Statistic Home work help Linear regression and logistic regression
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linearandlogisticregression.docx
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Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
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1 |
.941a |
.885 |
.872 |
1.00528 |
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a. Predictors: (Constant), SelfControl, NumStrains |
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ANOVAa |
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Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
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1 |
Regression |
132.570 |
2 |
66.285 |
65.590 |
.000b |
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Residual |
17.180 |
17 |
1.011 |
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Total |
149.750 |
19 |
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a. Dependent Variable: AgeFirstArrest |
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b. Predictors: (Constant), SelfControl, NumStrains |
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Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
95.0% Confidence Interval for B |
Collinearity Statistics |
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B |
Std. Error |
Beta |
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Lower Bound |
Upper Bound |
Tolerance |
VIF |
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1 |
(Constant) |
23.173 |
.669 |
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34.614 |
.000 |
21.760 |
24.585 |
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NumStrains |
-.110 |
.051 |
-.184 |
-2.163 |
.045 |
-.218 |
-.003 |
.937 |
1.067 |
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SelfControl |
-.106 |
.010 |
-.878 |
-10.343 |
.000 |
-.128 |
-.085 |
.937 |
1.067 |
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a. Dependent Variable: AgeFirstArrest |
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Collinearity Diagnosticsa |
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Model |
Dimension |
Eigenvalue |
Condition Index |
Variance Proportions |
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(Constant) |
NumStrains |
SelfControl |
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1 |
1 |
2.783 |
1.000 |
.01 |
.02 |
.02 |
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2 |
.147 |
4.350 |
.07 |
.93 |
.23 |
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3 |
.070 |
6.291 |
.92 |
.05 |
.76 |
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a. Dependent Variable: AgeFirstArrest |
1. When do we use linear regression? Give an original example that is relevant to criminology or criminal justice.
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?
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Omnibus Tests of Model Coefficients |
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Chi-square |
df |
Sig. |
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Step 1 |
Step |
53.959 |
5 |
.000 |
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Block |
53.959 |
5 |
.000 |
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Model |
53.959 |
5 |
.000 |
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Model Summary |
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Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
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1 |
93.346a |
.375 |
.519 |
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a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. |
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Classification Tablea |
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Observed |
Predicted |
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EverPot |
Percentage Correct |
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.00 |
1.00 |
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Step 1 |
EverPot |
.00 |
25 |
14 |
64.1 |
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1.00 |
5 |
71 |
93.4 |
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Overall Percentage |
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83.5 |
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a. The cut value is .500 |
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Variables in the Equation |
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B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
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Step 1a |
TimeStudy |
.275 |
.269 |
1.045 |
1 |
.307 |
1.316 |
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TimeExtracurrs |
-.479 |
.241 |
3.963 |
1 |
.047 |
.619 |
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ReligImport |
-.382 |
.183 |
4.339 |
1 |
.037 |
.683 |
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FriendsUse |
1.258 |
.349 |
13.015 |
1 |
.000 |
3.517 |
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TimeFriends |
1.140 |
.297 |
14.760 |
1 |
.000 |
3.127 |
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Constant |
-6.823 |
2.007 |
11.559 |
1 |
.001 |
.001 |
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a. Variable(s) entered on step 1: TimeStudy, TimeExtracurrs, ReligImport, FriendsUse, TimeFriends. |
10. When do we use logistic regression? Give an original example that is relevant to criminology or criminal justice.
11. How much of the change in the dependent variable is explained by the model as a whole? How do you know?
12. Which variables significantly predict marijuana use? How do you know?
13. Are the variables that significantly predict marijuana use are in the expected direction? How do you know?
14. Which variable best predicts marijuana use? How do you know?
15. How many more times is someone whose friends use drugs and alcohol likely to use marijuana that someone whose friends do not use drugs and alcohol?
16. How much less likely is someone who spends time in extracurriculars to use marijuana than someone who does not spend time in extracurriculars?
