**How to interpret multiple regression**

Regression
is useful for making a predictive model. Let's say there's a positive
linear correlation between

*K*and*N*, but you suspect that Factors*L*and*M*also contribute to Outcome*N*.
Make up a story—say, that Factors

*K*,*L*, and*M*represent intelligence, persistence, and amount of sleep per night and*N*refers to a course grade.
So,
to test the relative impacts of Factors

*K*,*L*, and*M*on Outcome*N*, you can feed each factor into a regression model, and test whether each factor increases the fit. That is, a correlation between Factor*K*and Outcome*N*yields a Pearson's*r*of .64 and*R2*of .4096.
But, when you run a regression
testing
the effect of
Factors

*K***and***L*on Outcome*N*, you find an*R2*of .5625, with a significant change in the*R2*value. That means that Factors*K*and*L***together**do a better job of explaining the relationship than Factor*K*alone.
Then,
you run a regression with Factors

*K*,*L*, and*M*together, and find an*R2*of .5929, with no significant change—this means that Factor*M*does not help to explain the relationship. Outcome*N*is due mostly to Factors*K*and*L*; Factor*M*is an unimportant predictor of Outcome*N*.*VoilĂ*! There's regression in a nutshell!

And, if you're confused about the math...remember in middle school or high school math, when you learned about "rise over run" and learned the formula y =

For more help explaining statistical concepts and when to use them,

*m*x + b? Yeah, that's a simple linear regression. With multiple regression, you can add multiple terms, such that y =*a*x_{1}+*b*x_{2}+*c*x_{3}...+ z. But it's still the same concept, just with more predictors than that lone "*m*x" term.
In case you missed it, there are some fantastic, easy-to-use, and FREE stats programs available now! I review them here.

please download my freely available PDF guide here!

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