Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables. Create a research question...

1 answer below »


  • Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.

  • Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.


By Day 3


Estimate a multiple regression model that answers your research question. Post your response to the following:



  1. What is your research question?

  2. Interpret the coefficients for the model, specifically commenting on the dummy variable.

  3. Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?

Answered Same DayJul 30, 2021

Answer To: Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in...

Pooja answered on Jul 31 2021
152 Votes
Analysis
1) My research question is to test is the hours per day watching TV is dependent on age and gender.
The dependent variable is hours per day watching TV. The independent variables are age and gender. Gender is coded as 1 for male and 0 for female; this new variable is coded as male which is known as a dummy variable.
2) The regression equation is given by, hours per day watching TV = 1.938 + 0.020*age + 0.094*male
The coefficient of age is 0.02. With one-year increase in age of the respondent, watching TV hours is increased by 0.2 hours per day. Fox, J. (2015).
The coefficient of the male is 0.094. For males, watching TV hours is 0.094 hours per day more in comparison to that of females.
3) The first assumption of regression analysis is the normality of residuals. Since the normal probability plot is s-shaped, the assumption of normality of residuals is followed. Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E....
SOLUTION.PDF
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here
Have files?