What is a multiple regression equation? (Select all that apply) a. One that represents the mathematical...
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
Homework Answers
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
Add Answer to:
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...
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
How does a bivariate regression model differ from a multiple regression model? Multiple Choice A bivariate...
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...
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...
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 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...
What are the four primary assumptions of multiple linear regression
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 normall...
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...
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
A valid multiple regression analysis assumes or requires that Select one: O a. The dependent variable...
A valid multiple regression analysis assumes or requires that Select one: O a. The dependent variable is measured using an ordinal, interval, or ratio scale O b. The residuals follow an F distribution O c. The independent variables and the dependent variable have a linear relationship O d. The observations are autocorrelated
Use computer software to find the multiple regression equation. Can the equation be used for prediction?...
Use computer software to find the multiple regression equation. Can the equation be used for prediction? A wildlife analyst gathered the data in the table to develop an equation to predict the weights of bears. He used WEIGHT as the dependent variable and CHEST, LENGTH, and SEX as the independent variables. For SEX, he used male=1 and female=2.
A valid multiple regression analysis assumes or requires that Select one: O a. The dependent variable is measured using an ordinal, interval, or ratio scale O b. The residuals follow an F distribution O c. The independent variables and the dependent variable have a linear relationship O d. The observations are autocorrelated
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