Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	RELEASED VS. DETAINED, GENDER OF DEFENDANT, AGE OF DEFENDANT AT ARREST, RACE OF DEFENDANT, # PRIOR FELONY CONVICTIONSb	.	Enter
a. Dependent Variable: # PRISON MONTHS-MAXIMUM
b. All requested variables entered.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.183a	.033	.010	57.17214
a. Predictors: (Constant), RELEASED VS. DETAINED, GENDER OF DEFENDANT, AGE OF DEFENDANT AT ARREST, RACE OF DEFENDANT, # PRIOR FELONY CONVICTIONS

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	23761.784	5	4752.357	1.454	.206b
Residual	686417.174	210	3268.653
Total	710178.958	215
a. Dependent Variable: # PRISON MONTHS-MAXIMUM
b. Predictors: (Constant), RELEASED VS. DETAINED, GENDER OF DEFENDANT, AGE OF DEFENDANT AT ARREST, RACE OF DEFENDANT, # PRIOR FELONY CONVICTIONS

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	38.206	28.432		1.344	.180
GENDER OF DEFENDANT	14.031	23.874	.040	.588	.557
AGE OF DEFENDANT AT ARREST	-.328	.476	-.049	-.689	.492
RACE OF DEFENDANT	-5.135	7.999	-.045	-.642	.522
# PRIOR FELONY CONVICTIONS	2.752	1.326	.146	2.075	.039
RELEASED VS. DETAINED	11.962	9.214	.090	1.298	.196
a. Dependent Variable: # PRISON MONTHS-MAXIMUM

Using the sentencing dataset, run a regression analysis and answer the following questions:

1. Which independent variable(s) have a statistically significant effect on prison sentence length? Interpret the beta coefficient for these significant variable(s).
2. What is the R2 for the model? Is this coefficient relatively high or low?
3. Although nonsensical, interpret the beta coefficient for the intercept.
4. Is multicollinearity impacting your results? Explain why or why not.
5. Are there any influential cases in the dataset? Explain your answer.