Question

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.941a	.885	.872	1.00528
a. Predictors: (Constant), SelfControl, NumStrains

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	132.570	2	66.285	65.590	.000b
	Residual	17.180	17	1.011
	Total	149.750	19
a. Dependent Variable: AgeFirstArrest
b. Predictors: (Constant), SelfControl, NumStrains

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B		Collinearity Statistics
		B	Std. Error	Beta			Lower Bound	Upper Bound	Tolerance	VIF
1	(Constant)	23.173	.669		34.614	.000	21.760	24.585
	NumStrains	-.110	.051	-.184	-2.163	.045	-.218	-.003	.937	1.067
	SelfControl	-.106	.010	-.878	-10.343	.000	-.128	-.085	.937	1.067
a. Dependent Variable: AgeFirstArrest

Collinearity Diagnosticsa
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions
				(Constant)	NumStrains	SelfControl
1	1	2.783	1.000	.01	.02	.02
	2	.147	4.350	.07	.93	.23
	3	.070	6.291	.92	.05	.76
a. Dependent Variable: AgeFirstArrest

3. What is the adjusted r² value and what does that value tell us?

4. Is the model as a whole significant? How do you know?

5. Which variable or variables significantly explain age at first arrest? How do you know?

6. Which variable explains more of age at first arrest? How do you know?

7. Are the beta values in the expected direction (a higher self control score indicates lower self control)? Explain.

8. Are there problems with collinearity? How do you know?

Answer 1

Please don't hesitate to give a thumbs up to the answer, in case you're satisfied with it

3. What is the adjusted r2 value and what does that value tell us?

.872 is the adjusted r2 value. It tells us that 87.2% of variation in dependent variable is being explained by the independent variables, this r square is also adjusted for the number of independent variables used in the equation

4. Is the model as a whole significant? How do you know?

Yes, model is significant. We know this from ANOVA table' p-value. If its less than .05 then
model is statistically significant

5. Which variable or variables significantly explain age at first arrest? How do you know?
Both NumStrains and SelfControl do. We know this because in the "Coefficients" table both
these coefficient' p-value is less than .05.

6. Which variable explains more of age at first arrest? How do you know?
SelfControl , because of the following reason: It has the least p-value, indicating more significance of the variable

7. Are the beta values in the expected direction (a higher self control score indicates lower self control)? Explain.
Yes, a higher selft control and a higher Number of strains leads to lower Age First arrests is an expected behavior. The model exactly replicates these results through the sign of the beta coefficients ( i.e. its -ive, indicating a inverse relationship_

8. Are there problems with collinearity? How do you know?
No problem w.r.t to collinearity. We know this because the VIF values in 1.067, a low value for calling out multicollinearity. Also, a value above 30 on the condition index might qualify for potential multicollinearity problem