SOMDEEP SEN; Business Analyst: Trimax Analytics

(e) somdeepenggmba@gmail.com; (p): 09748229123

LinkedIn: http://linkd.in/1ifqs3x

The Data set contains:

Performance of 400 elementary schools from the California Department of Education

Factors like class-size, parent education, student performance, etc.

Objectives:

To find the factors having major influence on the academic performance

To predict academic performance of an school using those factors

Note: Factors have been chosen based on statistical significance

Factors Impact

English language learners(ELL) Negative

Percentage first year in school (Mobility) Negative

Parent grad school (grad_sch) Positive

Percentage full credential (Full) Positive

Average Class size 4-6 (ACS_46) Positive

Variable Label Parameter

Intercept Intercept 459.71

ell english language learners -2.90

mobility pct 1st year in school -3.11

acs_46 avg class size 4-6 3.69

grad_sch parent grad school 3.38

full pct full credential 2.33

Regression Equation

API00= 459.71+ (-2.90)* ell+ (-3.11)*(mobility) + 3.69* acs_46+ grad_sch*(3.38) + full* (2.33)

To view the detailed SAS Code please visit the following link:http://bit.ly/1c08pGE

• ELLs are one of the fastest growing populations in the public schools

• Number of ELLs in CA is healthy due to the geographic location & economic significance

• ELL students come from different backgrounds &face multiple challenges

• But, the main challenge continues to be the problem in communication

Recommendations:

• Provide special coaching to ELLs to ensure that they master English

• Special coaching should be done before they get tested in English in core content areas

• Ensure that all ELL students receive the full range of services

• Improve teacher training opportunities so teachers can understand the needs of ELLs

• Students making non-promotional school changes is known as mobility

• California students, like students in the rest of the U.S., are highly mobile

• Mobility happens due to following reasons:

– Families changing their residences

– School changes initiated by students especially in California

– School changes initiated by schools especially in California

Recommendations:

Families should:

• Attempt to resolve problems at school before initiating transfer

• Make changes between semesters or at the end of the school year

Schools should:

• Counsel students to remain in the school if at all possible

• Prepare in advance for incoming transfers

• Assess the past enrollment history of incoming students

• Assess the number of previous school changes

• Facilitate the transition of new students as soon as they arrive

• Research shows US students spend less than 15% of their time in school

• Therefore parent involvement is as important as the time spend in school

• Checking homework, attending school meetings, influences student performance

• Educated parents finds it easier to get involved than the others

Recommendations:

• Look to ensure that parents with school graduation lie between 65-70%

• Conduct parent interview during the admission of the students

• Also take initiatives to increase parent engagements

• But schools shouldn’t limit a parent’s involvement based on socio-economic status

• Experienced teachers are more effective at raising student

• Experienced teachers are also more likely to be fully credentialed also

• Hence teacher retention could be instrumental in performance improvement

• Shortage of fully credential teachers is a prime reason for low performance & mobility

• Many assume that financial incentive is the silver bullet; but that is only partially true

Recommendations:

• Financial incentives can make schools more attractive to more qualified teachers

• Money is Necessary, But Clearly Not Sufficient

• Teachers often leave due to poor working conditions, and lack of administrative support

• Schools should recruit & develop administrators who can draw on the expertise of teachers

• Improvement in avg. class size in 4-6 grade((ACS_46) tends to improve performance

• ACS_46 can be improved when:

– Mobility is low

– Promotion of student’s from one grade to another is high

• So, it can be said that ACS_46 is an indicator of the overall academic performance

Recommendation:

• Focus should be on all the recommendations mentioned previously to improve ACS_46

• Outliers were found using the proc univariate option & treated accordingly

Before the treatment After the treatment

• This is done to check the overall significance of the model

• H0: independent variables collectively or individually can’t influence the dependent variable

• H1: the independent variables collectively or individually can influence the dependent variable

• If P value>α: H0 can’t be rejected & hence the model is useless

• If P value<α: H0 is rejected & hence some independent can influence the dependent variable

• In this case the P value<α & hence some independent can influence the dependent variable

• This happens when the independent variables are highly interdependent

• Hence the individual impact on the dependent variables can’t be correctly estimated

• The extent of multicolineraity is captured by the variance inflation factor(VIF)

• The final model must have only those variables having VIF ranging from 1.5 to 2

• To control multicolineraity certain variables gets removed based on high VIF

values

• For the rest the significance of the corresponding population parameter

• The P values of the variables are checked for the significance

• Variables having P value>α are not important for the model

• The final model must have variables having P value>α & VIF ranging from 1.5 to 2

• This occurs when the variance of the random error component is not constant

• The White’s test used for the check For Heteroscedasticity

• Null Hypothesis: Model is Homoscedastic

• If P value>α: H0 can’t be rejected & hence the model is Homoscedastic & vice-

versa

• The VIF SPEC option is used to check for the Heteroscedasticity

• Once the model has only the significant variables the o/p file created

• The o/p file contains the predicted & the residual variables

• The residual variables saved in the o/p file for normality

• This is done using the proc univariate with normal option

• Mean absolute percentage error or MAPE captures the overall % error of the model

• Ideally MAPE should be with in 10%

• This captures the proportion variation that can be explained by the linear regression

• Higher the value of R-square, better the explanatory power

• This acts as a measure of goodness of fit of the model

• R- square value should be at least 65% or .65

API00(E3)= C3+C4*D4+C5*D5+C6*D6+C7*D7+C8*D8

OR

API00= 459.71+ (-2.90)* ell+ (-3.11)*(mobility) + 3.69* acs_46+ grad_sch*(3.38) + full* (2.33)