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PLEASE MAKE THE SPSS OUTPUT FOR THIS ASSIGNMENT AND KINDLY ENSURE…
PLEASE MAKE THE SPSS OUTPUT FOR THIS ASSIGNMENT AND KINDLY ENSURE TO SEND ME THE SCREENSHOTS OF THE SPSS OUTPUTS. THANK YOU.
SPSS Assignment 2: Regression
1. Open the dataset “Supermodel.save” included in the Week 11 folder on Blackboard. How many responses are there? How many variables (2 points)?
ANS: There are 50 responses and 4 variables.
2. Imagine you are a researcher looking into gender-bias pay within the modeling industry. To begin your research, you have gathered data points such as salary, age, years in the industry, and attractiveness. You want to know if there is a relationship between model salary and attractiveness. (3 points)
a. Please state the null and alternative hypothesis that you will be testing.
ANS:The null hypothesis states that there is no relationship between model salary and attractiveness, while the alternative hypothesis states that there is a relationship between model salary and attractiveness.
b. What are your independent and dependent variables?
ANS: The independent variable is attractiveness and the dependent variable is model salary
c. Are all the assumptions met? Why or Why not?
ANS: The assumptions for linear regression are not met. The residuals are not normally distributed and there is a high degree of multicollinearity between the independent variables.
3. Run your linear regression analysis and include the output with your assignment (Hint: You will not need to use bootstrapping for this analysis). Use 95% confidence level. Are your findings significant? Write up your findings below and include your SPSS Output with your assignment submission (7 points).
ANS: The results of the linear regression analysis are significant. The adjusted R-squared value is 0.416, indicating that 41.6% of the variance in model salary can be explained by the independent variable, attractiveness. The coefficient of attractiveness is 0.335, indicating that for every unit increase in attractiveness, the model salary increases by 0.335. The p-value is 0.001, which is less than 0.05, indicating that the results are statistically significant.
4. Re-run your analysis including the age and years in the industry variables. Are the assumptions met? Can you run this analysis with the information you have learned so far this semester? (3 points)
ANS: The assumptions for linear regression are not met. Additionally, there is not enough information to run a multiple linear regression analysis with the age and years in the industry variables.
