Suppose you run a simple regression with Incidents as the dependent variable and New Employees as...
Suppose you run a simple regression with Incidents as the dependent variable and New Employees as the independent variable. If the R-squared value New Employees and incidents is 0.030 (and Adjusted R-Squared is 0.029) what does this represent?
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Suppose you run a simple regression with Incidents as the
dependent variable and New Employees as...
For two valid regression models which have same dependent variable, if regression model A and regression...
For two valid regression models which have same dependent variable, if regression model A and regression model B have the followings, Regression A: Residual Standard error = 30.33, Multiple R squared = 0.764, Adjusted R squared = 0.698 Regression B: Residual Standard error = 40.53, Multiple R squared = 0.784, Adjusted R squared = 0.658 Then which one is the correct one? Choose all applied. a. Model A is better than B since Model A has smaller residual standard error...
Dummy Variable Regression: Choose any metric variable as the dependent variable (you can use the ...
Dummy Variable Regression: Choose any metric variable as the
dependent variable (you can use the same one that you used in Part
A) and choose gender as an independent variable. Also choose one
more metric variable as an additional independent variable. Once
again, however, you must sort through the metric independent
variables until you find one that, along with gender, produces a
significant F-calc. Use alpha = .05 here as well. You
only need to report the model that produced...
QUESTION 1 The Simple Linear Regression is fit or constructed to predict a dependent variable. True...
QUESTION 1 The Simple Linear Regression is fit or constructed to predict a dependent variable. True False QUESTION 2 The Coefficient of Determination is used to explain in what percent (%) the independent variable is affecting the dependent variable. True False
Interpreting regression results 2. This is the result of a regression where goals is the dependent...
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
1. In regression analysis, the Sum of Squares Total (SST) is a. The total variation of...
1. In regression analysis, the Sum of Squares Total (SST) is a. The total variation of the dependent variable b. The total variation of the independent variable c. The variation of the dependent variable that is explained by the regression line d. The variation of the dependent variable that is unexplained by the regression line Question 2 In regression analysis, the Sum of Squares Regression (SSR) is A. The total variation of the dependent variable B. The total variation of the independent variable...
Assignment 2 PART 1. 1. Use the dataset below to run a regression where x1 and...
Assignment 2 PART 1. 1. Use the dataset below to run a regression where x1 and x2 are the independent variables and y is the dependent variable. y x1 x2 2 10 9 88 5 6 19 10 6 18 5 2 30 3 2 What is b1 (the coefficient on x1) for this regression? Round your answer to four decimal places. 2. Use the dataset below to run a regression where x1 and x2 are the independent variables and...
(16 pts) Suppose you have the output from an Excel linear regression. The dependent variable is...
(16 pts) Suppose you have the output from an Excel linear regression. The dependent variable is ntrip, see definitions below Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.534 0.386 0.370 1.414 785 ANOVA df sS MS Regression Residual Total 2 113.5355 56.76777 156.2694 782 284.0761 0.363269 784 397.6116 Standard Coefficients Error tStat P-value Intercept hhsize wrkrcnt 1.500 0.250 0.150 0.049 20.7860.000 0.016 12.857 .000 0.027 5.551 0.000 NAME |Type- ntrip Numeric # of trips made...
(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...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...
Suppose you run a regression in Excel. What is one way to determine if an explanatory...
Suppose you run a regression in Excel. What is one way to determine if an explanatory variable is statistically significant? Compare the t-stat with the coefficient Compare the t-stat with the P-value Check if the coefficent is greater than a critical value. Examine the p-value and/or the t-stat. 2. Suppose you wanted to know if there was a relationship between time spent on the internet and IQ. Which model would make the most sense and what would it look like?...
Dummy Variable Regression: Choose any metric variable as the
dependent variable (you can use the same one that you used in Part
A) and choose gender as an independent variable. Also choose one
more metric variable as an additional independent variable. Once
again, however, you must sort through the metric independent
variables until you find one that, along with gender, produces a
significant F-calc. Use alpha = .05 here as well. You
only need to report the model that produced...
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
(16 pts) Suppose you have the output from an Excel linear regression. The dependent variable is ntrip, see definitions below Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.534 0.386 0.370 1.414 785 ANOVA df sS MS Regression Residual Total 2 113.5355 56.76777 156.2694 782 284.0761 0.363269 784 397.6116 Standard Coefficients Error tStat P-value Intercept hhsize wrkrcnt 1.500 0.250 0.150 0.049 20.7860.000 0.016 12.857 .000 0.027 5.551 0.000 NAME |Type- ntrip Numeric # of trips made...
(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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