Question

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 decreases

The new independent variable may be unnecessary because the adjusted R-squared value decreases

None of the above

Math   Statistics-And-Probability   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
1. If we add k indicator variables to a regression model for a categorical variable with...
1. If we add k indicator variables to a regression model for a categorical variable with k levels, the regression tool will return one coefficient estimate of 0.00 because: a. The k variables are not independent b. The k variables are independent 2. If we add up to 3rd order polynomial terms to a regression model (e.g., x, x^2 and x^3) it will allow the relationship between X and Y to change direction 3 times. a. True b. False 3....
With a multi-variable linear regression model how can we decide which independent variables to remove from...
With a multi-variable linear regression model how can we decide which independent variables to remove from the model?
(2) The following are regression results for the model with five independent variables. PREDICTOR                          
(2) The following are regression results for the model with five independent variables. PREDICTOR                              COEF             STDEV                        Constant                                     -19.672           5.422 OUTLETS                                   -0.00063       0.00264 CARS                                            1.7399          0.5530 INCOME                                      0.4099          0.04385 AGE                                              2.0357          0.8779 BOSSES                                     -0.0344          0.1880 SSRegression = 1593. 81                                            SS (total) = 1602.89 b.    What percent of the variation in sales does the regression equation explain? (5 pts)        99.43% c.    Conduct a global test to determine if ANY of the independent variables are linearly related to annual sales.  Use alpha = .01. (16 pts) d.    What variables would you consider eliminating from this model? Why? Seat of the pants guesses...
A sample of 10 observations collected in a regression study on three variables, x_1(independent variable), x_2(independent...
A sample of 10 observations collected in a regression study on three variables, x_1(independent variable), x_2(independent variable and y(dependent variable). The sample resulted in the following data. SSR=28, SST=34 Using a 0.01 level of significance, we conclude that the regression model is significant overall. (Enter 1 if the conclusion is correct. Enter 0 if the conclusion is wrong.)
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...
A regression model involved 5 independent variables and 126 observations. The critical value of t for...
A regression model involved 5 independent variables and 126 observations. The critical value of t for testing the significance of each of the independent variable's coefficients will have is __
A regression model involved 5 independent variables and 136 observations. The critical value of t for...
A regression model involved 5 independent variables and 136 observations. The critical value of t for testing the significance of each of the independent variable's coefficients will have
Problem 7 You estimated a regression model using annual returns of ExxonMobil (as a dependent variable)...
Problem 7 You estimated a regression model using annual returns of ExxonMobil (as a dependent variable) and of the market (as an independent variable). The R-squared of this regression is 0.9. We can conclude that: A) ExxonMobil’s systematic risk is higher than ExxonMobil’s unsystematic risk B) ExxonMobil’s unsystematic risk is higher than ExxonMobil’s systematic risk C) ExxonMobil’s systematic risk and ExxonMobil’s unsystematic risk are about the same D) This R-squared implies that ExxonMobil’s beta is larger than one E) This...
Suppose we estimate a regression model containing a constant term and two explanatory variables. The analysis...
Suppose we estimate a regression model containing a constant term and two explanatory variables. The analysis is based on a sample size of 25 and produces a Durbin Watson statistic d=1.85. a. Approximately, what is the correlation coefficient between consecutive error terms? b. If this model has an R2 = .75, what is the value of the CALCULATED F statistic associated with a GLOBAL test? c. At the 1% level what is the CRITICAL value associated with a global test...
estimate std.error Intercept 26184.4 3517.3 snowfall 3824.8 247.5 r^2=.8565 adjusted r^2= .8529 s=8991 For each additional...
estimate std.error Intercept 26184.4 3517.3 snowfall 3824.8 247.5 r^2=.8565 adjusted r^2= .8529 s=8991 For each additional inch of snowfall, steam runoff decreases by 26,184 acre-feet, on average. increases by 26,184 acre-feet, on average. .decreases by 3824 acre-feet, on average.   increases by 3824 acre-feet, on average If multicollinearity is present, then we can conclude that the fitted regression model: may have estimated slopes very different from what we should expect due to numerical instabilities, making correct interpretation of the effect on...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT