REGRESSION ANALYSIS

Statistical Analysis

Statistical Analysis

Case 1 :

Independent variable = Incarceration Services

Dependent variable = Legal Services satisfaction

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.189a

.036

-.006

1.01842

.036

.848

1

23

.367

a. Predictors: (Constant), IncarcerationServices

b. Dependent Variable: LegalServices

The value of R is 18.9% and the value of R - square is 0.036 that means there is some sort of association between the dependent variable that the legal services satisfaction and the independent variable that is incarceration services, but it is not clear that the relationship between the variables are strong.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.879

1

.879

.848

.367a

Residual

23.855

23

1.037

Total

24.734

24

a. Predictors: (Constant), IncarcerationServices

b. Dependent Variable: LegalServices

The above chart is showing that the significant value of ANOVA table is not less than 0.05 which means the model is not statistically significant, this shows that the legal services do not depend on the incarceration services.

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1

(Constant)

3.971

1.385

2.868

.009

IncarcerationServices

.243

.264

.189

.921

.367

1.000

1.000

a. Dependent Variable: LegalServices

From the above table, it can be understood that there is not too much multi - colinearity between the variables because the values of tolerance and VIF are near to 1 which is a good indication. But the significance value of the variables is showing that there is no relationship between the incarceration services and the legal services. Moreover the beta values of the independent variable that is incarceration services is positive which shows that if there were relationship then it would be positive.

Case 2 :

Independent variable = Incarceration Services

Dependent variable = Sentence satisfaction

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.171a

.029

-.013

1.07972

.029

.694

1

23

.413

a. Predictors: (Constant), IncarcerationServices

b. Dependent Variable: SentenceSatisfaction

The value of R is 17.1% and the value of R - square is 0.029 that means there is some sort of association between the dependent variable that the sentence satisfaction and the independent variable that is incarceration services, but it is not clear that the relationship between the variables are strong or not.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.809

1

.809

.694

.413a

Residual

26.813

23

1.166

Total

27.622

24

a. Predictors: (Constant), IncarcerationServices

b. Dependent Variable: SentenceSatisfaction

The above chart is showing that the significant value of ANOVA table is also not less than 0.05 which means the model is not statistically significant, this shows that the sentence satisfaction do not depend on the incarceration services.

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1

(Constant)

3.743

1.468

2.550

.018

IncarcerationServices

.233

.279

.171

.833

.413

1.000

1.000

a. Dependent Variable: SentenceSatisfaction

From the above table, it can be understood that there is not too much multi - colinearity between the variables because the values of tolerance and VIF are near to 1 which is a good indication. But ...