Correlations, ANOVA, and Multiple Regression

 

 

How does linear regression differ from analysis of variance?
Using the Jerry Falwell Online Library, find 2 research studies related to public policy that use linear regression. Include the title of the study and a citation, then answer the following questions for each study (hint: you can probably use articles that you cited in your annotated bibliography earlier in the course):

What is one independent variable? What is the dependent variable?
If there is more than one independent variable, what argument does the researcher make that these variables are independent of one another?
Which of the two studies seems to present the least convincing evidence that the dependent variable is predicted by the independent variable? Why?

Sample Solution

Correlations, ANOVA, and Multiple Regression

Regression is a very effective statistical method to establish the relationship between sets of variables. ANOVA is the short form of analysis of variance. It’s a statistical tool that is generally used on random variables. Both the Regression and ANOVA are statistical models which are used in order to predict the continuous outcome but in case of the regression, continuous outcome is predicted on basis of the one or more than one continuous predictor variables whereas in case of ANOVA continuous outcome is predicted on basis of the one or more than one categorical predictor variables. Regression is a statistical method to establish the relationship between sets of variables in order to make predictions of the dependent variable with the help of independent variables. ANOVA, on the other hand, is a statistical tool applied to unrelated groups to find out whether they have a common mean.

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