Choose all correct answers regarding the multiple linear regression model: y = Bo+ B₁x₁ + B2X2 + + BkXk + U. (a) If you add a relevant variable XK+1, but corr(XK, Xk+1) is high, then you must not include xk+1 in the regression equation. (b) Adding a variable XK+₁ may lower the R² value. (c) Adding all the variables in the dataset does not imply that E[u | X1, X2, ... Xk] = 0. (d) We can estimate the value of ẞ₁ by minimizing Σ(û¡)².

Refer to the Stata output from an analysis of data on Store sales. The data include nominal store-level sales (measured in for the month of June 1996 from 844 stores of a national retail chain. The other variables are: A categorical string variable loctype which indicates the type of store location and takes one of three values: "mall" = 1 if the store is located in a shopping mall (indoor or outdoor) "street" = 1 if the store is located on a street "strip" = 1 if the store is located on a strip or highway ⚫ medhhinc = the median household income of the city where the store is located, measured in $1,000 • empl = the total number of employees working in the store in June 1996 . sqft = the store's size, measured in 1,000 square feet A manager wants to know how average monthly sales varies with the characteristics of a store and its location and uses Stata to perform the regression analysis shown. gen sqftXmedhhinc =sqft*medhhinc . reg sales mall street medhhinc empl sqft sqftXmedhhinc,…

We would like to examine whether there is a differential effect of study hours on test scores between students whose first language is English and those whose first language is not English by adding an interaction term. gen hoursEng = study_hours *English . reg test_score study_hours English hoursEng Source SS df MS Number of obs 876 Model Residual 136565.839 18952.9115 3 45521.9464 872 21.7349902 F(3, 872) Prob F 2094.41 0.0000 R-squared 0.8781 Total 155518.751 875 177.735715 Adj R-squared = Root MSE 0.8777 = 4.6621 test_score Coef. Std. Err. t P>|t| [95% Conf. Interval] study_hours English hoursEng 1.82049 .0369748 49.24 0.000 8.318905 .8108202 10.26 0.000 .101876 cons 43.52037 .0516819 .5767457 1.97 75.46 0.049 0.000 1.74792 6.727517 .0004404 42.3884 1.89306 9.910292 .2033115 44.65234 (a): What is the predicted test score for an individual who studied two hours and whose first language is not English? (Round to two decimal places) (b): Choose all correct answers (a) The differential…

Determine whether the following statement is true or false. Suppose you reject the null hypothesis: Ho: ẞ₁ = ẞ₂ = 0. Then, both ẞ₁ = 0 and ẞ2 ± 0. Explain your answer in two sentences or fewer.

Choose one correct answer: The ---A----- dataset includes 100,000 students from 512 schools observed over 12 months. To control differences between schools and months, we should add a total of What are A and B? (a) A: Time series, B: 524 (b) A: Time series, B: 522 (c) A: Panel, B: 524 (d) A: Panel, B: 522 dummies.

Refer to the Stata output from an analysis of data on Store sales. The data include nominal store-level sales (measured in for the month of June 1996 from 844 stores of a national retail chain. The other variables are: A categorical string variable loctype which indicates the type of store location and takes one of three values: "mall" = 1 if the store is located in a shopping mall (indoor or outdoor) "street" = 1 if the store is located on a street "strip" = 1 if the store is located on a strip or highway ⚫ medhhinc = the median household income of the city where the store is located, measured in $1,000 • empl = the total number of employees working in the store in June 1996 . sqft = the store's size, measured in 1,000 square feet A manager wants to know how average monthly sales varies with the characteristics of a store and its location and uses Stata to perform the regression analysis shown. gen sqftXmedhhinc =sqft*medhhinc . reg sales mall street medhhinc empl sqft sqftXmedhhinc,…

Essay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 age

The regression below examines the relationship between CEO salary (measured in thousands of dollars) and grad, whether the CEO has a graduate degree (an indicator variable), their years of tenure as CEO, and the profits of the company (in millions of dollars). Source SS df MS Number of obs 177 F(3, 173) 12.56 Model Residual 10867017.6 3 3622339.19 Prob > F 0.0000 173 288433.221 R-squared 0.1788 Adj R-squared 0.1646 Total 176 345261.163 Root MSE 537.06 salary Coef. Std. Err. t P>|t| [95% Conf. Interval] grad tenure -31.47414 81.70146 -0.39 0.701 129.7859 12.22944 2.15 0.033 .9951956 23.46368 profits .5807776 .1005807 5.77 0.000 .3822544 .7793009 cons 664.5934 79.02213 8.41 0.000 508.6218 820.565 (a): Perform the hypothesis that Ho: ẞprofits 0.7. What is the t statistic? (Round to one decimal place) (b): Let tcritical 2 given a = 0.05. Is profits statistically different from 0.7 at 5% significance level?

FILL IN THE BLANK, FILL IN $(A)