How Socio-economic Factors Affect Graduation Rate in Michigan

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HowSocio-economic Factors Affect Graduation Rate in Michigan

Tableof Contents

ExecutiveSummary…………………………………………………………………….3

Introduction……………………………………………………………………………..3

Modelto Use……………………………………………………………………………..5

Multicollinearityand Economic Model………………………………………………….6

VarianceInflation Factors (VIF)…………………………………………………………7

Heteroscedasticityand Homoscedasticity………………………………………………..8

HypothesisTest……………………………………………………………………………9

Teacher……………………………………………………………………………………10

LocalRevenue…………………………………………………………………………….11

ViolentCrime……………………………………………………………………………11

MEAPMath and Science………………………………………………………………..12

Conclusion………………………………………………………………………………13

Appendix……………………………………………………………………………….. 15

References………………………………………………………………………………..27

ExecutiveSummary

Educationis vital for the productivity future of the workers in Michigan, sodetermining the factors which impact graduation is important to theimminent success of the economy within the state. The researchinvestigates the key socio-economic aspects in the society affectingthe graduation rates of high school. The research will utilizevarious factors like crime rate, poverty level, class size, studentbody, local taxes on schools as well as standardized test scores. Theresults from multiple regression using the city data, school districtfrom the FBI and CCD propose that the level of poverty and crimerates are negatively impacting the graduation rates of high school.Reforms to the public policy and reducing the rate of crime and thelevels of poverty in the Michigan state could improve the rate ofgraduation of high schools and help minimize the chances of the MotorCity losing its important workforce in the auto industry and helpdiversify the future human capital of the Michigan.

Educationis an essential component to the growth and health of the economy, agauge of technological skills level, and productivity level of thefuture work. It is also a reflection of the societal values andnorms. Every parent is concerned, and the prospective business owneris also concerned because the young adults in the current schoolsystem are the future of the economy. Currently, education was at theforefront of the news as well as politics more so with theintroduction of the No Child Left Behind Act (NCLB) 15 years ago. Asthe concern for our youth’s education increases and public policyfocuses more on ways to improve the quality and outcome of thesecondary and primary education system, it is vital for us as anation, to find out the socio-economic status factors which influencethe ability of the youth to graduate from the high school.

Understandingthe variables which affect high school graduation will enable us toact in a manner which is beneficial to the society, creating policieswhich will boost education and eliminate the reasons for thestudents’ dropout rate. Moreover, we will be able to evaluate thefactors in our neighborhoods and communities which matter most andfocus all the attention on the local level. Within Michigan, it isvital to examine Detroit Metropolitan area and all thesesocio-economic factors so that we can easily find out the policies toenact which will not only boost the economic status but also the rateof graduation rate than the previous year in Michigan.

Moreover,it is imperative to note that the state is the best in the automotivesector, and boosting the graduation level will enable the state tohave enough technological skills necessary for the automotiveindustry in the state. It would create the norms and standards whichwill last beyond the current generation. Education is the future forthe whole community, and it will last beyond next generations tocome. Thus, without this perceived value of the education and itsrole in the state, the economy will flounder. Using data from fortydistricts and over seventy schools as shown in Appendix IV, the paperwill determine the socio-economic factors which are associated withthe rate of graduation in high school within the Metropolitan area.

Focusingon the Metro-Detroit area is essential for the well-being of theMichigan community. The study will help in developing the economichealth of the future of the economy and absent of the expectedexternal outcomes. Increasing the rate of graduation in the secondaryeducation will help in offering solutions to help to maximize thepotential for the human capital in the Detroit area and lessen thecost of losing the important working base and failing to meet theneeds of the high-tech economy. The paper examines the graduationrates in the Metropolitan Detroit and its relationship between thesocio-economic factors level like poverty degree in the district, thecrime rate of the entire city, teacher ratio and the MichiganEducational Assessment Program (MEAP) investigations technicalsubjects like math and science.

Modelto Use

Accordingto Sledge (2016), despite the commitment of the government to provideequity and opportunity for all the students’ family background, atall stages of development of learning the rate of graduation isdetermined by the socio-economic status of a young persona. Thepaper will consider social and economic factors like poverty, andviolent crime as they have an adverse impact on the rate ofgraduation for school students. All these social and economic factorspresent a model as shown below:

Grad= β1 + β2Teachers + β3Caucasians – β4Divorce + β5Females-β6VCrime – β7Poverty + β8Tests + β9Revenues + Ui

Themodel will include factors which I believe that they have asignificant impact on the graduation rate of the students inMetropolitan are in Michigan. Given the time and the scope, it is notpossible to exhaust all the variables of what can be obtained withinthe area of study. All the schools considered for the study werepicked regarding central location around the Metropolitan area andmore so twenty-mile radius. A total of forty schools districts andseventy high schools were considered. All these schools are neithercharter nor magnet schools.

Thecore independent socio-economic variables in this study are violentcrime rate and poverty. However, other factors are added to make thestudy more exhaustive. All these variables considered are continuousand quantitative using the city data and district data. Appendix Ipresent the descriptive statistics and sources for all the variablesin this study while Appendix gives overall data of this study.

Multicollinearityand Economic Model

Theequation below shows the economic model which predicts the teacher,MEAP, Female Local Rev, White, and Teacher impact the public rate ofgraduation. If this model is correct as per many theories, thenVcrime and Free Lunch affect the rate of graduation negatively in thepublic high schools in Michigan Metropolitan area for the school year2016 averagely.

Grad=b1+b2Teacher+ b3White+ b4Female- b5FreeLunch+b6LocalRev-b7VCrime+ b8MEAP+ei

Thestatistical analysis using the model is as shown below:

Yi=1.30+.003Teacheri.067Whitei.646Femalei.395FreeLunchi3.943E6Localrevi7.005Vcrimei+.087MEAPmathscii

TChr’

white

female

Free lunch

Local rev

Vcrime

MEAP

se = (0.322)

(0.006)

(0.049)

(0.471)

(0.082)

(0.000)

(2.545)

(0.093)

t = (4.043)

(0.511)

(-1.374)

(-1.373)

(-4.843)

(-0.648)

(-2.753)

(0.943)

sig = (0.000)

(0.613)

(0.179)

(0.179)

(0.000)

(0.521)

(0.010)

(0.353)

d.f.= 32r2 = 0.785 Adjusted r2 = 0.738

Accordingto Uriel, when all the necessary conditions are applied, theestimators are BLUE, that is they are “Best, Linear, Unbiased,Estimator” (Uriel, 2013). Best, in this case, shows that theestimator has the lowest variance and it is the most reliableestimator. Unbiased estimators are imperative as they produceaveragely correct results. Estimators is a formula which is used toget the point estimate

However,autocorrelation, multicollinearity, and the heteroscedasticity do notfollow the assumption, which is needed for an estimator to beconsidered BLUE. Appendix II show no direct output of the ClassicalLimit Regression Model assumption violations. However, since thesample is large, we can assume that the disturbance term isdistributed generally based on the Central Limit Theorem. Thus wewill conduct VIF and the hypothesis test basing on the assumptionthat U i ~ N.

VarianceInflation Factors (VIF)

Lookingat the Variance Inflation Factors to ascertain if there is anymulticollinearity between the independent variables, it is evidencedthat there is no significant cause to conclude that the regressionsuffers from multicollinearity. It is because the largest VIF valueof the data is 3.458, that is, for Lunch Rate variable. The LunchRate variable is shown to be less than ten showing that once canconclude that there is some collinearity, but it is not significantstatistically:

COR(X i , X j ) = 0 i≠ j

Autocorrelationviolation is not a big deal in this data because the data iscross-sectional and not time series. But, to ensure an accurateanalysis of the model, it will become vital to test the Durbin-WatsonStatistic of 2.007. The null hypothesis is that there is noautocorrelation while alternative hypothesis shows the presence ofautocorrelation.

H0:ρ = 0

H1:ρ ≠ 0

dl= 1.12 &lt du = 1.924 &lt 2.007 &gt 4-du = 2.076 &gt 4-dl = 2.88

Sincethe statistics at 2, indicated no autocorrelation, this test showsthat there is autocorrelation. We thus conclude that at 5% level,COR(U i ,U j ) = 0∀i≠ j

Heteroscedasticityand Homoscedasticity

Next,is that I checked if the error of the data is homoscedastic. Thepresence of heteroscedasticity results from such biased estimates ofthe standard error which makes the hypothesis test to be invalid. Iused each of the independent variables and graphed them against theresiduals squared. These charts look suspicious, which shows apossibility of heteroscedasticity in the model as shown in AppendixIII. Also, to determine the homoscedasticity, Park Tests for all thecontinuous independent variables were used. The test was significantto ascertain the statistical significance of the variance as itvaries from observation to observation. The result is also shown inAppendix III

Finally,there was no evidence that the collected the model was violating anyCLRM needed assumption for the estimates to archive BLUE model. Theresult in Appendix III showed that there was no statisticalsignificant collinearity, heteroscedasticity or autocorrelationbetween the independent variables and the disturbance terms. Themeasurement error in this study was limited, and there was no need tore-run any further regression. It was vital to undertake Hypothesistesting for the significance level of the model measurements and theindependent variables.

HypothesisTest

AppendixII shows the regression output, and it explains 73.8% of the variancein the rate of graduation in high school. It is imperative to conductthe hypothesis test to ensure that the statistical significance andto ascertain of the regression shows 73.8% of the high schoolvariance. The hypotheses are:

H0: R 2= 0

H1:R 2 &gt 0

Theresult was that: F-stat = 16.7while the p-value = 0.00 &lt 0.01&lt 0.05 &lt 0.10

Wewill reject the null hypothesis as a conclusion that there is 0%chance to be observed as an adjusted R2 of the 73.8% if the adjustedpopulation R2 is zero. The regression is statistically significant ata 1% level, and it explains 73.8% of the variance in the rate ofgraduation in high school.

Teacher

Examiningthe “Teacher” the slope is 0.003, which means that for everyadditional student in every teacher, there is 0.003 percentagedecrease in point of graduation rate on an average ceteris paribus. The hypothesis of the linear association between the teacher andgraduation rate is as shown

H0: β2 = 0

H1:β2 ≠ 0

Fromthe result, I got: t-stat = 0.511 and thep-value = 0.613 &gt 0.10&gt 0.05 &gt 0.01

Fromthis, we reject the null hypothesis. It shows that there is asubstantial evidence of the linear association between the percentageof the students who are eligible for a free lunch and the rate ofgraduation in high school at a 1% level of significance. To ascertainthis theory, I conducted a hypothesis test for a statisticallysignificant negative linear relationship between two given variables.

H0: β5 ≥ 0

H1:β5 &lt 0

Theresults were: t-stat = -4.843 while the p-value = 0.00/2 = 0.00 &lt0.01

Thus,I rejected the null hypothesis and concluded that there was evidenceof the strong negative association between the percentage of thestudents eligible for the free lunch and the graduation rate at a 1%significance level. It thus supports the theory.

LocalRevenue

Onthe other hand, examining the “Local Rev,’ one can see it’s theslope is -3.943E-6, which shows that for every increase in the dollarin the local revenue per student, there is a negative 3.943E-6percentage decrease in the rate of high school graduation averagelyceteris paribus. I conducted hypothesis testing of the slope of theLocal Revenue to ascertain its significance of the linear associationbetween it and graduation as follows:

H0: β6 = 0

H1:β6 ≠ 0

Theresults were that the t-stat = -0.648 whereas the p-value = 0.521 &gt0.10 &gt 0.05 &gt 0.01

Thus,we accept the null hypothesis, which there is no evidence of a linearrelationship between the amount of local revenue in real student andhigh graduation rate at a 10% significance level. However, due to thenegative slope of this variable, I suspect that the theory isincorrect and it will be vital to conduct hypothesis testing forstatistically significant negative linear association between twovariables as follows:

H0: β6 ≥ 0

H1:β6 &lt 0

Theresult is that, t-stat = -0.648 while the p-value = 0.521/2 = 0.2605&gt 0.10

Thus,we accept the null hypothesis and conclude that no evidence of theadverse relationship between the local revenue per student and rateof graduation in high school at 10% significance level.

ViolentCrime Rate

Theslope of the “VCrime Rate” is equal to -7.005. It shows that forevery percentage point increase in the violent crime rate in thecity, there is a 7.005 decrease in the high school rate of graduationaveragely. Hence, let’s conduct a hypothesis test of the ViolentCrime slope to ascertain statistical significance of the linearassociation between the Violent Crime and the graduation rate.

H0: β7 = 0

H1:β7 ≠ 0

Itresults to: t-stat = -2.753 and p-value = 0.01 = 0.01 &lt 0.05 &lt0.10

Theresults show that one should reject the null hypothesis as it showsthat there is a substantial evidence of a linear relationship betweenthe graduation rate of high school and violent crime rate in the cityat a 1% significance level. To test the model theory, it’sessential to conduct a hypothesis test for a statistical significancenegative linear relationship between the two variables.

H0: β7 ≥ 0

H1:β7 &lt 0

Itresult to a t-stat equaling to -2.753 and the p-value = 0.01/2 =0.005 &lt 0.01

Wewill reject the null hypothesis as the result shows that there isevidence of a strong negative association between the rate ofgraduation and rate of violent crime at a 1% significance level. Thus, it supports the theoretical model.

MEAPMath and Science

Theperformance of students in math and science classes is oftendetermined by their social status. Thus examining “MEAP Math Sci”with a slope of 0.087 shows that for every percentage point increasein meeting or surpassing the MEAP standards set for math as well asscience there is a 0.087% increase in the rate of high schoolgraduation averagely. Conducting hypothesis of the MEAP math andscience slope result will be important. It is as shown below:

H0: β8 = 0

H1:β8 ≠ 0

Theresult is: t-stat = 0.943 while p-value = 0.353 &gt 0.10 &gt 0.05 &gt0.01

Weaccept the null hypothesis and conclude that there is no evidence ofa linear association between the percent of students meeting orexceeding the MEAP standards for math and science at 10% significancelevel. To test the model theory, we will conduct a hypothesis test,for a statistically significant linear positive relationship betweenthe two independent variables.

H0: β8 ≤ 0

H1:β8 &gt 0

Thus,t-stat = 0.943 and the p-value = 0.353/2 = 0.1765 &gt 0.10

Theabove result shows that the null hypothesis should be accepted whilealternative hypothesis should be rejected. There is no evidence thata positive relationship between the percentage of the studentsmeetings or exceeding the standards of MEAP science including math inthe graduation of students in high school at a significance level of10%.

Conclusion

Thepaper has created a theoretical model which illustrates the differentrates of graduation in the high school in Michigan. The theory hasbeen estimated using SPSS as well as Ordinary Least Squaresregression. Although the economic model shows that 73.8% of the highschool graduation rate variance, most of the independent variableshave no significant effect on the high graduation rate in Michigan.However, the percentage of the female students and white students hasweak negative association with the mean graduation rate. Theseresults oppose the model used in this paper. However, there are twosocio-economic factors which statistically bear a predicting sign.These are violent crime rate and the Free Lunch Rate. These twofactors are mainly proxy of the poverty in surrounding study area.School districts with more poverty and crime rate have lower rates ofgraduation. These results are consistent with the main hypothesisthat poverty and crime pose a serious threat to the attainment ofeducation by children in Michigan.

AppendixI

N

Minimum

Maximum

Mean

Std. Deviation

Grad

40

.5484

.9915

.877603

.1130667

Teacher

40

16.7

24.7

20.298

1.8955

White

40

.0046

.9517

.664658

.2932748

Female

40

.4275

.5245

.489146

.0222691

Free_Lunch_Rate

40

.0245

.8932

.278839

.2111611

Local_Rev

40

989

9820

3621.65

1868.224

vcrime_rate

40

.0006

.0242

.005315

.0052443

MEAP_math_sci

40

.0400

.7775

.416725

.1614550

AppendixII

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

Durbin-Watson

1

.886a

.785

.738

.0578661

2.007

  1. Predictors: (Constant), MEAP_math_sci, Female, Teacher, Local Rev, vcrime rate, White, Free_Lunch_Rate

  2. Dependent variable. Graduation

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression Residual Total

.391

7

.056

16.700

.000a

.107

32

.003

.499

39

  1. Predictors: (Constant), MEAP_math_sci, Female, Teacher, Local_Rev, vcrime_rate, White, Free Lunch Rate

  2. Dependent variable. Graduation

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1 (Constant) Teacher White Female

Free_Lunch_Rate Local_Rev Vcrime_rate

MEAP_math_sci

1.300

.322

4.043

.000

.003

.006

.052

.511

.613

.641

1.559

-.067

.049

-.174

-1.374

.179

.418

2.391

-.646

.471

-.127

-1.373

.179

.781

1.280

-.395

.082

-.738

-4.843

.000

.289

3.458

-3.943E-6

.000

-.065

-.648

.521

.665

1.504

-7.005

2.545

-.325

-2.753

.010

.482

2.074

.087

.093

.125

.943

.353

.384

2.604

AppendixIII: Park Tests

TeacherPark Test

Modelsummary

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.085a

.007

-.019

1.84856

a.Predictors: (Constant), lnTeacher

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

.956

129.853

130.809

1

38

39

.956

3.417

.280

.600a

  1. Predictors: (Constant), lnTeacher

  2. Dependent Variable: lnResSq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnTeacher

-1.973

-1.689

9.604

3.193

-.085

-.205

-.529

.838

.600

  1. Dependent Variable: lnResSq

FemalePark Test

ModelSummary

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.096a

.009

-.017

1.84682

  1. Predictors: (Constant), lnFemale

Anova

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

1.200

129.609

130.809

1

38

39

1.200

3.411

.352

.557a

  1. Predictors: (Constant), lnFemale

  2. Dependent Variable: lnResSq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnFemale

-4.353

3.765

4.555

6.347

.096

-.956

.593

.345

.557

  1. Dependent Variable: lnResSq

FreeLunch Rate Park Test

ModelSummary

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.137a

.019

-.007

1.83783

  1. Predictors: (Constant), lnFree_Lunch_Rate

Anova

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

2.459

128.350

130.809

1

38

39

2.459

3.378

.728

.399a

  1. Predictors: (Constant), lnFree_Lunch_Rate

  2. Dependent Variable: lnResSq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnFree_Lunch_Rate

-6.572

.304

.630

.357

.137

-10.429

.853

.000

.399

  1. Dependent Variable: lnResSq

LocalRevenue Park Test

ModelSummary

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.148a

.022

-.004

1.83502

  1. Predictors: (Constant), lnLocal_Rev

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

2.852

127.957

130.809

1

38

39

2.852

3.367

.847

.363a

  1. Predictors: (Constant), lnLocal_Rev

  2. . Dependent Variable: lnResSq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnLocal_Rev

-11.421

.541

4.759

.588

.148

-2.400

.920

.021

.363

  1. Dependent Variable: lnResSq

ViolentCrime Park Test

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.014a

.000

-.026

1.85517

  1. Predictors: (Constant), lnvcrime_rate

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

.025

130.783

130.809

1

38

39

.025

3.442

.007

.932a

  1. Predictors: (Constant), lnvcrime_rate

  2. Dependent Variable: lnResSq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnvcrime_rate

-6.870

.032

2.101

.374

.014

-3.269

.086

.002

.932

  1. Dependent Variable: lnResSq

MEAPScience &amp Math Park Test

ModelSummary

Model

R

R Square

Adjusted R Square

Std. Error of the

Estimate

1

.094a

.009

-.017

1.84720

  1. Predictors: (Constant), lnMEAP

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

Residual

Total

1.148

129.661

130.809

1

38

39

1.148

3.412

.336

.565a

  1. Predictors: (Constant), lnMEAP

  2. Dependent variable inResq

Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

1 (Constant)

lnMEAP

-7.350

-.304

.595

.525

-.094

-12.354

-.580

.000

.565

  1. Dependent Variable: lnResSq

AppendixIV: Data Sets

District

City

Grad

Teacher

White

Female

Free Lunch

Rate

Local

Rev

VCrime Rate

MEAP

Math Sci

Residuals

Detroit City School

District

Detroit

0.6516

22.8

0.0176

0.4982

0.5515

1970

0.0242

0.2115

-0.0200

Grosse Pointe

Public Schools

Grosse

Pointe

0.9705

17.6

0.8910

0.4938

0.0292

4017

0.0013

0.7775

-0.0376

South Lake

Schools

St. Clair

Shores

0.9646

20.8

0.7389

0.4832

0.1636

3718

0.0024

0.5195

0.0120

East Detroit Public

Schools

Eastpointe

0.8663

21.3

0.6732

0.4887

0.2563

1654

0.0077

0.3085

-0.0047

Van Dyke Public

Schools

Warren

0.7352

20.6

0.6980

0.4790

0.4930

3213

0.0062

0.3175

-0.0495

Hamtramck Public

Schools

Hamtramck

0.7342

20.9

0.5275

0.4275

0.6044

1009

0.0135

0.3435

-0.0123

Lakeview Public

Schools

St. Clair

Shores

0.9445

20.5

0.9110

0.5245

0.0721

2432

0.0024

0.5485

-0.0127

Roseville

Community

Schools

Roseville

0.9429

24.1

0.7639

0.4472

0.2262

2585

0.0048

0.3875

0.0072

Center Line Public

Schools

Center

Line

0.9489

17.6

0.8279

0.4931

0.2587

4957

0.0028

0.4945

0.0660

Fitzgerald Public

Schools

Warren

0.8756

20.6

0.6408

0.4485

0.4014

4507

0.0062

0.2860

0.0390

Highland Park City

Schools

Detroit

0.5484

19.2

0.0046

0.4965

0.7691

989

0.0242

0.0400

-0.0168

Warren Woods

Public Schools

Warren

0.9904

19.6

0.8675

0.5112

0.1343

4078

0.0062

0.2885

0.1052

Lake Shore Public

Schools

St. Clair

Shores

0.9649

21.4

0.8848

0.5200

0.1152

2298

0.0024

0.4525

0.0251

Warren

Consolidated

Schools

Warren

0.9556

20.2

0.8631

0.5042

0.1966

4495

0.0062

0.4650

0.0746

Hazel Park City

School District

Hazel Park

0.7491

20.6

0.8482

0.5060

0.3148

3334

0.0057

0.3260

-0.0824

Fraser Public

Schools

Fraser

0.9574

18.1

0.8884

0.5091

0.1335

2817

0.0023

0.5205

0.0235

Clintondale

Community

Schools

Clinton

Township

0.8970

21.0

0.5229

0.5012

0.2886

2756

0.0034

0.2000

0.0214

Ferndale Public

Schools

Ferndale

0.9127

19.7

0.4441

0.5165

0.4754

2865

0.0056

0.4800

0.1107

Lamphere Public

Schools

Madison

Heights

0.8883

20.0

0.9141

0.4725

0.1611

9820

0.0023

0.5710

-0.0390

Madison Public

Schools

Madison

Heights

0.7778

21.5

0.7682

0.4992

0.3230

3299

0.0023

0.2725

-0.0823

School District of

the City of Royal

Oak

Royal Oak

0.9878

17.3

0.9078

0.5005

0.1081

5914

0.0019

0.6280

0.0423

Dearborn City

School District

Dearborn

0.9254

19.8

0.9283

0.4678

0.3076

4861

0.0049

0.4450

0.0639

Oak Park City

School District

Oak Park

0.9233

20.4

0.0475

0.4993

0.2240

3553

0.0051

0.4490

-0.0157

MT. Clemens

Community School

District

Mount

Clemens

0.6429

17.9

0.4090

0.4782

0.8932

4393

0.0072

0.3795

0.0112

Melvindale-North

Allen Park Schools

Melvindale

0.9544

21.4

0.6942

0.5000

0.3047

2571

0.0040

0.3405

0.0856

Utica Community

Schools

Total Utica

Area

0.9583

19.7

0.9328

0.4885

0.0661

2896

0.0021

0.5935

-0.0250

Clawson City

School District

Clawson

0.9457

18.6

0.9140

0.4675

0.0964

6022

0.0006

0.4010

-0.0184

Berkley School

District

Berkley

0.9600

18.1

0.8046

0.5000

0.0698

2666

0.0011

0.5650

-0.0235

Lincoln Park Public

Schools

Lincoln

Park

0.8539

23.0

0.8337

0.4873

0.2786

3031

0.0037

0.2975

-0.0255

Allen Park Public

Allen Park

0.9501

22.6

0.9142

0.5166

0.0926

2139

0.0016

0.5365

-0.0163

References

Sledge,C. M. (2016). Socioeconomicstatus and its relationship to educational resources.http://rdw.rowan.edu/cgi/viewcontent.cgi?article=2550&ampcontext=etd

Uriel,E. (2013). 3 Multiple linear regression: estimation andproperties.&nbspparameters,&nbsp1(2),3.http://www.uv.es/~uriel/3%20Multiple%20linear%20regression%20estimation%20and%20properties.pdf