Solution-Part -1make sure you read the output carefully

Part -1make sure you read the output carefully this output

Reference no: EM13347951

Part -1

Make sure you read the output carefully. This output pertains to a set of juvenile court data looking at the length of time that juvenile offenders are held in secure detention while their case is being processed through juvenile court. For this regression model, the following variables are used:

LOS: the length of stay in detention, in days

Number of filed charges: the number of different crimes they are currently charged with

Number of prior JD/JS cases: the number of prior juvenile court cases

Number of prior felonies: the number of prior charges for felonies

offlevel: this measure indicates the severity of the most serious charge they are charged with, where 0 is for the least severe and 8 is for the most severe

female: a dummy variable coded 0 for males and 1 for females

black: a dummy variable coded 1 for blacks

otherrac: a dummy variable coded 1 for the "other race" category (Note that when black and other race are in the model together, then the 0 category on each variable refers to whites)

The following questions pertain to the data:

a) What is the equation for this regression model?

b) Write one sentence describing what we have learned about the relationship between the dependent variable and each of the independent variables found to have a statistically significant relationship on the dependent variable. Write a sentence for each relationship. Be specific.

c) What can you conclude about the fit of the model? Explain.

d) What concerns, if any, can you raise about the model, based on the information provided here on the output? Explain.

Regression

Descriptive Statistics

Mean

Std. Deviation

LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

11.84

2.25

3.72

.83

.0861

.5976

.2490

3.4211

20.098

1.897

2.843

1.196

.28051

.49046

.43251

1.25469

2719

LOS

Number of filed charges

Number of prior JD/JS cases

Number of prior felonies

Pearson Correlation LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

1.000

.116

.294

.266

.035

.061

-.084

.161

.116

1.000

.040

.030

.010

.035

-.108

.248

.294

.040

1.000

.665

-.025

.162

-.143

-.024

.266

.030

.665

1.000

-.023

.131

-.208

.061

Sig. (1-tailed) LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

.000

.034

.001

.000

.018

.060

.303

.033

.000

.018

.000

.095

.000

.106

.000

.060

.000

.117

.000

.001

N LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

2719

Correlations

otherrac

black

female

offlevel

Pearson Correlation LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

.035

.010

-.025

-.023

1.000

-.374

-.071

-.047

.061

.035

.162

.131

-.374

1.000

-.027

.072

-.084

-.108

-.143

-.208

-.071

-.027

1.000

-.174

.161

.248

-.024

.061

-.047

.072

-.174

1.000

Sig. (1-tailed) LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

.034

.303

.095

.117

.000

.008

.001

.033

.000

.079

.000

.079

.000

.106

.001

.008

.000

N LOS

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

2719

Model Summaryb

Model

R Square

Adjusted R Square

Std. Error of the Estimate

.358a

.128

.126

18.794

a. Predictors: (Constant), offlevel, Number of prior JD/JS cases, otherrac, female, Number of filed charges, black, Number of prior felonies

b. Dependent Variable: LOS

ANOVAb

Model

Sum of

Squares

Mean Square

Sig.

1 Regression

Residual

Total

140415.78

957518.90

1097934.7

2711

2718

20059.397

353.198

56.794

.000a

a. Predictors: (Constant), offlevel, Number of prior JD/JS cases, otherrac, female, Number of filed charges, black, Number of prior felonies

b. Dependent Variable: LOS

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std. Error

Beta

1 (Constant)

Number of filed charges

Number of prior JD/JS

cases

Number of prior felonies otherrac

black female offlevel

-5.976

.714

1.575

1.808

4.085

.794

.335

2.325

1.343

.197

.172

.410

1.393

.806

.869

.303

.067

.223

.108

.057

.019

.007

.145

-4.449

3.626

9.170

4.407

2.933

.986

.385

7.683

.000

.003

.324

.700

.000

a. Dependent Variable: LOS

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

Predicted Value

Residual

Std. Predicted Value

Std. Residual

-5.64

-35.960

-2.432

-1.913

47.67

252.596

4.984

13.441

11.84

.000

7.188

18.769

1.000

.999

2719

a. Dependent Variable: LOS

Part-2

Pay equity for men and women has been an ongoing source of conflict for a number of years in North America. Suppose that a statistics practitioner is investigating the factors that affect salary differences between male and female university professors. He believes that the following variables have some impact on a professor's salary:

Number of years since first degree (years)

Highest degree, coded 1 for Ph.D and 0 otherwise (phd)

Average score on teaching evaluations (evaluati)

Number of articles published (articles)

Gender, coded 1 if male and 0 if female (gender)

The following questions pertain to the data:

a) What is the equation for this regression model?

c) What can you conclude about the fit of the model? Explain.

d) What concerns can you raise about the model, based on the information provided here on the output? Explain.

Regression

Descriptive Statistics

	Mean	Std. Deviation	N
salary	44889.790	12906.32702	100
years	23.7000	9.54151	100
phd	.8500	.35887	100
evaluati	5.3496	.54142	100
articles	12.1600	5.92839	100
gender	.6300	.48524	100

Correlations

		salary	years	phd	evaluati	articles	gender
Pearson Correlation	salary	1.000	.941	-.030	.475	.798	.070
	years	.941	1.000	-.161	.304	.698	.009
	phd	-.030	-.161	1.000	.176	.187	.142
	evaluati	.475	.304	.176	1.000	.409	.074
	articles	.798	.698	.187	.409	1.000	.038
	gender	.070	.009	.142	.074	.038	1.000
Sig. (1-tailed)	salary	.	.000	.383	.000	.000	.246
	years	.000	.	.055	.001	.000	.467
	phd	.383	.055	.	.040	.031	.079
	evaluati	.000	.001	.040	.	.000	.233
	articles	.000	.000	.031	.000	.	.352
	gender	.246	.467	.079	.233	.352	.
N	salary	100	100	100	100	100	100
	years	100	100	100	100	100	100
	phd	100	100	100	100	100	100
	evaluati	100	100	100	100	100	100
	articles	100	100	100	100	100	100
	gender	100	100	100	100	100	100

Model Summary

Model

R Square

Adjusted R Square

Std. Error of the Estimate

.974a

.948

.945

3014.94701

a. Predictors: (Constant), gender, years, phd, evaluati, articles

b. Dependent Variable: salary

ANOVAb

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1.6E+010	5	3127260664	344.037	.000a
	Residual	8.5E+008	94	9089905.455
	Total	1.6E+010	99	9089905.455

a. Predictors: (Constant), gender, years, phd, evaluati, articles

b. Dependent Variable: salary

Coefficientsa

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	-5916.189	3140.847	.755	-1.884	.063
	years	1021.696	48.926	.755	20.883	.000
	phd	725.748	961.524	.020	.755	.452
	evaluati	3728.939	619.822	.156	6.016	.000
	articles	439.148	80.693	.202	5.442	.000
	gender	1089.720	631.980	.041	1.724	.088

a. Dependent Variable: salary

Residuals Statisticsa

	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	21557.234	68157.203	44889.790	12567.51597	100
Residual	-7197.878	7003.2856	.00000	2937.82561	100
Std. Predicted Value	-1.857	1.851	.000	1.000	100
Std. Residual	-2.387	2.323	.000	.974	100

a. Dependent Variable: salary

Reference no: EM13347951

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Part -1make sure you read the output carefully this output

Reference no: EM13347951

ANOVAb

Reference no: EM13347951

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