# Math

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1.You
Are now trying to find good predictors for who uses the computer after the
Training (**variable**: **compuse2**). You heard the time 2 variables were more
**correlated** with your dependent variable, so focus on them as predictors (companx2, compcon2, complik2).

a.First, What is the correlation between each of those predictors and your criterion (i.E., dependent variable)?

**The compax2 and compuse2 were significantly
Correlated, r(38) = .76, p < .001.**

**The compcon2 and compuse2 were significantly
Correlated, r(38) = .73, p < .001.**

**The complik2 and compuse2 were significantly
Correlated, r(38) = .41, p = .008.**

a.How about the correlation just between the Predictors? Is that hinting at multicollinearity?

**The companx2 and compcon2 were significantly
Correlated, r(38) = .46, p = .003.**

**The compcon2 and complik2 were significantly
Correlated, r(38) = .38, p = .016.**

**The companx2 and complik2 were significantly
Correlated, r(38) = .42, p = .006.**

Because none of the correlations Were higher than .8, this does not hint at multicollinearity across the Independent variables.

a.When You input all the time 2 variables simultaneously In your model, is that a good model? Make sure you mention the multicollinearity Results.

Companx2, complik2, and compcon2 explained a significant proportion of variance in compuse2, R2 = .76, F(3,36) = 38.11, p < .001. Also, This model this does not seem to have any multicollinearity issues (Tolerance > .1; VIF < 10)

Report in APA style the results of your multiple regression.

Based on the multiple regression analysis,
Companx2 **significantly** predicted compuse2, b
= .53,
T(36) = 5.49, p < .001, compcon2 significantly predicted compuse2,
B = .49, t(36) = 5.20, p <
.001, but complik2 did not
Significantly predict compuse2, b
= .004,
T(36) =.04, p = .966.