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- Interpret the slope (m) and R2 values given in the equation of linear regression for both costs and benefits.A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?Determine the mode choice (personal vehicle or bus system) for the following regression model: Utility Function: Umode – (8.333 x 10-4)*(Access time in sec) – (6.667 x 10-4)*(Wait Time in sec) – (5.00 x 10-4)*(Riding time in sec) – (1.40)*(Cost, $) PARAMETER PERSONAL VEHICLE CITY BUS SYSTEM MODE CONSTANT -0.01 -0.07 ACCESS TIME (SECS) 300 600 WAITING TIME (SECS) 0 900 RIDING TIME (SECS) 1,500 6,000 COST (DOLLARS) $1.50 $1.00
- No written by hand solution Consider the following data: x⎯⎯x¯ = 20, sx = 2, y⎯⎯y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation? Multiple Choice yˆ = −13 − 0.40x yˆ= −13 + 0.40x yˆ = 3 − 0.40x yˆ = 3 + 0.40x18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?Question 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
- The regression equation to predict sales based on temperature is: Predicted sales = -2419.01+ 98.02 (temperature). A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.The demand function for Newton’s Donuts has been estimated as follows:Qx = -14 – 54Px + 45Py + 0.62Ax where Qx represents thousands of donuts; Px is the price per donut; Py is the average price per donut of other brands of donuts; and Ax represents thousands of dollars spent on advertising Newton’s Donuts. The current values of the independent variables are Ax=120, Px=0.95, and Py=0.64.Show all of your calculations and processes. Describe your answer for each question in complete sentences, whenever it is necessary. Calculate the price elasticity of demand for Newton’s Donuts and describe what it means. Describe your answer and show your calculations. Derive an expression for the inverse demand curve for Newton’s Donuts. Describe your answer and show your calculations. If the cost of producing Newton’s Donuts is constant at $0.15 per donut, should they reduce the price and thereafter, sell more donuts (assuming profit maximization is the company’s goal)? Should Newton’s Donuts spend…A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________
- The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 83.7 + 2.23x1 + 1.60x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.445. (a)Compute and interpret R2 and Ra2.(Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??) . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??).The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ŷ = 21 + 13x1 + 9x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 14,630. (a)For the estimated regression equation given, compute R2. R2 = ?? (b)Compute Ra2. (Round your answer to two decimal places.) Ra2 = ?? (c)Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.) The adjusted coefficient of determination shows that (??) % of the variability has been explained by the two independent variables; thus, we conclude that the model does explain a large amount of variability.Consider the simple regression model: y=0.56+1.56x+u Using this and assuming the estimated Var(y)=0.64 and the estimated Var(x)=3.07, what is the estimated Var(x+y)?