I neep help on two questions!

A condition that occurs in multiple regression analysis if the independent variables are themselves correlated is known as:

1. autocorrelation
2. stepwise regression
3. multicorrelation
4. multicollinearity (I think this is the answer)

Multiple regression uses quantitative values to represent variables that are numeric in nature, but occasionally the use of qualitative variables are used to represent conditions of interest. In those cases, the researcher would use (circle one):

1. dummy variables (I'm thinking this one.)
2. systemic variables
3. non-systemic variables
4. insulating variables

Thanks for the help!

Too Funny- Debra got caught. ;0)

Why were you searching on Google as well?

For the first question, the condition that occurs in multiple regression analysis if the independent variables are themselves correlated is known as multicollinearity. This occurs when two or more independent variables in the regression model are highly correlated with each other, making it difficult to determine the individual contribution of each variable.

To answer the second question, when qualitative variables need to be represented in multiple regression analysis, researchers often use dummy variables. Dummy variables are created to represent different categories or levels of a qualitative variable. Each level is assigned a binary value (usually 0 or 1) to indicate whether it belongs to that category or not. By including these dummy variables in the regression model, qualitative information can be incorporated into the analysis. Therefore, the correct answer is 1. dummy variables.

I am in your class. This is against the rules of this test "Debra". Screen shot posted and provided to instructor. Good luck on the test. It's amazing what a search on Google can do these days.