OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO units
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- You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…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?Given the regression equationY = -50 + 12Xa. What is the change in Y when X changes by +3?b. What is the change in Y when X changes by -4?c. What is the predicted value of Y when X = 12?d. What is the predicted value of Y when X = 23?e. Does this equation prove that a change in X causes a change in Y?
- 18. 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?You estimated the following regression. What value would you predict for Y, if X = 90? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 231 -------------+---------------------------------- F(1, 229) = 67069.23 Model | 2.6971e+09 1 2.6971e+09 Prob > F = 0.0000 Residual | 9209021.29 229 40214.0668 R-squared = 0.9966 -------------+---------------------------------- Adj R-squared = 0.9966 Total | 2.7063e+09 230 11766675.6 Root MSE = 200.53 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.88308 .173309 258.98 0.000 44.5416 45.22457 _cons | 21.1834 18.22441 1.16 0.246…You estimated the following regression. Source | SS df MS Number of obs = 117 -------------+---------------------------------- F(1, 115) = 0.06 Model | 2523.61281 1 2523.61281 Prob > F = 0.8003 Residual | 4513680.09 115 39249.3921 R-squared = 0.0006 -------------+---------------------------------- Adj R-squared = -0.0081 Total | 4516203.7 116 38932.7906 Root MSE = 198.11 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 40.18036 158.4597 0.25 0.800 -273.6979 354.0586 _cons | -556.9531 11761.12 -0.05 0.962 -23853.48 22739.57 ------------------------------------------------------------------------------…
- Given the regression equationY = 43 + 10Xa. What is the change in Y when X changes by +8?b. What is the change in Y when X changes by -6?c. What is the predicted value of Y when X = 11? d. What is the predicted value of Y when X = 29? e. Does this equation prove that a change in X causes a change in Y?DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.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____________
- DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 7. If the price of asphalt (PR) decreases by 15, what will happen to the estimated quantity of cement demanded? Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.An attempt was made to evaluate the inflation rate as a predictor of the spot rate in the German treasury bill market. For a sample of 79 quarterly observations, the estimated linear regressionŷ = 0.0027 + 0.7916xwas obtained, wherey = actual change in the spot ratex = change in the spot rate predicted by the inflation rateThe coefficient of determination was 0.097, and the estimated standard deviation of the estimator of the slope of the population regression line was 0.2759.a. Interpret the slope of the estimated regression line.b. Interpret the coefficient of determination.c. Test the null hypothesis that the slope of the population regression line is 0 against the alternative that the true slope is positive, and interpret your result.d. Test, against a two-sided alternative, the null hypothesis that the slope of the population regression line is 1, and interpret your result.Historical demand for Peeps is as displayed in the table. Month Demand January 11 February 18 March 31 April 39 May 44 June 53 July 67 August 82 September 96 Develop forecasts from June through October using these techniques: Holt's method with alpha=0.2 and beta=0.1. For Holt's model, the level and trend for May are assumed to be 44 and 12. Judge which forecast method is the best based on MAD.