In the following regression Price = 119.2 + 0.485BDR+23.4Bath +0.156Hsize + 0.002Lsize +0.09 (23.9) (2.61) (8.94) (0.011) (0.00048) (0.311) (10.5) The F-statistic for omitting BDR and Age from the regression is F-statistic=0.08, which has a p-value=0.98. Are the coefficients on BDR and Age statistically significantly different from 0 at 5% level? O Yes, significantly different from 0 since the p-value>0.05. O No, not significantly different from 0 since the p-value>0.05. We don't know O depends on sample size
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- Conduct a regression analysis in Excel using the following data: X Y 12 40 23 50 40 59 33 58 18 45 a) What is the value of b0? Include 1 decimal place in your answer. b) What is the value of b1? Include 2 decimal places in your answer.In exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.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 (??).
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- You estimated a regression with the following output. Source | SS df MS Number of obs = 472-------------+---------------------------------- F(1, 470) > 99999.00 Model | 2.2728e+09 1 2.2728e+09 Prob > F = 0.0000 Residual | 4246681.85 470 9035.4933 R-squared = 0.9981-------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.2771e+09 471 4834590.83 Root MSE = 95.055------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- X | 29.84419 .0595046 501.54 0.000 29.72726 29.96112 _cons | 88.27799 7.592427 11.63 0.000 73.35868 103.1973------------------------------------------------------------------------------…Given the estimated multiple regression equation ŷ = 6 + 5x1 + 4x2 + 7x3 + 8x4 what is the predicted value of Y in each case? a. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 b. x1 = 23, x2 = 18, x3 = 10, and x4 = 11 c. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 d. x1 = -10, x2 = 13, x3 = -8, and x4 = -16(10+05)The following data were collected on the height (inches) and weight (pounds) of women swimmers.Height6870646566 Weight132110106115128 a. Develop the estimated regression equation by computing the values of b0 and b1.