@Academic Giant. Help with statistic research methods

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13.3 A researcher heard about a company that was considered very friendly to women 40 years ago, when it was not so common. He wanted to determine whether this company had discriminated on the basis of gender. He examined the salaries of employees when hired and ran a regression in which StartingAnnualSalary (in dollars) was the dependent variable and the independent variables were Education (in years) and a Female dummy variable. (Note that for this organization in this period, mean starting salary was $6,806, median was $6,000, and standard deviation was $3,148.) Results are below: GRAPH IN SCANS PG 422

a. Interpret in words the coefficient of the Female variable. Is it statistically significant? Is it practically significant?

b. Imagine you wanted to evaluate whether gender discrimination was occurring at this business at the same time. Describe another control variable you would want, briefly justifying your choice and the problems from omitting it.

How much Is a Garden Worth in Manhattan?

13.4 Say you are asked to study how much outdoor space (a balcony, garden, or patio) raises the price of an apartment in an expensive New York City neighborhood, the West Village. To answer this question, you have obtained a random sample of 32 out of the 290 current listings for residential apartments in the West Village area listed through the New York Times (as of August 2009). The data include the following variables:

Price—listed price of the apartment, in dollars

Bedrooms—number of bedrooms

Bathrooms—number of bathrooms

Rooms—number of total rooms in the equipment

SqFt—number of square feet in the apartment

Age—age of the apartment building, in years

Garage—1 if garage is available in the building, otherwise 0

Pets—1 if pets allowed, otherwise 0

Doorman—1 if building has a doorman, otherwise 0

Fireplace—1 if apartment has a fireplace, otherwise 0

Elevator—1 if building has an elevator, otherwise 0

Outdoor—1 if apartment has a balcony, garden, or patio, otherwise 0

Your first analysis is a regression with price as the dependent variable and Outdoor as the only independent variable. Your results are: GRAPH IN SCANS PG.423

a. What is the regression equation, including the coefficients from your regression analysis and their units?

b. Using just this output, what is your estimate (so far) of the effect of outdoor space on the listing price of an apartment?

c. Are there any potential sources of bias? Predict the direction of bias.

d. For your next analysis, you run a regression with Price as the dependent variable and Outdoor and SqFt as the independent variables. Using the results below, what is the new regression equation, including the coefficients from the regression analysis and units?

SECOND GRAPH IN SCANS

e. Based on just the analysis so far, what is the estimated effect of outdoor space on the listing price of an apartment?

f. Why is it different than in the earlier regression?

g. Are there any other potential sources of bias? If so, how would you control for them ideally? How would you control for them with the data available?

h. How do you interpret the coefficient for SqFt?

i. How would having more than 32 observations change your results?