print out of its regression results: Beer = Bo + BIEDUC + BAAGE + BAAGE + BAGENDER + BRACE+ BIGENDER'RACE+ E, where Beer is monthly beer consumption (ounces), EDUC is years of education. We have 2 qualitative variables gender and race. Gender takes 2 values, GEN=1 if the person is male and GEN=0 for females. The variable race also takes 2 values, RACE=1 if the person is white and RACE=0 if the person is not white SUMMARY OUTPUT Regression Statistics R Square Adjusted R Square ??? 0.4684 Standard Error ??? Observations 40 ANOVA df MS 319.3 Regression Residual Total 64.8 8.43 ??? ??? ??? ??? ??? 597.5 Coefficients Standard Error 107.397 Intercept EDUC -150.254 -16.7755 8.4579 AGE 75.45905 37.3261 AGE -1.72456 238.9424 0.5397 GEN 81.6054 RACE 123.7404 103.1804 GEN. RACE 76.4308 51.0670 a. Calculate the missing numbers (???). b.Interpret the parameter of RACE (123.74). c. Is the parameter of RACE (B6) significant? Use a = 0.05 and interpret your results. d. Interpret the adjusted R. e.Conduct a test of Global Usefulness of the model (test of goodness of fit) using a=0.05. Interpret the results of the test. Make sure to specify the null and the alternative hypotheses, test statistics and its distribution, and the critical value, and interpret the results.

The line of best fit through a set of data is y = – 27.749 + 1.474x According to this equation, what is the predicted value of the dependent variable when the independent variable has value 30? y = Round to 1 decimal place.

Let price denote a price index for the goods sold by a restaurant, advert the amount spent on advertising, sales the sales for the restaurant, and consider the following two regressions First regression: sales = B1 + B2price + B3price? + B4advert + ßsadvert? + e, Second regression: sales = B1 + B2price + B3price? + e We estimate both regressions using a sample of 105 observations. The sum of square residuals (E ê) from the first regression equals 50, while the sum of square Li=1 residuals from the second regression equals 70. Suppose we are interested in testing the null hypothesis that expected sales do not depend on advertising. What is the F- statistic for this null hypothesis? Recall the F-statistic is given by ((SSER - SSEU)/J)/(SSEy/(n – K)). O a. -15 O b. 42 Oc. 21 O d. 20 O e. All other options are incorrect.

Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 171 cm tall.   \begin{array}{c|ccccccc} \mbox{Height} & 174 & 166 & 176 & 176 & 178 & 172 & 172 \cr \hline \mbox{Weight} & 55 & 47 & 55 & 56 & 58 & 52 & 53 \cr \end{array} The regression equation is \hat{y} =   +   x . The best predicted weight of a supermodel who is 171 cm tall is