The tables below show the regression output of a multiple regression model relating salary , the beginning salaries in dollars of employees in a given company to the following predictor variables: education, experience and a variable stem indicating whether or not they have an undergraduate degree in a stem field or not. (the units of both education and experience are years.) anova table: response: salary mean sq de sum sq na 2416338 62 9113079 f value na pr(> f) na regression residuals na nα coefficients: estimate std. error t value pr(> t). (intercept) 3226.4 937.7 nα nα education 850.0 na 3.646 na experience 923.4 260.1 na stem na 330.1 1.675

1. fill in the missing values in the above table. (you have filled the nas for the anova table in hw 2.)

2. test whether or not the linear regression model explains significantly more variability in salary than a model with no explanatory variables. what assumptions are you making? specify the null and alternative hypotheses, the test used, and your conclusion using a = 0.05.

3. is there a positive linear relationship between salary and experience , after accounting for the effect of the variables stem and education ? specify the null and alternative hypotheses, the test used, and your conclusion using a = 0.05. (hint: one-sided test)

4. what salary interval would you forecast for an electrical engineer with 10 years of education and 5 years working in a related field? use a= 0.05.

5. what salary interval would you forecast, on average, for english majors with 10 years of education and 6 years in a related field? use a = 0.05.