Multiple regression using STATA video 1 

Me too

Basically, there is a negative relationship between the two variables. So higher scores on anxiety are associated with lower achievement scores. By the same token, lower scores on anxiety are associated with higher scores on achievement. You have the basic idea.

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SMint = Subject Matter of interest... MGoals = Mastery Goals

HI there, Ibsa. I'm assuming you are confused because I didn't use the i. prefix for the gender variable. When you have a binary predictor (such as gender), there is no need to state that it is a factor variable (although it it perfectly ok if you would rather do that). The i. prefix simply will cause Stata to dummy code your factor variable, so that group membership is identifiable on a set of binary predictor variables (each coded 0 and 1). Since in my example gender is already binary and dummy coded 0 and 1, there's no need to request this using the i. prefix (although again, it is ok if you want to do this). Cheers!

@@mikecrowson2462 Well explained. Thank you

Hi Kidest, it's been a very long time since I put this video up. Going back and looking at the output, it looks like the unstandardized regression slope for anxiety is -.16. That means that for every one raw score unit increase on anxiety, achievement is predicted to decrease by .16 raw score units. Alternatively, we can say that for every one raw score unit decrease on anxiety, achievement is predicted to increase by .16 raw score units. I'm not sure where you got the 24% you are referring to. If you are asking about the standardized regression slope (Beta column) of -.25, then this is the predicted change in Achievement in standard score (aka z-score units) per standard score increase on the predictor. So, the standardized coefficient of -.25 indicates that for every one z-score unit increase on anxiety, achievement is predicted to decrease by .25 z-score units. I hope this helps!