals. Use plots and verbal summaries instead. You may include values for a few selected individuals if you wish.)