A multiple regression analysis produced the following tables:
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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 624.5369 8.569122 4.736515...
A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515 Standard Error 78.49712 1.652255 0.699194 t statistic 7.956176 5.186319 6.774248 p value 6.88E-06 0.000301 3.06E-05 Source Regression Residual Total df 2 11 13 SS 1660914 156637.5 1817552 MS 830457.1 14239.77 F 58.31956 p value 1.4E-06 For x1= 30 and x2 = 100, the predicted value of y is 753.77 O 1,173.00 O 1,355.26 615.13 6153.13
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?
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...
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...
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression...
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12323.56 12323.56 90.0481 Residual 8 1094.842 136.8550 Total 9 13418.4 Estimate the value of Ŷ when X = 4.
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|...
5. Summary of regression between a dependent variable y and two independent variables X, and x2...
5. Summary of regression between a dependent variable y and two independent variables X, and x2 is as follows. Please complete the table: SUMMARY OUTPUT Regression Statistics Multiple R 0.9620 R Square R2E? Adjusted R Square 0.9043 Standard Error 12.7096 Observations 10 ANOVA F Significance F F=? Overall p-value=? Regression Residual Total 2 df of SSE MS MSR=? MSE? 14052.1550 1130.7450 SSTE? MSE? 9 Coefficients -18.3683 Standard Error 17.9715 t Stat -1.0221 Intercept ty=? 2.0102 4.7378 0.2471 0.9484 P-value 0.3408...
The following table is the output of multiple linear regression analysis. a. Use the table to...
The following table is the output of multiple linear regression
analysis.
a. Use the table to report the F statistic. What is its degree of
freedom? What is the number of observations.
b. Find the p-value related to F on the computer output and report
its value. Using the p-value, test the significance of the
regression model at the .10, .05, .01, and .001 levels of
significance. What do you conclude?
Please show work and explain each step!
df ANOVA...
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use...
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use for the production department. Since your manager is new and does not understand the regression analysis tables, you will need to explain why one set of statistics is better than the other and why you have chosen the better driver. Manufacturing Direct Labor Hours Regression Statistics Multiple R 0.799304258 R Square 0.638887297 Adjusted R Square 0.602776026 Standard Error...
A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515 Standard Error 78.49712 1.652255 0.699194 t statistic 7.956176 5.186319 6.774248 p value 6.88E-06 0.000301 3.06E-05 Source Regression Residual Total df 2 11 13 SS 1660914 156637.5 1817552 MS 830457.1 14239.77 F 58.31956 p value 1.4E-06 For x1= 30 and x2 = 100, the predicted value of y is 753.77 O 1,173.00 O 1,355.26 615.13 6153.13
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
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...
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...
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|...
5. Summary of regression between a dependent variable y and two independent variables X, and x2 is as follows. Please complete the table: SUMMARY OUTPUT Regression Statistics Multiple R 0.9620 R Square R2E? Adjusted R Square 0.9043 Standard Error 12.7096 Observations 10 ANOVA F Significance F F=? Overall p-value=? Regression Residual Total 2 df of SSE MS MSR=? MSE? 14052.1550 1130.7450 SSTE? MSE? 9 Coefficients -18.3683 Standard Error 17.9715 t Stat -1.0221 Intercept ty=? 2.0102 4.7378 0.2471 0.9484 P-value 0.3408...
The following table is the output of multiple linear regression
analysis.
a. Use the table to report the F statistic. What is its degree of
freedom? What is the number of observations.
b. Find the p-value related to F on the computer output and report
its value. Using the p-value, test the significance of the
regression model at the .10, .05, .01, and .001 levels of
significance. What do you conclude?
Please show work and explain each step!
df ANOVA...
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