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Regression Models:
Assignment #3:
Name: ________________________________________________________________________
Assignments are designed to help you master the important concepts presented in lecture and the
text so that you don’t need to stress about the exams. The subject mater in assignments will be
similar to what appears on exams. Mastery of this material is crucial to understanding the
statistical methods that scholars use in our field. Feel free to collaborate with your peers,
however, if you cannot complete these questions by yourself, and in a reasonable amount of
time, you need more exposure to the concepts in order to do well on the upcoming exam.
Use your dataset to complete the following questions. Please attach the appropriate Stata output
whit each question highlighted, labeled by question number, and placed in the correct order in
which they are asked. Also be sure to describe your dataset and variables that you use in your
work. Your work will be graded on the accuracy of the information provided as well as the
degree of academic professional style attained in each response. There are 9 items, based on 6
different questions; each is worth up to 5 points for a possible of 40 points.
1) Choose 3 continuous variables appropriate for a multivariate regression model. Explain
why your analysis is appropriate. Use Stata to analyze the relationship between these
variables and describe the results to the reader. Be sure to interpret the effect of a) each
coefficient, b) each standardized coefficient, and c) the R2 of the model.
2) Add a dichotomous independent variable to the model you constructed above. Describe
and interpret this new variable to the reader.
3) Perform an F test to see if the addition of this new variable significantly improves your
models fit.
4) Add a discrete variable with 3 or more categories to your model above. Describe and
interpret this new variable to the reader. Also be sure to explain the rational used for
choosing the reference category.
5) Perform an F test to see if the addition of this new variable significantly improves your
models fit.
6) Construct a correlation matrix for the variables in your model. Interpret this matrix for
your reader.
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