Question

In conducting a multiple linear regression analysis, an R2 value of 0.46 is obtained. An extra...

In conducting a multiple linear regression analysis, an R2 value of 0.46 is obtained. An extra variable is added and R2 improves to 0.52. The analyst conducting the regression analysis concludes that this is a meaningful increase in R2 and determines that the latter model is an appropriate model to be used. Is this decision justified?

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
The R2 statistic can decrease when a new regressor variable is added to a multiple linear...
The R2 statistic can decrease when a new regressor variable is added to a multiple linear regression model. A) True B) False The adjusted R2 statistic can decrease when a new regressor variable is added to a multiple linear regression model. A) True B) False Cook’s distance is a measure of influence on the regression model for the individual observation in a sample. A) True B) False
Suppose we have a regression model with 3 independent variables and an R2 value of 0.65...
Suppose we have a regression model with 3 independent variables and an R2 value of 0.65 and an adjusted R2 value of 0.61. When we add a fourth independent variable, the new R2 value is 0.73 and the new adjusted R2 value is 0.54. What can we conclude about this newly added independent variable? Group of answer choices The new independent variable improves our model because R-squared increased The new independent variable improves our model because the adjusted R-squared value...
The following results were obtained as part of a multiple regression analysis involving 3 independent variables:...
The following results were obtained as part of a multiple regression analysis involving 3 independent variables: SSR = 11440   SSYY = 16781       and n = 28 What is the value of the calculated F statistic? What is the critical value for the model test if alpha = 0.05? What is the value of the coefficient of determination? What is the value of the standard error of the estimate? What is the value of adjusted R2? What are the total number...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
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 = 82.5 + 2.26x1 + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST = 25.3 and SSR = 23.415. (a)Compute and interpret R2 and Ra2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
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 = 83.1 + 2.23x1 + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.395. (a) Compute and interpret R2 and Ra2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
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 = 83.8 + 2.26x1 + 1.50x2. The computer solution, based on a sample of eight weeks, provided SST = 25.8 and SSR = 23.385. (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...
The data file Demographics was used in a simple linear regression model where Unemployment Rate is...
The data file Demographics was used in a simple linear regression model where Unemployment Rate is the response variable and Cost of Living is the explanatory variable. You may refer to the previous two questions for the regression model if you wish. The anova function in R was used to obtain the breakdown of the sums of squares for the regression model. This is shown below: > anova(myreg)Analysis of Variance Table Response: Unemployment Df Sum Sq Mean Sq F value...
One of your last homework problems related to a bivariate linear regression analysis on a sample...
One of your last homework problems related to a bivariate linear regression analysis on a sample of 107 nations. It used the percent of the adult population that was literate in a nation to predict life expectancy for people born now in that nation. In other words, the study investigated whether nations with higher rates of literacy have longer (or perhaps shorter) life expectancy. The value of R2 for the regression analysis was 0.749. Data on the same 107 nations...
Multiple linear regression results: Dependent Variable: Cost Independent Variable(s): Summated Rating Cost = -43.111788 + 1.468875...
Multiple linear regression results: Dependent Variable: Cost Independent Variable(s): Summated Rating Cost = -43.111788 + 1.468875 Summated Rating Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept -43.111788 10.56402 ≠ 0 98 -4.0810021 <0.0001 Summated Rating 1.468875 0.17012937 ≠ 0 98 8.633871 <0.0001 Analysis of variance table for multiple regression model: Source DF SS MS F-stat P-value Model 1 8126.7714 8126.7714 74.543729 <0.0001 Error 98 10683.979 109.02019 Total 99 18810.75 Summary of fit: Root MSE: 10.441273 R-squared: 0.432...
1.A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1...
1.A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $255,000.00 $200,000.00...