Please answer all parts of the question. No need to show work.

\fA study of 30 secretaries' yearly salaries (in thousands of dollars) was done. The researchers want to predict salaries from several other variables. The variables considered to be potential predictors of salary are months of service (x, ) , years of education (X2 ) , score on a standardized test (X3 ) , words per minute (wpm) typing speed (X4 ) , and ability to take dictation in words per minute (X5 ) . The accompanying table is a multiple regression with all five variables. Assume that the residual plots show no violations of the conditions for using a linear regression model. Complete parts a through d below. Click the icon to view a multiple regression with all five variables. a) What is the regression equation? y = [+x1 +x2+ x3+x4+ x5 (Type integers or decimals.) b) From this model, what is the predicted salary y (in thousands of dollars) of a secretary with 9 years (108 months) of experience, 8th grade education (8 years of education), a 47 on the standardized test, 63 wpm typing speed, and the ability to take 32 wpm dictation? The predicted salary is thousand dollars. (Round to one decimal place as needed.) c) Test whether the coefficient of words per minute of typing speed (x4 ) is significantly different from zero at a = 0.05. First state the null and alternative hypotheses. Choose the correct hypotheses below. O A. Ho: B3 = 0 HA: B3 # 0 OB. Ho: B4 = 0 HA: B4 # 0 O C. Ho: P5 = 0 HA: B5 # 0The test statistic is. (Type integers or decimals.) The P-value is. (Round to three decimal places as needed.) Draw a conclusion. Choose the correct answer below. O A. Fail to reject the null hypothesis. The coefficient is significantly different from zero. O B. Reject the null hypothesis. The coefficient is significantly different from zero. O C. Reject the null hypothesis. The coefficient is not significantly different from zero. O D. Fail to reject the null hypothesis. The coefficient is not significantly different from zero. d) How might this model be improved? O A. Remove X3, X4, and X5 from the regression equation. O B. Remove x, and x4 from the regression equation. O C. Remove X3 and X5 from the regression equation. O D. Remove x4 and X5 from the regression equation. O E. Remove X5 from the regression equation. OF. Remove x4 from the regression equation