Excel Assignment (Prompt)

ECON 3100, Fall 2023

Due 11/21/23 at 3:00 PM

Note I do not care as long as it is in by midnight on the 21st. Iset this as the due date time because I will not be responding to email about the assignment after that point.

You will conduct an analysis (mostly using regressions) of a dataset, answer specific questions, and write ashort paper (1-2 single spaced pages) presenting your findings. Your paper must be a minimum of 1

single spaced page (normal margins, 11- or 12-point font) and a maximum of 2 pages. Your submission  will include both an excel document and either a pdfor word document. In the Excel Document, make  sure to clearly label each worksheet, especially the regressions. Both the PDF/Word Document and the Excel Document will be graded.

Information about the dataset: The data is 83 Montana renters. Our goal is to take information about  our sample to make statements about the population of interest (Montanan’swho rent). The variable we will use as our dependent variable is gross rent. Our various independent variables are sex (all 83   respondents answered either male or female), marital status (condensed to three possible options, married, single, or separated), number of vehicles owned, monthly income (in dollars), and whether or not the individual lives in a rural area.

Regression 1: Run a regression with gross rent as the dependent variable, and monthly income, sex and   rural area as the independent variables. Recode the sex variable so 1 = female and 0 = male. Recode the  rural area variable so yes=1 and no=0. Include this regression in excel as a separate worksheet and make sure it is clearly labeled “Regression 1” .

A)   Interpret ALL of the slope coefficients (but not the intercept, be specific)

B)   Which variables are statistically significant? How do you know? Provide at least two pieces of evidence. If none are statistically significant, answer N/A.

C)   Which variables are not statistically significant? How do you know? Provide at least two pieces of evidence.  If none are statistically significant, answer N/A.

D)   What is the predicted gross rent for a female, that does not live in a rural area, that makes 2500 dollars per month?

Regression 2: Create a series of dummy variables for the marital status variable and make “single” your   reference category. Run a regression with the same dependent and independent variables as regression 1, but now add your new binary variables.

E)    Interpret the new slope coefficients (only for the marital status variables).

F)    Are either or both of the new variables statistically significant? How do you know? Provide at least two pieces of evidence (for each variable)?

G)   Does this new model have a higher ADJUSTED R-Squared than Regression 1? What does this mean?

Regression 3: Create new dummy variables based on the number of vehicles variable. Instead of running number of vehicles as part of the regression as a numeric variable, make a new binary variable for all the possible values for number of vehicles (except for zero, this will be your reference category). Run a new   regression including all of the variables from Regression 2 and adding the new binary/dummy variables    you just created.

H)   Interpret the slopes on all of your new binary/dummy variables?

I)     Which vehicle variables are statistically significant (if any)? How do you know?

J)    Name one comparison (in terms of the dummy variables) that you are NOT able to make, in terms of statistical significance, and state why.

K)   Conduct a partial F-Test to check the joint significance of the new variables? Are the new variables combined statistically significant? How do you know?