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A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515...
A multiple regression analysis produced the following tables: Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075...
A multiple regression analysis produced the following
tables:
Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075 Standard Error 154.5534 2.333548 0.335605 t statistic 3.990108 -1.43058 5.30407 p value 0.000947 0.170675 5.83E-05 Source Regression Residual Total df 2 15 17 SS 121783 61876.68 183659.6 MS 60891.48 4125.112 p value 0.000286 F 14.76117 Using a = 0.01 to test the null hypothesis Ho: B1 = B2 = 0, the critical F value is 8.68 6.36 8.40 O 6.11 O 3.36
A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876...
A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 35.18215 7.721648 762.1533 96.8433 3.007943 1.852483 0.074919 0.363289 0.719218 2.567086 0.016115 2 df Regression 2 Residual 25 27 58567032 12765573 71332605 MS 29283516 57.34861 510622.9 Total Using a-0.10 to test the null hypothesis Ho: b2 0, the critical t value is. ± 1.316 ± 1.314 ± 1.703 ± 1.780 ± 1.708
The following is a partial result of Multiple Regression analysis conducted in Excel. Predictor Coefficients Standard...
The following is a partial result of Multiple Regression analysis conducted in Excel. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.61 2548.99 -0.05 0.157154 x1 4.25 22.25 1.08 0.005682 x2 3.10 17.45 1.87 0.03869 x3 15.18 11.88 1.03 0.00002 Specify the following: Regression Equation: Which independent/predictor variables are statistically significant at α = 0.01 and Why?
Consider the following partial computer output from a simple linear regression analysis. P Predictor Coef SE...
Consider the following partial computer output from a simple linear regression analysis. P Predictor Coef SE Coef T Constant 9.35 0.000 4.8615 0.5201 0.05866 Independent Var -0.34655 S=4862R-Sq. Analysis of Variance MS DF SS F Source 1 34.90 Regression 13 Residual Error 14 11.3240 Total What is the predicted value of ywhen x 9.00?
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef...
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq| Analysis of Variance SS MS Source DF F Regression 1 34.90 Residual Error 13 Total 14 11.3240 Calculate the MSE
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq|...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor)...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor) variables. Provide some important arguments about the fitted multiple regression model. (Give one argument about each of the three main outputs.) [4 marks] SUMMARY OUTPUT Regression Statistics Multiple R 0.9870 R Square Adjusted R Square 0.9741 0.9721 Standard Error 116.2766 Observations 43 ANOVA Significance F df SS MS F Regression 19817036.22 6605678.74 488.58 5.82876E-31 Residual 527289.46 39 13520.24 Total 42 20344325.68 P-value Coefficients Standard...
A simple linear regression (linear regression with only one predictor) analysis was carried out using a...
A simple linear regression (linear regression with only one predictor) analysis was carried out using a sample of 23 observations From the sample data, the following information was obtained: SST = [(y - 3)² = 220.12, SSE= L = [(yi - ġ) = 83.18, Answer the following: EEEEEEEE Complete the Analysis of VAriance (ANOVA) table below. df SS MS F Source Regression (Model) Residual Error Total Regression standard error (root MSE) = 8 = The % of variation in the...
CALCULATOR The following is a partial computer output of a multiple regression analysis of a data set containing 20...
CALCULATOR The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variabl The regression equation is SALEPRIC 1470+0.8145 LANDVAL + 0.8204 IMPROVAL +13.529 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S 79190.48 R-Sq 89.7% R-Sq(ad) =87.8% Analysis of Variance Source DF SS MS Regression 3 2926558914...
The following Regression function has been developed to check the relationship between the dependent variable y...
The following Regression function has been developed to check
the relationship between the dependent variable y and the
independent variable ?1 .
Consider the following Minitab output and answer the
questions.
Regression Equation
?̂ = ? . ? ? + ? . ? ? x1
a) Please fill out the Coefficients table appropriately.
b) Please fill out the ANOVA table appropriately.
c) Suppose that variables ?2 ??? ?3 are added to the above model
and the following regression analysis is...
The following Regression function has been developed to check the relationship between the dependent variable y...
The following Regression function has been developed to check
the relationship between the dependent variable y and the
independent variable ?1 .
Consider the following Minitab output and answer the
questions.
Regression Equation
?̂ = ? . ? ? + ? . ? ? x1
a) Please fill out the Coefficients table appropriately.
b) Please fill out the ANOVA table appropriately.
c) Suppose that variables ?2 ??? ?3 are added to the above model
and the following regression analysis is...
A multiple regression analysis produced the following
tables:
Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075 Standard Error 154.5534 2.333548 0.335605 t statistic 3.990108 -1.43058 5.30407 p value 0.000947 0.170675 5.83E-05 Source Regression Residual Total df 2 15 17 SS 121783 61876.68 183659.6 MS 60891.48 4125.112 p value 0.000286 F 14.76117 Using a = 0.01 to test the null hypothesis Ho: B1 = B2 = 0, the critical F value is 8.68 6.36 8.40 O 6.11 O 3.36
A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 35.18215 7.721648 762.1533 96.8433 3.007943 1.852483 0.074919 0.363289 0.719218 2.567086 0.016115 2 df Regression 2 Residual 25 27 58567032 12765573 71332605 MS 29283516 57.34861 510622.9 Total Using a-0.10 to test the null hypothesis Ho: b2 0, the critical t value is. ± 1.316 ± 1.314 ± 1.703 ± 1.780 ± 1.708
Consider the following partial computer output from a simple linear regression analysis. P Predictor Coef SE Coef T Constant 9.35 0.000 4.8615 0.5201 0.05866 Independent Var -0.34655 S=4862R-Sq. Analysis of Variance MS DF SS F Source 1 34.90 Regression 13 Residual Error 14 11.3240 Total What is the predicted value of ywhen x 9.00?
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq| Analysis of Variance SS MS Source DF F Regression 1 34.90 Residual Error 13 Total 14 11.3240 Calculate the MSE
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq|...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor) variables. Provide some important arguments about the fitted multiple regression model. (Give one argument about each of the three main outputs.) [4 marks] SUMMARY OUTPUT Regression Statistics Multiple R 0.9870 R Square Adjusted R Square 0.9741 0.9721 Standard Error 116.2766 Observations 43 ANOVA Significance F df SS MS F Regression 19817036.22 6605678.74 488.58 5.82876E-31 Residual 527289.46 39 13520.24 Total 42 20344325.68 P-value Coefficients Standard...
A simple linear regression (linear regression with only one predictor) analysis was carried out using a sample of 23 observations From the sample data, the following information was obtained: SST = [(y - 3)² = 220.12, SSE= L = [(yi - ġ) = 83.18, Answer the following: EEEEEEEE Complete the Analysis of VAriance (ANOVA) table below. df SS MS F Source Regression (Model) Residual Error Total Regression standard error (root MSE) = 8 = The % of variation in the...
CALCULATOR The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variabl The regression equation is SALEPRIC 1470+0.8145 LANDVAL + 0.8204 IMPROVAL +13.529 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S 79190.48 R-Sq 89.7% R-Sq(ad) =87.8% Analysis of Variance Source DF SS MS Regression 3 2926558914...
The following Regression function has been developed to check
the relationship between the dependent variable y and the
independent variable ?1 .
Consider the following Minitab output and answer the
questions.
Regression Equation
?̂ = ? . ? ? + ? . ? ? x1
a) Please fill out the Coefficients table appropriately.
b) Please fill out the ANOVA table appropriately.
c) Suppose that variables ?2 ??? ?3 are added to the above model
and the following regression analysis is...
The following Regression function has been developed to check
the relationship between the dependent variable y and the
independent variable ?1 .
Consider the following Minitab output and answer the
questions.
Regression Equation
?̂ = ? . ? ? + ? . ? ? x1
a) Please fill out the Coefficients table appropriately.
b) Please fill out the ANOVA table appropriately.
c) Suppose that variables ?2 ??? ?3 are added to the above model
and the following regression analysis is...
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