Suppose that there are six x-variables in a model to predict y. The sample size is n = 100. SSTO ...
Suppose that there are six x-variables in a model to predict y. The sample size is n = 100. SSTO = 5000 and SSE = 2250. (a) Calculate R2adj. (Remember that p is the number of regression parameters including the intercept!)
(b) Calculate the AICp and BICp values for this model.
(c) Suppose that the six x-variables in the model were selected from a set of 10 possible x-variables and that the SSE for a model that contains all 10 variables is SSE = 1850. Calculate the value of Cp for the model with five x-variables and indicate whether this value is a desirable value of the Cp statistic.
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Suppose that there are six x-variables in a model to predict y. The sample size is n = 100. SSTO ...
Suppose four x-variables are candidates to be in a model for predicting y. The sample size is n -...
Suppose four x-variables are candidates to be in a model for predicting y. The sample size is n - 50 and SST。= 884.8. SSE values for all possible models (which all include an intercept term) are given in the table below, SSEModelSSEModel 452.2 X1.X2297.9Xi.X2,X3 SSE 261.6 262.2 297.6 273.2 Жу 279.5 | Х, X4 | 370.7 | XiXyX 389.3X2.X3262.5XiX3.X4 X2X4 | 309.3 | X1.X2X3Xi | 26 1.6 X3.X4273.3 (a) Complete the following Best Subsets table. VarsR-SqR-Sq (adj)Mallows Cp 1 2...
Suppose that you estimate a regression model with a sample size of 92 observations and 10...
Suppose that you estimate a regression model with a sample size of 92 observations and 10 explanatory variables, including the intercept, using ordinary least squares and the residual sum of squares from this estimated model is 22. You then conduct a Ramsey’s RESET on this model and the residual sum of squares from the Ramsey regression is 20. The test statistic associated with this Ramsey’s RESET is ______, and you can conclude at the 5-percent level of significance that _________________....
Question 3 3 pts Suppose that you estimate a regression model with a sample size of...
Question 3 3 pts Suppose that you estimate a regression model with a sample size of 112 observations and 10 explanatory variables, including the intercept, using ordinary least squares and the residual sum of squares from this estimated model is 22. You then conduct a Ramsey's RESET on this model and the residual sum of squares from the Ramsey regression is 20. The test statistic associated with this Ramsey's RESET is _, and you can conclude at the 5-percent level...
Question 3 3 pts Suppose that you estimate a regression model with a sample size of...
Question 3 3 pts Suppose that you estimate a regression model with a sample size of 112 observations and 10 explanatory variables, including the intercept, using ordinary least squares and the residual sum of squares from this estimated model is 22. You then conduct a Ramsey's RESET on this model and the residual sum of squares from the Ramsey regression is 20. The test statistic associated with this Ramsey's RESET is and you can conclude at the 5- percent level...
Suppose our sample data consists of the six (x,y) pairs {(2,5), (4,6), (5,5), (7,10), (8,9), (10,13)}....
Suppose our sample data consists of the six (x,y) pairs {(2,5), (4,6), (5,5), (7,10), (8,9), (10,13)}. a. Find SST. b. Determine the fitted regression equation for this data, and use it to predict y for x=2, 4, 5, 7, 8, and 10. c. Find SSE. d. Use the results from part (a) and (c) to calculate the coefficient of determination.
Using 3 variables and 13 observations, we are going to predict 'y. According to 0.05 significant...
Using 3 variables and 13 observations, we are going to predict 'y. According to 0.05 significant level, present stepwise regression and find the value of R square Variables SSR (regression sum of squares) 1500 SSE (error sum of squares) 1000 X X₂, X2 xz, xz X2, X₃ X2, X2, X3 SST (total sum of squares) 2500 2500 2500 2500 2500 2500 2500 2500 1400 1300 1300 2250 2400 2435 2450 1100 1200 250 100 65 50
2) Suppose the regression model y = B0 + B1x1 + B2x2 + B3x3 + B4x1x2...
2) Suppose the regression model y = B0 + B1x1 + B2x2 + B3x3 + B4x1x2 + B5x1x3 + B6x2x3 was fit to n = 27 data points with SSE = 2000.0. a) Set up the null and alternative hypotheses for testing whether the interaction terms are significant. b) Give the reduced model necessary to test the significance of the interaction terms. c) The reduced model resulted in SSE = 2800. Calculate the value of the test statistic appropriate for...
Suppose we have the following values for variables X (independent) and Y (dependent) _ _ Σ(X...
Suppose we have the following values for variables X (independent) and Y (dependent) _ _ Σ(X – X)(Y – Y) = -630 _ Σ(X – X)2 = 168 SSE = 1225 SSR = 2362.5 If n = 8, what is the value of the standard error for the simple regression model (round to three decimals)?
2) Suppose the regression model y = B0 + B1x1 + B2x2 + B3x3 + B4x1x2 + B5x1x3 + B6x2x3 was fit to n = 27 data points with SSE = 2000.0. a) Set up the null and alternative hypotheses for testing whethe...
2) Suppose the regression model y = B0 + B1x1 + B2x2 + B3x3 + B4x1x2 + B5x1x3 + B6x2x3 was fit to n = 27 data points with SSE = 2000.0. a) Set up the null and alternative hypotheses for testing whether the interaction terms are significant. b) Give the reduced model necessary to test the significance of the interaction terms. c) The reduced model resulted in SSE = 2800. Calculate the value of the test statistic appropriate for...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo +...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
Suppose four x-variables are candidates to be in a model for predicting y. The sample size is n - 50 and SST。= 884.8. SSE values for all possible models (which all include an intercept term) are given in the table below, SSEModelSSEModel 452.2 X1.X2297.9Xi.X2,X3 SSE 261.6 262.2 297.6 273.2 Жу 279.5 | Х, X4 | 370.7 | XiXyX 389.3X2.X3262.5XiX3.X4 X2X4 | 309.3 | X1.X2X3Xi | 26 1.6 X3.X4273.3 (a) Complete the following Best Subsets table. VarsR-SqR-Sq (adj)Mallows Cp 1 2...
Question 3 3 pts Suppose that you estimate a regression model with a sample size of 112 observations and 10 explanatory variables, including the intercept, using ordinary least squares and the residual sum of squares from this estimated model is 22. You then conduct a Ramsey's RESET on this model and the residual sum of squares from the Ramsey regression is 20. The test statistic associated with this Ramsey's RESET is _, and you can conclude at the 5-percent level...
Question 3 3 pts Suppose that you estimate a regression model with a sample size of 112 observations and 10 explanatory variables, including the intercept, using ordinary least squares and the residual sum of squares from this estimated model is 22. You then conduct a Ramsey's RESET on this model and the residual sum of squares from the Ramsey regression is 20. The test statistic associated with this Ramsey's RESET is and you can conclude at the 5- percent level...
Using 3 variables and 13 observations, we are going to predict 'y. According to 0.05 significant level, present stepwise regression and find the value of R square Variables SSR (regression sum of squares) 1500 SSE (error sum of squares) 1000 X X₂, X2 xz, xz X2, X₃ X2, X2, X3 SST (total sum of squares) 2500 2500 2500 2500 2500 2500 2500 2500 1400 1300 1300 2250 2400 2435 2450 1100 1200 250 100 65 50
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
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