Question

Problem: You are interested in factors that predict the salaries of catwalk models. You collected data from 231 models (attached SPSS data file: supermodel_1.sav). For each model, you asked: salary per day in dollars (on days when working (variable: salary_2), age (variable: age), and how many years they have worked as a model (variable: years). You also got a panel of experts from various modeling agencies to rate the attractiveness of each model as a percentage, with 100% being perfectly attractive (variable: beauty). Using that data . . .

1. Conduct a scatterplot of beauty and salary; paste the scatter plot here. How would you describe the relationship between attractiveness and salary?

2.Are there any outliers in the data that appear in the scatterplot? If so, how might you interpret them, in the context of your study?

3.How would you check the assumptions of homoscedasticity and linearity? Also, is the assumption of homoscedasticity violated in the plot below?

4.Check for multicollinearity of the independent variables using correlation. Paste the correlation table here. How would you describe the presence of multicollinearity among the independent variables? If any multicollinearity exists, what do you think should be done about it, in terms of this analysis?

Scatterplot Dependent Variable: salary 2 do 2 Regression Standardized Residual

5.Check the assumption of normality using Komogorov-Smirnoff one-sample test for normality. Which variables (if any) appear normally distributed? Which variables (if any) do not? Below you are asked to conduct a multiple regression. Paste the outcome into this Word document (be sure it is readable). Use the output to answer questions 7 - 10. (Note: if you conduct the regression incorrectly, causing wholly incorrect output, there will be a 5 point deduction [your highest possible grade will be 45/50 = 90%]. The instructor will then use the incorrect output you attached to determine if, although the output itself was wrong, you used the output correctly to answer the questions.)

6. Conduct a multiple regression of age, number of years as a model, and attractiveness on the dependent variable salary_2.

7. What is the value of R2? How large is that, relatively, and what does it mean?

8. Is the regression significant? How do you know?

9. Which coefficients are significant? Complete this sentence: Every increase of an average of 1 year in the number of years that a woman is a model is associated with _____.

10. Overall, what are your conclusions about model salary, based on the sum total of this data?

Answer 1

Could not find the data to do regression, scatterplot, kindly update the link for 'supermodel_1.sav'.