Below are the results of a regression using GSS data. Individuals were asked how

Question

Below are the results of a regression using GSS data. Individuals were asked how often they gave money to panhandlers? A number of independent variables were used including the respondent's income, political ideology, race, education, age, and sex, as well as geographic variables such as population size of the respondent's location.

a) Which variables are statistically significant, and how do they affect the frequency of giving to the homeless?

b) How much variance does the above model explain? Is it a good model?

Independent Variables Intercept (Income) op. Size) (Conservatism) (Race, White) Education) (Gender Male) R2 Coefficients 7.895 013 002** 486 3.234 076 -1.382 047 029 1190 p

Explanation / Answer

hoi: beta_i is not significant. betai = 0. for i=1,2,3,,4,5,6,7

v/s h1i: beta_i is significant. betai = 0. for i=1,2,3,,4,5,6,7

with p-value < 0.05, i can say that coefficients x2(popn size), x4(race, white) are the only significant variables.

with a unit increase in popn size, there is .002 units increase in money given to panhandlers.

for whites, there is 3.235 units less in money given to panhandlers as compred to other races.

with R^2= 2.9%. onlt 2.9% variaiton in dependent variable money given to panhandlers in explained by all independent variables. this percentage is very less. and model doesnt seem to be a good fit to the data

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