Question

An analyst wishes to study how expenditure on travel by an individual varies with their gender and employment status. They collect cross-sectional data for these variables and the data is as follows:

Travel expenditure in
dollars Gender Employment Status

40 Male Employed
31 Female Unemployed
18 Male Unemployed
19 Male Unemployed
47 Male Employed
27 Female Unemployed
26 Female Unemployed
17 Male Unemployed
43 Male Employed
49 Male Employed
15 Male Unemployed
25 Female Unemployed
29 Female Unemployed
20 Male Unemployed
41 male Employed

Taking gender as D1i = 1 if individual is female, but 0 otherwise; and employment status as D2i = 1 if employed, but 0 otherwise; regress travel expenditure on gender and employment status

Answer 1

The student's question involves performing a regression analysis on travel expenditure data with gender and employment status as variables. The method uses dummy variables for gender and employment status to establish their influence on travel expenditure.

Step-by-step explanation:

The student is asking to perform a regression analysis on travel expenditure based on gender and employment status. Assuming gender as D1i, where D1i = 1 for females and 0 for males, and employment status as D2i, where D2i = 1 if employed and 0 otherwise, the analyst would use a statistical software or method to regress the expenditure on these dummy variables.

The regression equation would typically look like this: Travel_Expenditure = β_0 + β_1D1i + β_2D2i + εi, where β_0 is the intercept, β_1 is the coefficient for the gender variable, β_2 is the coefficient for the employment status, and εi is the error term.

By applying the regression analysis, the analyst would be able to determine the influence of gender and employment status on travel expenditure, which could help in understanding consumer behavior and developing targeted marketing strategies.