Which of the following refers to a relatively high correlation among the independent variables of a regression equation?

Related BrainMass Solutions

• Economics and management

  Which of the following refers to a relatively high correlation among the independent variables of a regression equation? A. autocorrelation B. the identity problem C.

• Using Excel for Simple Regression Analyses

  Each of the regression lines had a positive y-intercept, meaning that a level of satisfaction with benefits equal to 0 would still be associated with a moderate to high level of job satisfaction (ranging from 5.9 to 6.6).

• regression analysis

  64613 Multicollinearity Multicollinearity refers to the existence of correlation among the independent variables in a multiple regression model. Discuss how multicollinearity can impact your regression analysis. How do you indentify it?

• Regression: Predictor and Criterion Variables

  Correlation shows relationship (co-relation) between two variables. It also gives us ways to measure behavior and to distinguish among the influences of overlapping variables.

• Economics and Management

  When the R2 of a regression equation is very high, it indicates that C. a high proportion of the variation in the independent variable can be accounted for by the variation in the independent variables 3.

• Regression Analysis

  The results of the regression will need to be stated as a complete equation resembling: Y = bx + a.

• 13 Statistics Questions

  The coefficient of correlation (r) is a measure of the interdependence of your x and y variables. If r is close to -1 or 1, the correlation (the interdependence of the variables) is very high. If r = 0, there is no correlation.

• Statistics: multilinear regression in SPSS

  Quantify Your Predictor/Independent Variables: Fill out the following table to quantify each of your two independent or predictor variables.

• Discussion on Causation, Correlation and Regression

  But when you run the regression, it will show you the relationship between a dependent variable and at least one independent variables. We use the regression to predict the value of dependent variable on the basis of the independent variables.