When you have a first regression model, then a second regression model with an additional independent...
When you have a first regression model, then a second regression model with an additional independent variable, then a third regression model with yet another additional independent variable, we call these a set of:
| a. |
subsequent models |
|
| b. |
concurrent models |
|
| c. |
nested models |
|
| d. |
incremental models |
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When you have a first
regression model, then a second regression model with an additional
independent...
You have been asked to engage in an additional project concerning sales of the Nikon D5...
You have been asked to engage in an additional project
concerning sales of the Nikon D5 camera etc. Specifically, this
time you wish to study the sales of a certain camera lens by first
using two independent variables, sales of camera bodies and the
price of the lens. These are the independent variables for problem
one. For problem two, you will add the gender of the purchaser of
the equipment to the model. In the third question, you will examine...
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...
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...
1) A regression model that involves a single independent variable is called ________. A) single linear...
1) A regression model that involves a single independent variable is called ________. A) single linear regression B) simple unit regression C) simple linear regression D) individual linear regression
What do we mean by “regression toward the mean?” A. The linear regression equation can be...
What do we mean by “regression toward the mean?” A. The linear regression equation can be used to identify the average value of each variable in the model. B. Linear regression normalizes the scale of the variables so they have a mean of zero and standard deviation of 1. C. The phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on a second measurement. D. Outliers in the model...
Suppose you ran a regression with a full model. After looking at the result, you decided to drop ...
Suppose you ran a regression with a full model. After looking at the result, you decided to drop some variables and run another regression with a reduced model. The ANOVA tables of the two regression models are as follow: Full Model MS Significance F 3 3465.51 24 2925.463 27 6390.973 0.000259047 Regression Residual Total Reduced Model MS Significance F Regression Residual Total 1 3396.754 26 2994.219 27 6390.973 1.08156E-05 4.1. Question 4.1 What is the test statistic for the overall...
When evaluating a multiple regression model, for example when we regress dependent variable Y on two...
When evaluating a multiple regression model, for example when we regress dependent variable Y on two independent variables X1 and X2, a commonly used goodness of fit measure is: A. Correlation between Y and X1 B. Correlation between Y and X2 C. Correlation between X1 and X2 D. Adjusted-R2 E. None of the above
we have heteroskedasticity in a regression when: When the variance of error terms changes when an...
we have heteroskedasticity in a regression when: When the variance of error terms changes when an independent variable become larger. The consequent error terms of the regression are correlated with each other. When two or more independent variables are correlated with each other. When the regression error terms are correlated with and independent variable.
Multiple regression analysis produces a _______ for each independent variable in the model. Choices A. Partial...
Multiple regression analysis produces a _______ for each independent variable in the model. Choices A. Partial adjusted R-Square B. Partial regression coefficient C. Partial chi-square D. Partial correlation coefficient
1) You are reviewing a ridge regression model that your business partner is bu...
1) You are reviewing a ridge regression model that your business partner is building. In the model, he has applied the shrinkage penalty to all terms (intercept included), leading to a massive reduction in variance. Is everything okay with this implementation? (Choose the MOST CORRECT answer.) a) Yes, the ridge regression model is implemented properly. b) No, there should be a massive reduction in bias (not variance). c) No, the shrinkage penalty term does not apply to the intercept. d)...
You have been asked to engage in an additional project
concerning sales of the Nikon D5 camera etc. Specifically, this
time you wish to study the sales of a certain camera lens by first
using two independent variables, sales of camera bodies and the
price of the lens. These are the independent variables for problem
one. For problem two, you will add the gender of the purchaser of
the equipment to the model. In the third question, you will examine...
Suppose you ran a regression with a full model. After looking at the result, you decided to drop some variables and run another regression with a reduced model. The ANOVA tables of the two regression models are as follow: Full Model MS Significance F 3 3465.51 24 2925.463 27 6390.973 0.000259047 Regression Residual Total Reduced Model MS Significance F Regression Residual Total 1 3396.754 26 2994.219 27 6390.973 1.08156E-05 4.1. Question 4.1 What is the test statistic for the overall...
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