What is a multiple regression equation? (Select all that apply)
a. One that represents the mathematical effect that several independent variables have on the dependent variable
b. One in which the x-values are multiplied by one another
c. One that explains more of the variance in y than does a single linear regression equation
d. An experimental model for determining best practices
e. One that uses more than one predictor variable to predict the value of the outcome variable
f. One that explains all of the variance in the dependent variable, in terms of several independent variables
correct options are:
a. One that represents the mathematical effect that several independent variables have on the dependent variable
e. One that uses more than one predictor variable to predict the value of the outcome variable
What is a multiple regression equation? (Select all that apply) a. One that represents the mathematical...
Which of the following apply to multiple linear regression? (Check all correct answers.) Multiple predictor variables Multiple outcome variables A single predictor variable A single outcome variable
Multiple regression procedures may be used when two or more interval-level measures serve as predictors of some normally distributed interval-level dependent variable. In this model, the regression coefficient for any independent or predictor variable (X1) represents the change in the dependent or outcome variable (Y) associated with one unit change in X1, while controlling for or maintaining other predictors (X2, X3, etc.) at constant. If you required to use this model in the analysis of the data of a research...
How does a bivariate regression model differ from a multiple regression model? Multiple Choice A bivariate regression has only one dependent and independent variable but a multiple regression has one dependent variable and may have many independent variables. A bivariate regression has more than one dependent variable and only one independent variable where a multiple regression has one dependent variable and may have many independent variables. A bivariate regression has only one dependent and many independent variables but a multiple...
How does a regression plane differ from a regression line? Multiple Choice A regression plane represents a two-dimensional space (e.g. one dependent and one independent variable) whereas a regression line represents a three-dimensional space (e.g. one dependent and two independent variables). A regression plane represents a three-dimensional space (e.g. one dependent and two independent variables) whereas a regression line represents a two-dimensional space (e.g. one dependent and one independent variable). A regression plane can represent a bivariate regression model and...
The mathematical equation relating the expected value of the dependent variable to the value of the independent variables, which has the form of E(y) = β0 + β1x1 + β2x2 + β3x3 +...+ βpxp is: a. a multiple regression equation. b. a simple linear regression model. c. a multiple nonlinear regression model. d. an estimated multiple regression equation.
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...
QUESTION 2 In multiple linear regression analysis, the number of independent variables should be as large as possible. more than 5. guided by economic theory. enough to guarantee that statistical significance is achieved. QUESTION 3 Omitted variable bias occurs when always occurs when performing simple linear regression analysis. independent variables that should be included in the analysis are not included and those independent variables are related to the variables in the regression model. independent variables that should not be included...
What are the four primary assumptions of multiple linear regression (check all that apply)? Select one or more: a. Linear relationships between predictors and outcome b. Residuals are normally distributed with a mean of zero. c. There is constant variance of residuals d. The residuals are independent e. The predictors are normally distributed.
Which of the following are assumptions for the linear regression model? CHECK THAT ALL MAY APPLY!!! Select one or more: a. Regression function (i.e., equation) is linear. b. Error terms are normally distributed. c. Error terms are independent. d. Error terms have constant variance. e. Regression model fits all observations (i.e., no outliers).
A multiple regression model has _____. a. at least two dependent variables b. more than one dependent variable c. more than one independent variable d. only one independent variable