Question

Suppose that in a multiple regression the overall model is significant, but the p values of...

Suppose that in a multiple regression the overall model is significant, but the p values of none of the individual slope coefficients are small enough. This means that:

a.none of the other choices are correct

b. nonlinear model would be a better fit

c. the assumptions have been violated

d. multicollinearity may be present

e. linear regression would be better

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
In any regression model, p denotes the number of explanatory variables in the model. In simple...
In any regression model, p denotes the number of explanatory variables in the model. In simple linear regression (SLR), p=1. True/False? When testing whether the slope of a explanatory variable is 0 or not in context of multiple regression, what distribution is used to determine the p-value? standard normal distribution / t distribution with n−1 degrees of freedom / t distribution with n−2 degrees of freedom / t distribution with n−p−1 degrees of freedom ? In multiple regression, there is...
If a regression model is fit with two predictors (x1) and (x2) and the t-test p-values...
If a regression model is fit with two predictors (x1) and (x2) and the t-test p-values for each is large but when two simple linear regressions are fit each with only one of the predictors, the t-test p-values are small, the variables (x1) and (x2) are: (choose 1 one of the options below) a) highly coorelated b) both significant c) insignificant d) plain weird
For multiple linear regression, two cases of models exist in this problem. Y -> overall test...
For multiple linear regression, two cases of models exist in this problem. Y -> overall test score / ex) sat A,B,C,D,E -> subjects of the test First Model: Y = A + B + C + D   / intercept <0, coefficients: β(A)<0,  β(B)<0,  β(C)>0,  β(D)>0 Second Model: Y = B + C + D (excluded A) / intercept <0, coefficients:  β(B)>0,  β(C)>0,  β(D)>0 At the first model, estimated coefficient of A and B were negative. The result was quite confusing, so second model was made from...
Using 20 observations, the multiple regression model y = β0 + β1x1 + β2x2 + ε...
Using 20 observations, the multiple regression model y = β0 + β1x1 + β2x2 + ε was estimated. A portion of the regression results is shown in the accompanying table: df SS MS F Significance F Regression 2 2.12E+12 1.06E+12 55.978 3.31E-08 Residual 17 3.11E+11 1.90E+10 Total 19 2.46E+12 Coefficients Standard Error t Stat p-value Lower 95% Upper 95% Intercept −986,892 130,984 −7.534 0.000 −1,263,244 −710,540 x1 28,968 32,080 0.903 0.379 −38,715 96,651 x2 30,888 32,925 0.938 0.362 −38,578 100,354...
With multiple regression, the main focus is on variables that are significant within the model and...
With multiple regression, the main focus is on variables that are significant within the model and contribute to the variation occurring on the dependent variable. When multiple variances within the model are insignificant, then the reliability of the model is reduced. Therefore, we can not depend on the model for future reference. In this analysis, the dependent variable is ethical behavior that can be determined by the course taken, age, gender, and personality character of an individual. This model can...
Which of the following statements about the regression standard error hold TRUE? (2p) I. The regression...
Which of the following statements about the regression standard error hold TRUE? (2p) I. The regression standard error reflects the variation of the y-values about the regression line. II. The regression standard error is an estimate of the model standard deviation . III. The larger the regression standard error is, the better the model fits the data and the more precise inference about the regression model will be. A) I B) I and II C) II and III D) I,...
13. Interpreting the intercept in a simple linear regression model is: * (A) reasonable if the...
13. Interpreting the intercept in a simple linear regression model is: * (A) reasonable if the sample contains values of x around the origin. (B) not reasonable because researchers are interested in the effect of a change in x on the change in y. (C) reasonable if the intercept’s p-value is less than 0.05. (D) not reasonable because it is always meaningless. 14. Which of the following is NOT one of the assumptions necessary for simple linear regressions?: * (A)...
Discuss the model and interpret the results: report overall model fit (t and significance), report the...
Discuss the model and interpret the results: report overall model fit (t and significance), report the slope coefficient and significance, report and interpret r squared. Regression Statistics Multiple R 0.001989374 R Square 3.95761E-06 Adjusted R Square -0.005046527 Standard Error 8605.170404 Observations 200 ANOVA df SS MS F Significance F Regression 1 58025.4985 58025.4985 0.00078361 0.977695901 Residual 198 14661693620 74048957.68 Total 199 14661751645 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 15668.85874 2390.079111 6.555790838...
PROVIDE EXPLANATION PLS! 1. The owner of a construction firm, upon seeing the regression result, disagrees...
PROVIDE EXPLANATION PLS! 1. The owner of a construction firm, upon seeing the regression result, disagrees because the model suggests that the number of bathrooms does not contribute information in the prediction of home price. He explains that when he adds another bathroom, it increases the value of the home. How might you explain this apparent paradox? A, If it is determined that bigger homes tend to have more bathrooms, then multicollinearity may be a problem. That would lead to...
1.Running the following regression model: where Yt and Xt are both non-stationary variables. Which of the...
1.Running the following regression model: where Yt and Xt are both non-stationary variables. Which of the following statements is TRUE? a. This necessarily leads to spurious results without exception b. The Durbin-Watson d statistic will be larger than the R2 c. Including a dummy variable in the regression specification will provide meaningful results 2.A researcher might be alerted to the presence of multicollinearity by which of the following: a. High values of the estimated slope coefficients b. A very low...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT