Suppose that we obtained an estimated equation for the regression of weekly sales of palm pilots and the price charged during the week. Interpret the constants b0 for the product brand manager.
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Suppose that we obtained an estimated equation for the regression of weekly sales of palm pilots and the price charged during the week. Interpret the constants b0 for the product brand manager.
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- Consider the regression model Yi = β0 + β1X1i + β2X2i + ui. Transform the regression so that you can use a t-statistic to testa) β1=β2.b) β1+aβ2=0.c) β1 + β2 = 1.Consider the regression model Yi = β0 + β1Xi + ui.a. Suppose you know that β0 = 0. Derive a formula for the least squaresestimator of β1.b. Suppose you know that β0 = 4. Derive a formula for the least squaresestimator of β1?Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.
- Suppose you have the following data: X Y 3 9 7 45 5 25 2 8 Using OLS methods: estimate the following regression model: Sq root of Yi = Beta0 + Beta1(Xi) + errorInterpret the coefficients in the regression modelAn OLS regression should be used when the independent variable is nominal. A. True B. False
- You estimated a regression with the following output. Source | SS df MS Number of obs = 223 -------------+---------------------------------- F(1, 221) = 17592.99 Model | 182392130 1 182392130 Prob > F = 0.0000 Residual | 2291176.96 221 10367.3166 R-squared = 0.9876 -------------+---------------------------------- Adj R-squared = 0.9875 Total | 184683307 222 831906.786 Root MSE = 101.82 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.97037 .0902481 132.64 0.000 11.79252 12.14823 _cons | 74.40159 10.96696 6.78 0.000 52.78839 96.01479…You have been presented with the following data and asked to fit statistical demand functions:PERIOD QUANTITY PRICE INCOME ADVErtising1 120 8 10 3 2 165 4 22 7 3 120 7 20 5 4 165 3 20 8 5 180 4 30 8 6 90 10 19 6 7 150 4 18 10.2 8 190 1.6 25 9.3 9 160 5 30 8 10 200 2 35 9.5Linear Relationshipa. Use any multiple regression packages (eg. SPSS package) to estimate a linear relationship between the dependent variable and the independent variables. b. Is the estimated…calculate slope coefficient for a regression of Y on X calculate the constant of a regression of Y on X calculate the residual for the first observation in the table
- Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customerYou estimated the following regression. What value would you predict for Y, if X = 46? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 452 -------------+---------------------------------- F(1, 450) > 99999.00 Model | 451909533 1 451909533 Prob > F = 0.0000 Residual | 1435457.34 450 3189.9052 R-squared = 0.9968 -------------+---------------------------------- Adj R-squared = 0.9968 Total | 453344990 451 1005199.53 Root MSE = 56.479 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 12.27466 .0326116 376.39 0.000 12.21057 12.33875 _cons | 69.67934 3.949949 17.64 0.000…The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?