Removing an existing predictor variable from a regression model: A. Can never increase R-squared B. Can...
Removing an existing predictor variable from a regression model:
A. Can never increase R-squared
B. Can never decrease R-squared
C. Has never any effect on R-squared
D. Changes R-squared by either increasing or decreasing it
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Removing an existing predictor variable from a regression
model:
A. Can never increase R-squared
B. Can...
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...
In a regression analysis, if a predictor variable x is found to be highly significant we...
In a regression analysis, if a predictor variable x is found to be highly significant we would conclude that: A. a change in y causes a change in x B. a change in x causes a change in y C. changes in x are not related to changes in y D. changes in x are associated to changes in y
1.) What is the difference between a simple regression model and a multiple regression model? a.)...
1.) What is the difference between a simple regression model and a multiple regression model? a.) There isn’t one. The two terms are equivalent b.) A simple regression model has a single predictor whereas a multiple regression model has potentially many c.) A simple regression model can handle only limited amounts of data whereas a multiple regression model can handle large data sets d.) A simple regression is appropriate for a dichotomous outcome variable, whereas a multiple regression model should...
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5.) You can NOT use a regression for which of the following activities? a.) You can in fact use a regression model for all of these b.) Forecasting new observations c.) Identifying unusual data points d.) Measuring the proportion of variability in the outcome variable explained by the predictor variables 6.) A simple regression equation decomposes the observed data into two parts: the fitted values and the residuals. What is the interpretation of a residual? a.) The horizontal distance from...
#1 In simple linear regression, r is the: a) coefficient of determination. b) mean square error. ...
#1 In simple linear regression, r is the: a) coefficient of determination. b) mean square error. c) correlation coefficient. d) squared residual. #2 In regression analysis, with the model in the form y = β0 + β1x + ε, x is the a) estimated regression equation. b) y-intercept. c) slope. d) independent variable. #3 A regression analysis between sales (y in $1,000s) and advertising (x in dollars) resulted in the following equation. ŷ = 40,000 + 3x The above equation...
2. Consider a simple linear regression model for a response variable Yi, a single predictor variable...
2. Consider a simple linear regression model for a response variable Yi, a single predictor variable ri, i-1,... , n, and having Gaussian (i.e. normally distributed) errors Ý,-BzitEj, Ejį.i.d. N(0, σ2) This model is often called "regression through the origin" since E(Yi) 0 if xi 0 (a) Write down the likelihood function for the parameters β and σ2 (b) Find the MLEs for β and σ2, explicitly showing that they are unique maximizers of the likelihood function. (Hint: The function...
1.13 Consider a multiple regression model 1.15 Consider a multiple regression model: with a dummy variable:...
1.13 Consider a multiple regression model 1.15 Consider a multiple regression model: with a dummy variable: h(wage)-A, + β.educ + β white + β,NonWhite + u where wage and educ denote the annual income and the number of years of education, respectively. White indicates the dummy variable taking 1 if white and zero otherwisc. Non White indicates the dummy variable taking 1 if non-white (African, Hispanic, Asian, Pacific Islander, Native American, etc.) and zero otherwise. Which of the following is...
True (T) or False (F) - 5 points (NO NEED TO EXPLAIN, JUST SAY WHETHER IT IS TRUE OR FALSE) B. 1. R-squared never decre...
True (T) or False (F) - 5 points (NO NEED TO EXPLAIN, JUST SAY WHETHER IT IS TRUE OR FALSE) B. 1. R-squared never decreases when you add more regressors. In panel data we can observe many individuals across more than one point in time. 2. 3. We want to test the effect of a speed limit change in New Mexico. When choosing a control state, we should try to find a state that also had a change in speed...
12. (Ch13.5) True or False? The effect of a binary predictor in a multiple regression with three ...
12. (Ch13.5) True or False? The effect of a binary predictor in a multiple regression with three predictors (two of them are continuous, one is binary) is to shift the regression fitting plane up or down 13. (Ch13.5) True or False? Unlike other predictors, the student's t for testing the significance of a binary predictor is either 0 or l1 14. (Ch13.5) In a multiple regression model of student grades, we would code the nine categories of business courses taken...
1 Will making each of the following changes increase, decrease, or have no effect on the...
1 Will making each of the following changes increase, decrease, or have no effect on the size of a t-value? In each case, circle the appropriate response. a. Increasing the difference between the means. Increase Decrease No Change b. Decreasing the variance of both groups. Increase Decrease No Change c. Increasing the sample size of both groups. Increase Decrease No Change d. Decreasing the population size Increase Decrease No Change
2. Consider a simple linear regression model for a response variable Yi, a single predictor variable ri, i-1,... , n, and having Gaussian (i.e. normally distributed) errors Ý,-BzitEj, Ejį.i.d. N(0, σ2) This model is often called "regression through the origin" since E(Yi) 0 if xi 0 (a) Write down the likelihood function for the parameters β and σ2 (b) Find the MLEs for β and σ2, explicitly showing that they are unique maximizers of the likelihood function. (Hint: The function...
1.13 Consider a multiple regression model 1.15 Consider a multiple regression model: with a dummy variable: h(wage)-A, + β.educ + β white + β,NonWhite + u where wage and educ denote the annual income and the number of years of education, respectively. White indicates the dummy variable taking 1 if white and zero otherwisc. Non White indicates the dummy variable taking 1 if non-white (African, Hispanic, Asian, Pacific Islander, Native American, etc.) and zero otherwise. Which of the following is...
True (T) or False (F) - 5 points (NO NEED TO EXPLAIN, JUST SAY WHETHER IT IS TRUE OR FALSE) B. 1. R-squared never decreases when you add more regressors. In panel data we can observe many individuals across more than one point in time. 2. 3. We want to test the effect of a speed limit change in New Mexico. When choosing a control state, we should try to find a state that also had a change in speed...
12. (Ch13.5) True or False? The effect of a binary predictor in a multiple regression with three predictors (two of them are continuous, one is binary) is to shift the regression fitting plane up or down 13. (Ch13.5) True or False? Unlike other predictors, the student's t for testing the significance of a binary predictor is either 0 or l1 14. (Ch13.5) In a multiple regression model of student grades, we would code the nine categories of business courses taken...
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