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Running head: PROJECT #2 – MULTIPLE REGRESSION ANALYSIS 1

Project #2 – Multiple Regression Analysis

Francis T. Harten

Long Island University / Post

PROJECT #2 – MULTIPLE REGRESSION ANALYSIS 2

***************************************************************** The following Stata code utilized for EDD 1006 Project #2**

** Multiple Regression Analysis Due March 25th, 2013 **

** Original Stata data set load from class web site of **

** Professor Red Owl with the following added Stata code **

** from Francis T. Harten **

****************************************************************

*Step 1

use "http://myweb.liu.edu/~redowl/data/reportcards200405.dta", clear

*Step 2

codebook, compact

*Step 3

describe

*Step 4

Summarize

*Step 5

tab1 district co enroll enroll000 ppexp ppexp000 povrate attend csize medtchexp

passeng4 passmat4 passeng8 passmat8 passengr passmathr passsocsr gradrate pct4col

pass8 pass4 passrege

*Step 6

regress pct4col enroll000, beta

*Step 7

regress pct4col enroll000, beta

*Step 8


regress pct4col ppexp000, beta

*Step 9

regress pct4col csize, beta

*Step 10

regress pct4col attend, beta

*Step 11

regress pct4col pass4, beta

*Step 12

regress pct4col passregents, beta

*Step 13

regress pct4col medtchexp, beta

*Step 14

regress pct4col co, beta

*Step 15

regress pct4col enroll000 ppexp000 csize attend pass4 passregents medtchexp co, beta

*Step 16

estat vif

correlate pct4col enroll000 ppexp000 csize attend pass4 passregents medtchexp co

*Step 17

*Step 18

graph matrix pct4col enroll000 ppexp000 csize attend pass4 passregents medtchexp co,

half


***************************************************************** The following Stata code produces linear and polynomial **** plots of the depependent variable (pct4col) vs. the **** independent variables specified in Data Analysis **** Project #2 (Multiple Regression). **** This provides visual evidence of the linearity or **** nonlinearity of the relationships between the dependent **** variable and each independent variable. **** Provided by Red Owl for use in EDD 1006 Project #2 **** Francis T. Harten 03-25-13 *****************************************************************

* Step 1* Load the data set from the web.use "http://myweb.liu.edu/~redowl/data/reportcards200405.dta", clear

* Step 2set more off

* Step 3* Turn on feature to place all graphs in tabs of graph window* instead of separate graph windows. (This may not work on Macs, but* that feature is just cosmetic and will not affect the ultimate graphs.)set autotabgraphs on

* Step 4* This drops any graphs that may be in memory from previous analyses.* The "capture" command instructs Stata to ignore errors if no graphs* are already in memory and none need to be dropped.capture graph drop _all

* Step 5* This loop produces the graph for each non-binary independent variable* in the varlist.foreach var of varlist enroll000 ppexp000 csize attend pass4 passregents medtchexp { twoway (lpolyci pct4col `var') (lfit pct4col `var', lcolor(blue)) (sc pct4col `var', msize(small) ) , legend(off) title(`var') scheme(s1color) name(gr_`var') }

* Step 6* This produces the graph for the single binary independent variable,* co, and omits the polynomial line. Any line between two points * (i.e., the averages of the categories of a binary variable) will always* be a straight line, so a polynomial fit would not be meaninful.twoway (lfitci pct4col co) (lfit pct4col co, lcolor(blue)) (sc pct4col co, mcolor(maroon) msize(small) ) , xlabel(1(1)2, valuelabel) legend(off) title(co) scheme(s1color) name(gr_co)

* Step 7 * This produces the first set of combined graphs from the loop above.graph combine gr_enroll000 gr_ppexp000 gr_csize gr_attend, cols(2) title(Review of Linearity) scheme(s1color) name(gr_comb1)

* Step 8* This produces the second set of combined graphs from the loop above.graph combine gr_pass4 gr_passregents gr_medtchexp gr_co, cols(2) title(Review of Linearity) scheme(s1color) name(gr_comb2)

* Step 9* This shows the nonlinear relationship between pct4col and ppexp * for all districts.twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12.8 19)) (lfit pct4col ppexp000 if ppexp000>=19 & ppexp000<=22, lcolor(blue)


range(19 22)) (lfit pct4col ppexp000 if ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col ppexp000, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Regression Lines - All Districts) scheme(s1color) name(gr_ppexp000all)

* Step 10* This adds an overall linear regression line to the polynomical and segmented regression linestwoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12.8 25)) (lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12.8 19)) (lfit pct4col ppexp000 if ppexp000>=19 & ppexp000<=22, lcolor(blue) range(19 22)) (lfit pct4col ppexp000 if ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col ppexp000, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Regression Lines - All Districts) scheme(s1color) name(gr_ppexp000all2)

* Step 11* This shows the nonlinear relationship between pct4col and ppexp * for Suffolk County districts.preservekeep if co==1twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12 25)) (lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12 19)) (lfit pct4col ppexp000 if ppexp000>=22, lcolor(blue) range(22 26)) (sc pct4col ppexp000, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(20(10)100) xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Suffolk County) scheme(s1color) name(gr_ppexp000suf)restore

* Step 12* This shows the nonlinear relationship between pct4col and ppexp * for Nassau County districts.preservekeep if co==2twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12 25)) (lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12 19)) (lfit pct4col ppexp000 if ppexp000>=19 & ppexp000<=22, lcolor(blue) range(19 22)) (lfit pct4col ppexp000 if ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col ppexp000, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Nassau County) scheme(s1color) name(gr_ppexp000nas)restore

* Step 13* This shows the nonlinear relationship between pct4col and csize * for all districts.twoway (lpolyci pct4col csize) (lfit pct4col csize if csize<=20, lcolor(blue) range(16 20)) (lfit pct4col csize if csize>=20 & csize<=23, lcolor(blue) range(20 23)) (lfit pct4col csize if csize>=23, lcolor(blue) range(23 25)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(csize) xlabel(16(2)26) xline(20) xline(23) legend(off) title(Regression Lines - All Districts) scheme(s1color) name(gr_csizeall)

* Step 14* This adds an overall linear regression line to the polynomical and segmented regression linestwoway (lpolyci pct4col csize) (lfit pct4col csize, lcolor(red) range(16 25)) (lfit pct4col csize if csize<=20, lcolor(blue) range(16 20)) (lfit pct4col csize if csize>=20 & csize<=23, lcolor(blue) range(20 23)) (lfit pct4col csize if csize>=23, lcolor(blue) range(23 25)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100)


xtitle(csize) xlabel(16(2)26) xline(20) xline(23) legend(off) title(Regression Lines - All Districts) scheme(s1color) name(gr_csizeall2)

* Step 15* This shows the nonlinear relationship between pct4col and csize* for Suffolk County districts.preservekeep if co==1twoway (lpolyci pct4col csize) (lfit pct4col csize, lcolor(red) range(16 25)) (lfit pct4col csize if csize<=20 & csize<=24, lcolor(blue) range(16 20)) (lfit pct4col csize if csize>=20 & csize<=24, lcolor(blue) range(20 24)) (lfit pct4col csize if csize>=24, lcolor(blue) range(24 25)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(csize) xlabel(16(2)26) xline(20) xline(24) legend(off) title(Regression Lines - Suffolk County) scheme(s1color) name(gr_csizesuf)restore

* Step 16* This shows the nonlinear relationship between pct4col and csize* for Nassau County districts.preservekeep if co==2twoway (lpolyci pct4col csize) (lfit pct4col csize, lcolor(red) range(18 23)) (lfit pct4col csize if csize<=20, lcolor(blue) range(18 20)) (lfit pct4col csize if csize>=20, lcolor(blue) range(20 23)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(csize) xlabel(18(2)24) xline(20) legend(off) title(Regression Lines - Nassau County) scheme(s1color) name(gr_csizenas)restore


___ ____ ____ ____ ____ (R)

/__ / ____/ / ____/

___/ / /___/ / /___/ 12.1 Copyright 1985-2011 StataCorp LP

Statistics/Data Analysis StataCorp

4905 Lakeway Drive

College Station, Texas 77845 USA

800-STATA-PC http://www.stata.com

979-696-4600 [email protected]

979-696-4601 (fax)

Single-user Stata perpetual license:

Serial number: 30120594666

Licensed to: Francis T. harten

Long Island University

Notes:

. use "http://datalibrary.us/reportcards200405.dta", clear

. codebook, compact

Variable Obs Unique Mean Min Max Label

--------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

------------------

district 92 92 . . . School District Name

co 92 2 1.434783 1 2 County

enroll 92 92 4627.261 249 17158 Enrollment

enroll000 92 92 4.627261 .249 17.158 Enrollment in 1000s

ppexp 92 91 16927.52 12799 25022 Per Pupil Expenditures

ppexp000 92 91 16.92752 12.799 25.022 Per Pupil Expenditures in $1000s

povrate 92 33 12.93478 0 89 Poverty Rate


attend 92 8 94.93478 90 97 Average Percent Attendance

csize 92 10 21.47826 16 25 Average Class Size

medtchexp 92 12 11.15217 7 18 Median Teacher Workforce

Experience in Years

passeng4 92 34 83.72826 52 99 4th Grade Pass Rate Eng Regents

passmat4 92 21 93.38043 72 100 4th Grade Pass Rate Math Regents

passeng8 92 42 66.97826 18 90 8th Grade Pass Rate Eng Regents

passmat8 92 39 74.23913 23 93 8th Grade Pass Rate Math Regents

passengr 92 31 87.5 30 100 English Regents Pass Rate

passmathr 92 34 86.05435 21 99 Math Regents Pass Rate

passsocsr 92 34 86.52174 24 100 Social Studies Regents Pass Rate

gradrate 92 39 84.05435 33 100 Graduation Rate

pct4col 92 43 63.26087 11 100 % Grads Who Go to College in Year

after Graduation

pass8 92 61 70.6087 22 91 Avg Pass Rate 8th Grade Eng &

Math Regents

pass4 92 41 88.55435 64 99.5 Avg Pass Rate 4th Grade Eng &

Math Regents

passregents 92 60 86.69203 25 99.66666 Avg Pass Rate Eng Math SocStud

Regents

--------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

------------------

. describe

Contains data from http://datalibrary.us/reportcards200405.dta

obs: 92

vars: 22 29 Feb 2012 12:35

size: 6,256


--------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

------------------

storage display value

variable name type format label variable label

--------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

------------------

district str25 %25s School District Name

co byte %11.0g coval County

enroll int %8.0g Enrollment

enroll000 float %9.0g Enrollment in 1000s

ppexp long %8.0g Per Pupil Expenditures

ppexp000 float %9.0g Per Pupil Expenditures in $1000s

povrate byte %8.0g Poverty Rate

attend byte %8.0g Average Percent Attendance

csize byte %8.0g Average Class Size

medtchexp float %9.0g Median Teacher Workforce Experience in

Years

passeng4 byte %8.0g 4th Grade Pass Rate Eng Regents

passmat4 byte %8.0g 4th Grade Pass Rate Math Regents

passeng8 byte %8.0g 8th Grade Pass Rate Eng Regents

passmat8 byte %8.0g 8th Grade Pass Rate Math Regents

passengr byte %8.0g English Regents Pass Rate

passmathr byte %8.0g Math Regents Pass Rate

passsocsr byte %8.0g Social Studies Regents Pass Rate

gradrate byte %8.0g Graduation Rate

pct4col byte %8.0g % Grads Who Go to College in Year after

Graduation

pass8 float %9.0g Avg Pass Rate 8th Grade Eng & Math

Regents


pass4 float %9.0g Avg Pass Rate 4th Grade Eng & Math

Regents

passregents float %9.0g Avg Pass Rate Eng Math SocStud Regents

--------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

------------------

Sorted by:

. summarize

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

district | 0

co | 92 1.434783 .4984448 1 2

enroll | 92 4627.261 3024.397 249 17158

enroll000 | 92 4.627261 3.024397 .249 17.158

ppexp | 92 16927.52 3152.303 12799 25022

-------------+--------------------------------------------------------

ppexp000 | 92 16.92752 3.152303 12.799 25.022

povrate | 92 12.93478 16.82476 0 89

attend | 92 94.93478 1.412692 90 97

csize | 92 21.47826 1.680386 16 25

medtchexp | 92 11.15217 2.467079 7 18

-------------+--------------------------------------------------------

passeng4 | 92 83.72826 9.032424 52 99

passmat4 | 92 93.38043 5.706835 72 100

passeng8 | 92 66.97826 15.25244 18 90

passmat8 | 92 74.23913 16.04757 23 93

passengr | 92 87.5 11.80566 30 100

-------------+--------------------------------------------------------

passmathr | 92 86.05435 13.03787 21 99

passsocsr | 92 86.52174 12.48338 24 100


gradrate | 92 84.05435 12.98551 33 100

pct4col | 92 63.26087 16.19311 11 100

pass8 | 92 70.6087 15.23819 22 91

-------------+--------------------------------------------------------

pass4 | 92 88.55435 7.105352 64 99.5

passregents | 92 86.69203 12.19142 25 99.66666

. tab1 district co enroll enroll000 ppexp ppexp000 povrate attend csize medtchexp

passeng4 passmat4 passeng8 passmat8 passengr passmathr passsocsr gradrate

pct4col pass8 pass4 passrege

> nts

-> tabulation of district

School District Name | Freq. Percent Cum.

--------------------------+-----------------------------------

Amityville | 1 1.09 1.09

Babylon | 1 1.09 2.17

Baldwin | 1 1.09 3.26

Bay Shore | 1 1.09 4.35

Bayport-Blue Point | 1 1.09 5.43

Bethpage | 1 1.09 6.52

Brentwood | 1 1.09 7.61

Carle Place | 1 1.09 8.70

Center Moriches | 1 1.09 9.78

Central Islip | 1 1.09 10.87

Cold Spring Harbor | 1 1.09 11.96

Commack | 1 1.09 13.04

Comsewogue | 1 1.09 14.13

Connetquot | 1 1.09 15.22

Copiague | 1 1.09 16.30

Deer Park | 1 1.09 17.39


1266 | 1 1.09 5.43

1267 | 1 1.09 6.52

1386 | 1 1.09 7.61

1465 | 1 1.09 8.70

1628 | 1 1.09 9.78

1663 | 1 1.09 10.87

1711 | 1 1.09 11.96

1730 | 1 1.09 13.04

1752 | 1 1.09 14.13

1833 | 1 1.09 15.22

1957 | 1 1.09 16.30

1975 | 1 1.09 17.39

2132 | 1 1.09 18.48

2254 | 1 1.09 19.57

2284 | 1 1.09 20.65

2372 | 1 1.09 21.74

2437 | 1 1.09 22.83

2537 | 1 1.09 23.91

2606 | 1 1.09 25.00

2702 | 1 1.09 26.09

2750 | 1 1.09 27.17

2755 | 1 1.09 28.26

2819 | 1 1.09 29.35

2843 | 1 1.09 30.43

2851 | 1 1.09 31.52

2865 | 1 1.09 32.61

2945 | 1 1.09 33.70

3035 | 1 1.09 34.78

3063 | 1 1.09 35.87

3104 | 1 1.09 36.96

3140 | 1 1.09 38.04

3219 | 1 1.09 39.13


3283 | 1 1.09 40.22

3355 | 1 1.09 41.30

3535 | 1 1.09 42.39

3553 | 1 1.09 43.48

3589 | 1 1.09 44.57

3617 | 1 1.09 45.65

3622 | 1 1.09 46.74

3647 | 1 1.09 47.83

3660 | 1 1.09 48.91

3662 | 1 1.09 50.00

3946 | 1 1.09 51.09

4013 | 1 1.09 52.17

4077 | 1 1.09 53.26

4126 | 1 1.09 54.35

4160 | 1 1.09 55.43

4203 | 1 1.09 56.52

4212 | 1 1.09 57.61

4399 | 1 1.09 58.70

4483 | 1 1.09 59.78

4786 | 1 1.09 60.87

4787 | 1 1.09 61.96

4801 | 1 1.09 63.04

4896 | 1 1.09 64.13

4911 | 1 1.09 65.22

4999 | 1 1.09 66.30

5161 | 1 1.09 67.39

5309 | 1 1.09 68.48

5472 | 1 1.09 69.57

5482 | 1 1.09 70.65

5811 | 1 1.09 71.74

5874 | 1 1.09 72.83

6137 | 1 1.09 73.91


6189 | 1 1.09 75.00

6242 | 1 1.09 76.09

6323 | 1 1.09 77.17

6410 | 1 1.09 78.26

6453 | 1 1.09 79.35

6475 | 1 1.09 80.43

6677 | 1 1.09 81.52

6913 | 1 1.09 82.61

6951 | 1 1.09 83.70

7125 | 1 1.09 84.78

7482 | 1 1.09 85.87

7561 | 1 1.09 86.96

7972 | 1 1.09 88.04

7987 | 1 1.09 89.13

8004 | 1 1.09 90.22

8353 | 1 1.09 91.30

9144 | 1 1.09 92.39

9745 | 1 1.09 93.48

9974 | 1 1.09 94.57

10191 | 1 1.09 95.65

10541 | 1 1.09 96.74

11520 | 1 1.09 97.83

15528 | 1 1.09 98.91

17158 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of enroll000

Enrollment |

in 1000s | Freq. Percent Cum.

------------+-----------------------------------


.249 | 1 1.09 1.09

.679 | 1 1.09 2.17

.934 | 1 1.09 3.26

1.014 | 1 1.09 4.35

1.266 | 1 1.09 5.43

1.267 | 1 1.09 6.52

1.386 | 1 1.09 7.61

1.465 | 1 1.09 8.70

1.628 | 1 1.09 9.78

1.663 | 1 1.09 10.87

1.711 | 1 1.09 11.96

1.73 | 1 1.09 13.04

1.752 | 1 1.09 14.13

1.833 | 1 1.09 15.22

1.957 | 1 1.09 16.30

1.975 | 1 1.09 17.39

2.132 | 1 1.09 18.48

2.254 | 1 1.09 19.57

2.284 | 1 1.09 20.65

2.372 | 1 1.09 21.74

2.437 | 1 1.09 22.83

2.537 | 1 1.09 23.91

2.606 | 1 1.09 25.00

2.702 | 1 1.09 26.09

2.75 | 1 1.09 27.17

2.755 | 1 1.09 28.26

2.819 | 1 1.09 29.35

2.843 | 1 1.09 30.43

2.851 | 1 1.09 31.52

2.865 | 1 1.09 32.61

2.945 | 1 1.09 33.70

3.035 | 1 1.09 34.78


3.063 | 1 1.09 35.87

3.104 | 1 1.09 36.96

3.14 | 1 1.09 38.04

3.219 | 1 1.09 39.13

3.283 | 1 1.09 40.22

3.355 | 1 1.09 41.30

3.535 | 1 1.09 42.39

3.553 | 1 1.09 43.48

3.589 | 1 1.09 44.57

3.617 | 1 1.09 45.65

3.622 | 1 1.09 46.74

3.647 | 1 1.09 47.83

3.66 | 1 1.09 48.91

3.662 | 1 1.09 50.00

3.946 | 1 1.09 51.09

4.013 | 1 1.09 52.17

4.077 | 1 1.09 53.26

4.126 | 1 1.09 54.35

4.16 | 1 1.09 55.43

4.203 | 1 1.09 56.52

4.212 | 1 1.09 57.61

4.399 | 1 1.09 58.70

4.483 | 1 1.09 59.78

4.786 | 1 1.09 60.87

4.787 | 1 1.09 61.96

4.801 | 1 1.09 63.04

4.896 | 1 1.09 64.13

4.911 | 1 1.09 65.22

4.999 | 1 1.09 66.30

5.161 | 1 1.09 67.39

5.309 | 1 1.09 68.48

5.472 | 1 1.09 69.57


5.482 | 1 1.09 70.65

5.811 | 1 1.09 71.74

5.874 | 1 1.09 72.83

6.137 | 1 1.09 73.91

6.189 | 1 1.09 75.00

6.242 | 1 1.09 76.09

6.323 | 1 1.09 77.17

6.41 | 1 1.09 78.26

6.453 | 1 1.09 79.35

6.475 | 1 1.09 80.43

6.677 | 1 1.09 81.52

6.913 | 1 1.09 82.61

6.951 | 1 1.09 83.70

7.125 | 1 1.09 84.78

7.482 | 1 1.09 85.87

7.561 | 1 1.09 86.96

7.972 | 1 1.09 88.04

7.987 | 1 1.09 89.13

8.004 | 1 1.09 90.22

8.353 | 1 1.09 91.30

9.144 | 1 1.09 92.39

9.745 | 1 1.09 93.48

9.974 | 1 1.09 94.57

10.191 | 1 1.09 95.65

10.541 | 1 1.09 96.74

11.52 | 1 1.09 97.83

15.528 | 1 1.09 98.91

17.158 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of ppexp


Per Pupil |

Expenditure |

s | Freq. Percent Cum.

------------+-----------------------------------

12799 | 1 1.09 1.09

12814 | 1 1.09 2.17

12863 | 1 1.09 3.26

13086 | 1 1.09 4.35

13148 | 1 1.09 5.43

13227 | 1 1.09 6.52

13229 | 1 1.09 7.61

13295 | 1 1.09 8.70

13433 | 1 1.09 9.78

13467 | 1 1.09 10.87

13594 | 1 1.09 11.96

13633 | 1 1.09 13.04

13775 | 1 1.09 14.13

13780 | 1 1.09 15.22

13795 | 1 1.09 16.30

14023 | 1 1.09 17.39

14126 | 1 1.09 18.48

14194 | 1 1.09 19.57

14225 | 1 1.09 20.65

14274 | 1 1.09 21.74

14398 | 1 1.09 22.83

14456 | 1 1.09 23.91

14487 | 1 1.09 25.00

14494 | 1 1.09 26.09

14526 | 1 1.09 27.17

14596 | 1 1.09 28.26

14651 | 1 1.09 29.35


14704 | 1 1.09 30.43

14707 | 1 1.09 31.52

14822 | 1 1.09 32.61

14841 | 1 1.09 33.70

14993 | 1 1.09 34.78

15034 | 1 1.09 35.87

15142 | 1 1.09 36.96

15211 | 1 1.09 38.04

15236 | 1 1.09 39.13

15362 | 1 1.09 40.22

15466 | 1 1.09 41.30

15573 | 1 1.09 42.39

15607 | 1 1.09 43.48

15636 | 1 1.09 44.57

15647 | 1 1.09 45.65

15757 | 1 1.09 46.74

16122 | 1 1.09 47.83

16148 | 1 1.09 48.91

16227 | 1 1.09 50.00

16466 | 1 1.09 51.09

16502 | 1 1.09 52.17

16530 | 1 1.09 53.26

16663 | 1 1.09 54.35

16932 | 1 1.09 55.43

17000 | 1 1.09 56.52

17003 | 1 1.09 57.61

17081 | 1 1.09 58.70

17204 | 1 1.09 59.78

17324 | 1 1.09 60.87

17325 | 2 2.17 63.04

17368 | 1 1.09 64.13

17370 | 1 1.09 65.22


17513 | 1 1.09 66.30

17552 | 1 1.09 67.39

17615 | 1 1.09 68.48

17768 | 1 1.09 69.57

17833 | 1 1.09 70.65

17916 | 1 1.09 71.74

18020 | 1 1.09 72.83

18027 | 1 1.09 73.91

18178 | 1 1.09 75.00

18388 | 1 1.09 76.09

18404 | 1 1.09 77.17

18864 | 1 1.09 78.26

19181 | 1 1.09 79.35

19434 | 1 1.09 80.43

19634 | 1 1.09 81.52

19688 | 1 1.09 82.61

19917 | 1 1.09 83.70

21038 | 1 1.09 84.78

21296 | 1 1.09 85.87

21664 | 1 1.09 86.96

21695 | 1 1.09 88.04

21705 | 1 1.09 89.13

21910 | 1 1.09 90.22

22245 | 1 1.09 91.30

22301 | 1 1.09 92.39

22419 | 1 1.09 93.48

22529 | 1 1.09 94.57

23162 | 1 1.09 95.65

24063 | 1 1.09 96.74

24654 | 1 1.09 97.83

24981 | 1 1.09 98.91

25022 | 1 1.09 100.00


------------+-----------------------------------

Total | 92 100.00

-> tabulation of ppexp000

Per Pupil |

Expenditure |

s in $1000s | Freq. Percent Cum.

------------+-----------------------------------

12.799 | 1 1.09 1.09

12.814 | 1 1.09 2.17

12.863 | 1 1.09 3.26

13.086 | 1 1.09 4.35

13.148 | 1 1.09 5.43

13.227 | 1 1.09 6.52

13.229 | 1 1.09 7.61

13.295 | 1 1.09 8.70

13.433 | 1 1.09 9.78

13.467 | 1 1.09 10.87

13.594 | 1 1.09 11.96

13.633 | 1 1.09 13.04

13.775 | 1 1.09 14.13

13.78 | 1 1.09 15.22

13.795 | 1 1.09 16.30

14.023 | 1 1.09 17.39

14.126 | 1 1.09 18.48

14.194 | 1 1.09 19.57

14.225 | 1 1.09 20.65

14.274 | 1 1.09 21.74

14.398 | 1 1.09 22.83

14.456 | 1 1.09 23.91

14.487 | 1 1.09 25.00


14.494 | 1 1.09 26.09

14.526 | 1 1.09 27.17

14.596 | 1 1.09 28.26

14.651 | 1 1.09 29.35

14.704 | 1 1.09 30.43

14.707 | 1 1.09 31.52

14.822 | 1 1.09 32.61

14.841 | 1 1.09 33.70

14.993 | 1 1.09 34.78

15.034 | 1 1.09 35.87

15.142 | 1 1.09 36.96

15.211 | 1 1.09 38.04

15.236 | 1 1.09 39.13

15.362 | 1 1.09 40.22

15.466 | 1 1.09 41.30

15.573 | 1 1.09 42.39

15.607 | 1 1.09 43.48

15.636 | 1 1.09 44.57

15.647 | 1 1.09 45.65

15.757 | 1 1.09 46.74

16.122 | 1 1.09 47.83

16.148 | 1 1.09 48.91

16.227 | 1 1.09 50.00

16.466 | 1 1.09 51.09

16.502 | 1 1.09 52.17

16.53 | 1 1.09 53.26

16.663 | 1 1.09 54.35

16.932 | 1 1.09 55.43

17 | 1 1.09 56.52

17.003 | 1 1.09 57.61

17.081 | 1 1.09 58.70

17.204 | 1 1.09 59.78


17.324 | 1 1.09 60.87

17.325 | 2 2.17 63.04

17.368 | 1 1.09 64.13

17.37 | 1 1.09 65.22

17.513 | 1 1.09 66.30

17.552 | 1 1.09 67.39

17.615 | 1 1.09 68.48

17.768 | 1 1.09 69.57

17.833 | 1 1.09 70.65

17.916 | 1 1.09 71.74

18.02 | 1 1.09 72.83

18.027 | 1 1.09 73.91

18.178 | 1 1.09 75.00

18.388 | 1 1.09 76.09

18.404 | 1 1.09 77.17

18.864 | 1 1.09 78.26

19.181 | 1 1.09 79.35

19.434 | 1 1.09 80.43

19.634 | 1 1.09 81.52

19.688 | 1 1.09 82.61

19.917 | 1 1.09 83.70

21.038 | 1 1.09 84.78

21.296 | 1 1.09 85.87

21.664 | 1 1.09 86.96

21.695 | 1 1.09 88.04

21.705 | 1 1.09 89.13

21.91 | 1 1.09 90.22

22.245 | 1 1.09 91.30

22.301 | 1 1.09 92.39

22.419 | 1 1.09 93.48

22.529 | 1 1.09 94.57

23.162 | 1 1.09 95.65


24.063 | 1 1.09 96.74

24.654 | 1 1.09 97.83

24.981 | 1 1.09 98.91

25.022 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of povrate

Poverty |

Rate | Freq. Percent Cum.

------------+-----------------------------------

0 | 4 4.35 4.35

1 | 10 10.87 15.22

2 | 8 8.70 23.91

3 | 5 5.43 29.35

4 | 9 9.78 39.13

5 | 6 6.52 45.65

6 | 5 5.43 51.09

7 | 3 3.26 54.35

8 | 4 4.35 58.70

9 | 5 5.43 64.13

10 | 2 2.17 66.30

11 | 3 3.26 69.57

12 | 3 3.26 72.83

13 | 1 1.09 73.91

14 | 1 1.09 75.00

16 | 1 1.09 76.09

18 | 1 1.09 77.17

19 | 1 1.09 78.26

21 | 1 1.09 79.35

25 | 3 3.26 82.61


26 | 3 3.26 85.87

28 | 1 1.09 86.96

30 | 2 2.17 89.13

34 | 1 1.09 90.22

35 | 1 1.09 91.30

37 | 1 1.09 92.39

41 | 1 1.09 93.48

42 | 1 1.09 94.57

49 | 1 1.09 95.65

58 | 1 1.09 96.74

67 | 1 1.09 97.83

72 | 1 1.09 98.91

89 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of attend

Average |

Percent |

Attendance | Freq. Percent Cum.

------------+-----------------------------------

90 | 1 1.09 1.09

91 | 2 2.17 3.26

92 | 2 2.17 5.43

93 | 6 6.52 11.96

94 | 22 23.91 35.87

95 | 19 20.65 56.52

96 | 33 35.87 92.39

97 | 7 7.61 100.00

------------+-----------------------------------

Total | 92 100.00


-> tabulation of csize

Average |

Class Size | Freq. Percent Cum.

------------+-----------------------------------

16 | 1 1.09 1.09

17 | 1 1.09 2.17

18 | 4 4.35 6.52

19 | 4 4.35 10.87

20 | 12 13.04 23.91

21 | 19 20.65 44.57

22 | 26 28.26 72.83

23 | 17 18.48 91.30

24 | 7 7.61 98.91

25 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of medtchexp

Median |

Teacher |

Workforce |

Experience |

in Years | Freq. Percent Cum.

------------+-----------------------------------

7 | 3 3.26 3.26

8 | 11 11.96 15.22

9 | 14 15.22 30.43

10 | 12 13.04 43.48

11 | 10 10.87 54.35


12 | 18 19.57 73.91

13 | 9 9.78 83.70

14 | 6 6.52 90.22

15 | 3 3.26 93.48

16 | 4 4.35 97.83

17 | 1 1.09 98.91

18 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passeng4

4th Grade |

Pass Rate |

Eng Regents | Freq. Percent Cum.

------------+-----------------------------------

52 | 1 1.09 1.09

56 | 1 1.09 2.17

63 | 1 1.09 3.26

64 | 1 1.09 4.35

65 | 1 1.09 5.43

67 | 1 1.09 6.52

70 | 1 1.09 7.61

71 | 2 2.17 9.78

72 | 2 2.17 11.96

73 | 1 1.09 13.04

74 | 1 1.09 14.13

75 | 2 2.17 16.30

76 | 3 3.26 19.57

78 | 3 3.26 22.83

79 | 2 2.17 25.00

80 | 3 3.26 28.26


81 | 2 2.17 30.43

82 | 4 4.35 34.78

83 | 5 5.43 40.22

84 | 7 7.61 47.83

85 | 2 2.17 50.00

86 | 6 6.52 56.52

87 | 6 6.52 63.04

88 | 5 5.43 68.48

89 | 2 2.17 70.65

90 | 7 7.61 78.26

91 | 6 6.52 84.78

92 | 3 3.26 88.04

93 | 3 3.26 91.30

94 | 1 1.09 92.39

95 | 2 2.17 94.57

96 | 1 1.09 95.65

98 | 3 3.26 98.91

99 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passmat4

4th Grade |

Pass Rate |

Math |

Regents | Freq. Percent Cum.

------------+-----------------------------------

72 | 1 1.09 1.09

79 | 2 2.17 3.26

80 | 2 2.17 5.43

82 | 1 1.09 6.52


83 | 3 3.26 9.78

85 | 2 2.17 11.96

86 | 2 2.17 14.13

87 | 1 1.09 15.22

88 | 1 1.09 16.30

89 | 4 4.35 20.65

90 | 1 1.09 21.74

91 | 2 2.17 23.91

92 | 5 5.43 29.35

93 | 5 5.43 34.78

94 | 8 8.70 43.48

95 | 11 11.96 55.43

96 | 12 13.04 68.48

97 | 9 9.78 78.26

98 | 7 7.61 85.87

99 | 8 8.70 94.57

100 | 5 5.43 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passeng8

8th Grade |

Pass Rate |

Eng Regents | Freq. Percent Cum.

------------+-----------------------------------

18 | 1 1.09 1.09

20 | 1 1.09 2.17

22 | 1 1.09 3.26

30 | 1 1.09 4.35

36 | 1 1.09 5.43

44 | 1 1.09 6.52


45 | 2 2.17 8.70

46 | 2 2.17 10.87

48 | 1 1.09 11.96

50 | 1 1.09 13.04

51 | 2 2.17 15.22

52 | 1 1.09 16.30

54 | 4 4.35 20.65

55 | 1 1.09 21.74

56 | 1 1.09 22.83

58 | 2 2.17 25.00

61 | 2 2.17 27.17

63 | 1 1.09 28.26

64 | 3 3.26 31.52

65 | 5 5.43 36.96

66 | 3 3.26 40.22

67 | 2 2.17 42.39

68 | 2 2.17 44.57

69 | 5 5.43 50.00

70 | 1 1.09 51.09

71 | 4 4.35 55.43

72 | 4 4.35 59.78

73 | 1 1.09 60.87

74 | 3 3.26 64.13

75 | 3 3.26 67.39

76 | 3 3.26 70.65

77 | 5 5.43 76.09

78 | 3 3.26 79.35

79 | 2 2.17 81.52

81 | 4 4.35 85.87

82 | 3 3.26 89.13

83 | 1 1.09 90.22

84 | 1 1.09 91.30


85 | 2 2.17 93.48

86 | 3 3.26 96.74

88 | 1 1.09 97.83

90 | 2 2.17 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passmat8

8th Grade |

Pass Rate |

Math |


------------+-----------------------------------

23 | 1 1.09 1.09

24 | 1 1.09 2.17

26 | 1 1.09 3.26

33 | 1 1.09 4.35

44 | 1 1.09 5.43

46 | 1 1.09 6.52

49 | 1 1.09 7.61

51 | 2 2.17 9.78

52 | 1 1.09 10.87

55 | 3 3.26 14.13

57 | 1 1.09 15.22

58 | 2 2.17 17.39

59 | 1 1.09 18.48

61 | 2 2.17 20.65

63 | 1 1.09 21.74

65 | 2 2.17 23.91

67 | 2 2.17 26.09

70 | 1 1.09 27.17


71 | 4 4.35 31.52

72 | 1 1.09 32.61

73 | 2 2.17 34.78

74 | 4 4.35 39.13

75 | 5 5.43 44.57

76 | 4 4.35 48.91

78 | 2 2.17 51.09

79 | 2 2.17 53.26

80 | 2 2.17 55.43

81 | 4 4.35 59.78

82 | 2 2.17 61.96

84 | 6 6.52 68.48

85 | 8 8.70 77.17

86 | 2 2.17 79.35

87 | 1 1.09 80.43

88 | 4 4.35 84.78

89 | 1 1.09 85.87

90 | 3 3.26 89.13

91 | 1 1.09 90.22

92 | 6 6.52 96.74

93 | 3 3.26 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passengr

English |

Regents |

Pass Rate | Freq. Percent Cum.

------------+-----------------------------------

30 | 1 1.09 1.09

36 | 1 1.09 2.17


54 | 1 1.09 3.26

65 | 1 1.09 4.35

67 | 1 1.09 5.43

68 | 1 1.09 6.52

70 | 1 1.09 7.61

75 | 4 4.35 11.96

77 | 1 1.09 13.04

78 | 1 1.09 14.13

80 | 1 1.09 15.22

81 | 1 1.09 16.30

82 | 2 2.17 18.48

83 | 4 4.35 22.83

84 | 3 3.26 26.09

85 | 1 1.09 27.17

86 | 6 6.52 33.70

87 | 4 4.35 38.04

88 | 3 3.26 41.30

89 | 5 5.43 46.74

90 | 4 4.35 51.09

91 | 3 3.26 54.35

92 | 4 4.35 58.70

93 | 7 7.61 66.30

94 | 5 5.43 71.74

95 | 5 5.43 77.17

96 | 9 9.78 86.96

97 | 5 5.43 92.39

98 | 2 2.17 94.57

99 | 4 4.35 98.91

100 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00


-> tabulation of passmathr

Math |

Regents |


------------+-----------------------------------

21 | 1 1.09 1.09

43 | 1 1.09 2.17

44 | 1 1.09 3.26

46 | 1 1.09 4.35

57 | 1 1.09 5.43

68 | 1 1.09 6.52

70 | 1 1.09 7.61

71 | 1 1.09 8.70

72 | 1 1.09 9.78

73 | 1 1.09 10.87

74 | 1 1.09 11.96

75 | 1 1.09 13.04

77 | 2 2.17 15.22

79 | 1 1.09 16.30

80 | 3 3.26 19.57

81 | 1 1.09 20.65

82 | 3 3.26 23.91

83 | 4 4.35 28.26

84 | 2 2.17 30.43

85 | 2 2.17 32.61

86 | 4 4.35 36.96

87 | 2 2.17 39.13

88 | 3 3.26 42.39

89 | 7 7.61 50.00

90 | 3 3.26 53.26

91 | 5 5.43 58.70


92 | 5 5.43 64.13

93 | 3 3.26 67.39

94 | 8 8.70 76.09

95 | 9 9.78 85.87

96 | 8 8.70 94.57

97 | 2 2.17 96.74

98 | 2 2.17 98.91

99 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passsocsr

Social |

Studies |

Regents |


------------+-----------------------------------

24 | 1 1.09 1.09

38 | 1 1.09 2.17

47 | 1 1.09 3.26

61 | 1 1.09 4.35

66 | 2 2.17 6.52

67 | 1 1.09 7.61

72 | 1 1.09 8.70

74 | 2 2.17 10.87

75 | 1 1.09 11.96

76 | 1 1.09 13.04

77 | 2 2.17 15.22

78 | 1 1.09 16.30

79 | 1 1.09 17.39

80 | 2 2.17 19.57


81 | 1 1.09 20.65

82 | 2 2.17 22.83

83 | 2 2.17 25.00

84 | 4 4.35 29.35

85 | 4 4.35 33.70

86 | 2 2.17 35.87

87 | 3 3.26 39.13

88 | 5 5.43 44.57

89 | 2 2.17 46.74

90 | 5 5.43 52.17

91 | 3 3.26 55.43

92 | 9 9.78 65.22

93 | 4 4.35 69.57

94 | 5 5.43 75.00

95 | 10 10.87 85.87

96 | 4 4.35 90.22

97 | 4 4.35 94.57

98 | 1 1.09 95.65

99 | 2 2.17 97.83

100 | 2 2.17 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of gradrate

Graduation |

Rate | Freq. Percent Cum.

------------+-----------------------------------

33 | 1 1.09 1.09

39 | 1 1.09 2.17

43 | 1 1.09 3.26

52 | 1 1.09 4.35


53 | 1 1.09 5.43

65 | 1 1.09 6.52

66 | 1 1.09 7.61

67 | 1 1.09 8.70

68 | 1 1.09 9.78

69 | 2 2.17 11.96

72 | 1 1.09 13.04

73 | 2 2.17 15.22

74 | 1 1.09 16.30

75 | 4 4.35 20.65

76 | 1 1.09 21.74

77 | 1 1.09 22.83

78 | 2 2.17 25.00

79 | 2 2.17 27.17

80 | 1 1.09 28.26

81 | 2 2.17 30.43

82 | 3 3.26 33.70

83 | 1 1.09 34.78

84 | 3 3.26 38.04

85 | 3 3.26 41.30

86 | 3 3.26 44.57

87 | 5 5.43 50.00

88 | 2 2.17 52.17

89 | 5 5.43 57.61

90 | 6 6.52 64.13

91 | 4 4.35 68.48

92 | 4 4.35 72.83

93 | 6 6.52 79.35

94 | 6 6.52 85.87

95 | 2 2.17 88.04

96 | 4 4.35 92.39

97 | 1 1.09 93.48


98 | 2 2.17 95.65

99 | 3 3.26 98.91

100 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of pct4col

% Grads Who |

Go to |

College in |

Year after |

Graduation | Freq. Percent Cum.

------------+-----------------------------------

11 | 1 1.09 1.09

31 | 2 2.17 3.26

38 | 2 2.17 5.43

41 | 3 3.26 8.70

45 | 5 5.43 14.13

46 | 1 1.09 15.22

48 | 1 1.09 16.30

49 | 2 2.17 18.48

50 | 2 2.17 20.65

51 | 3 3.26 23.91

54 | 3 3.26 27.17

55 | 5 5.43 32.61

56 | 5 5.43 38.04

57 | 1 1.09 39.13

58 | 1 1.09 40.22

59 | 2 2.17 42.39

60 | 2 2.17 44.57

61 | 2 2.17 46.74


62 | 3 3.26 50.00

63 | 3 3.26 53.26

64 | 2 2.17 55.43

65 | 2 2.17 57.61

66 | 2 2.17 59.78

67 | 3 3.26 63.04

69 | 3 3.26 66.30

70 | 2 2.17 68.48

71 | 2 2.17 70.65

73 | 3 3.26 73.91

75 | 3 3.26 77.17

77 | 2 2.17 79.35

78 | 2 2.17 81.52

79 | 3 3.26 84.78

82 | 1 1.09 85.87

84 | 1 1.09 86.96

85 | 2 2.17 89.13

86 | 1 1.09 90.22

87 | 1 1.09 91.30

88 | 2 2.17 93.48

89 | 1 1.09 94.57

90 | 2 2.17 96.74

91 | 1 1.09 97.83

95 | 1 1.09 98.91

100 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of pass8

Avg Pass |

Rate 8th |


Grade Eng & |

Math |


------------+-----------------------------------

22 | 1 1.09 1.09

23 | 1 1.09 2.17

26.5 | 2 2.17 4.35

42.5 | 1 1.09 5.43

45.5 | 1 1.09 6.52

46 | 1 1.09 7.61

48.5 | 1 1.09 8.70

50 | 1 1.09 9.78

51 | 2 2.17 11.96

52 | 1 1.09 13.04

52.5 | 1 1.09 14.13

54.5 | 2 2.17 16.30

56.5 | 1 1.09 17.39

57 | 1 1.09 18.48

58 | 1 1.09 19.57

59.5 | 1 1.09 20.65

60.5 | 1 1.09 21.74

62.5 | 2 2.17 23.91

63.5 | 1 1.09 25.00

64 | 1 1.09 26.09

64.5 | 1 1.09 27.17

65 | 1 1.09 28.26

66 | 1 1.09 29.35

67.5 | 1 1.09 30.43

68.5 | 1 1.09 31.52

69 | 1 1.09 32.61

69.5 | 2 2.17 34.78

70 | 2 2.17 36.96


70.5 | 2 2.17 39.13

71 | 1 1.09 40.22

72.5 | 4 4.35 44.57

73 | 2 2.17 46.74

73.5 | 1 1.09 47.83

74 | 2 2.17 50.00

74.5 | 1 1.09 51.09

75 | 2 2.17 53.26

75.5 | 2 2.17 55.43

76.5 | 2 2.17 57.61

77 | 2 2.17 59.78

77.5 | 2 2.17 61.96

78 | 2 2.17 64.13

78.5 | 1 1.09 65.22

79 | 3 3.26 68.48

79.5 | 1 1.09 69.57

80.5 | 3 3.26 72.83

81 | 2 2.17 75.00

81.5 | 1 1.09 76.09

82 | 2 2.17 78.26

82.5 | 1 1.09 79.35

83 | 3 3.26 82.61

83.5 | 1 1.09 83.70

84 | 1 1.09 84.78

84.5 | 2 2.17 86.96

85 | 2 2.17 89.13

85.5 | 1 1.09 90.22

87 | 3 3.26 93.48

87.5 | 1 1.09 94.57

88.5 | 1 1.09 95.65

89 | 2 2.17 97.83

90 | 1 1.09 98.91


91 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of pass4

Avg Pass |

Rate 4th |

Grade Eng & |

Math |


------------+-----------------------------------

64 | 1 1.09 1.09

66 | 1 1.09 2.17

71 | 1 1.09 3.26

72.5 | 1 1.09 4.35

73 | 1 1.09 5.43

73.5 | 1 1.09 6.52

78 | 1 1.09 7.61

78.5 | 3 3.26 10.87

79.5 | 1 1.09 11.96

80 | 1 1.09 13.04

81.5 | 1 1.09 14.13

82.5 | 2 2.17 16.30

83 | 3 3.26 19.57

83.5 | 1 1.09 20.65

84 | 3 3.26 23.91

84.5 | 1 1.09 25.00

85 | 1 1.09 26.09

85.5 | 2 2.17 28.26

86 | 2 2.17 30.43

86.5 | 1 1.09 31.52


88 | 6 6.52 38.04

89 | 2 2.17 40.22

89.5 | 3 3.26 43.48

90 | 5 5.43 48.91

90.5 | 6 6.52 55.43

91 | 4 4.35 59.78

91.5 | 3 3.26 63.04

92 | 4 4.35 67.39

92.5 | 6 6.52 73.91

93 | 2 2.17 76.09

93.5 | 2 2.17 78.26

94 | 1 1.09 79.35

94.5 | 5 5.43 84.78

95 | 2 2.17 86.96

95.5 | 2 2.17 89.13

96 | 1 1.09 90.22

96.5 | 4 4.35 94.57

97 | 1 1.09 95.65

98 | 1 1.09 96.74

99 | 2 2.17 98.91

99.5 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

-> tabulation of passregents

Avg Pass |

Rate Eng |

Math |

SocStud |


------------+-----------------------------------


25 | 1 1.09 1.09

39.33333 | 1 1.09 2.17

49 | 1 1.09 3.26

57 | 1 1.09 4.35

66.66666 | 1 1.09 5.43

67 | 1 1.09 6.52

69 | 1 1.09 7.61

72 | 1 1.09 8.70

74 | 1 1.09 9.78

75.33334 | 1 1.09 10.87

76 | 1 1.09 11.96

77.33334 | 1 1.09 13.04

77.66666 | 1 1.09 14.13

78 | 1 1.09 15.22

79 | 1 1.09 16.30

79.33334 | 1 1.09 17.39

79.66666 | 1 1.09 18.48

80.66666 | 1 1.09 19.57

82 | 1 1.09 20.65

82.33334 | 1 1.09 21.74

82.66666 | 1 1.09 22.83

83 | 1 1.09 23.91

83.33334 | 1 1.09 25.00

83.66666 | 1 1.09 26.09

84.33334 | 1 1.09 27.17

85 | 1 1.09 28.26

85.33334 | 2 2.17 30.43

85.66666 | 1 1.09 31.52

86 | 1 1.09 32.61

86.33334 | 1 1.09 33.70

86.66666 | 2 2.17 35.87

87 | 1 1.09 36.96


87.33334 | 2 2.17 39.13

88 | 2 2.17 41.30

88.33334 | 2 2.17 43.48

88.66666 | 3 3.26 46.74

89 | 1 1.09 47.83

90 | 1 1.09 48.91

90.33334 | 1 1.09 50.00

90.66666 | 2 2.17 52.17

91 | 1 1.09 53.26

91.33334 | 2 2.17 55.43

91.66666 | 1 1.09 56.52

92 | 2 2.17 58.70

92.33334 | 6 6.52 65.22

92.66666 | 2 2.17 67.39

93.33334 | 2 2.17 69.57

93.66666 | 1 1.09 70.65

94 | 3 3.26 73.91

94.33334 | 3 3.26 77.17

94.66666 | 2 2.17 79.35

95 | 4 4.35 83.70

95.33334 | 3 3.26 86.96

95.66666 | 3 3.26 90.22

96 | 1 1.09 91.30

96.66666 | 3 3.26 94.57

97.33334 | 2 2.17 96.74

98.66666 | 1 1.09 97.83

99 | 1 1.09 98.91

99.66666 | 1 1.09 100.00

------------+-----------------------------------

Total | 92 100.00

. regress pct4col enroll000, beta


Source | SS df MS Number of obs = 92

-------------+------------------------------ F( 1, 90) = 3.97

Model | 1008.31333 1 1008.31333 Prob > F = 0.0493

Residual | 22853.4258 90 253.926953 R-squared = 0.0423

-------------+------------------------------ Adj R-squared = 0.0316

Total | 23861.7391 91 262.216914 Root MSE = 15.935

------------------------------------------------------------------------------

pct4col | Coef. Std. Err. t P>|t| Beta

-------------+----------------------------------------------------------------

enroll000 | -1.100622 .5523253 -1.99 0.049 -.2055638

_cons | 68.35374 3.04827 22.42 0.000 .

------------------------------------------------------------------------------

. regress pct4col ppexp000, beta


-------------+------------------------------ F( 1, 90) = 12.43

Model | 2895.02137 1 2895.02137 Prob > F = 0.0007


-------------+------------------------------ Adj R-squared = 0.1116

Total | 23861.7391 91 262.216914 Root MSE = 15.263

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

ppexp000 | 1.789275 .5075692 3.53 0.001 .3483171

_cons | 32.97287 8.738007 3.77 0.000 .

------------------------------------------------------------------------------

. regress pct4col csize, beta



-------------+------------------------------ F( 1, 90) = 3.27

Model | 835.986169 1 835.986169 Prob > F = 0.0740


-------------+------------------------------ Adj R-squared = 0.0243

Total | 23861.7391 91 262.216914 Root MSE = 15.995

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

csize | -1.803723 .9978284 -1.81 0.074 -.1871753

_cons | 102.0017 21.4964 4.75 0.000 .

------------------------------------------------------------------------------

. regress pct4col attend, beta


-------------+------------------------------ F( 1, 90) = 32.61

Model | 6346.29455 1 6346.29455 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.2578

Total | 23861.7391 91 262.216914 Root MSE = 13.95

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

attend | 5.91142 1.035192 5.71 0.000 .5157142

_cons | -497.9385 98.28651 -5.07 0.000 .

------------------------------------------------------------------------------

. regress pct4col pass4, beta



-------------+------------------------------ F( 1, 90) = 49.96

Model | 8518.01997 1 8518.01997 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.3498

Total | 23861.7391 91 262.216914 Root MSE = 13.057

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

pass4 | 1.361642 .1926361 7.07 0.000 .597473

_cons | -57.31848 17.113 -3.35 0.001 .

------------------------------------------------------------------------------

. regress pct4col passregents, beta


-------------+------------------------------ F( 1, 90) = 92.56

Model | 12098.4906 1 12098.4906 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5015

Total | 23861.7391 91 262.216914 Root MSE = 11.433

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

passregents | .9457814 .0983032 9.62 0.000 .7120567

_cons | -18.73084 8.605051 -2.18 0.032 .

------------------------------------------------------------------------------

. regress pct4col medtchexp, beta



-------------+------------------------------ F( 1, 90) = 1.90

Model | 494.508029 1 494.508029 Prob > F = 0.1710


-------------+------------------------------ Adj R-squared = 0.0098

Total | 23861.7391 91 262.216914 Root MSE = 16.113

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

medtchexp | .9448936 .6846658 1.38 0.171 .1439579

_cons | 52.72325 7.818131 6.74 0.000 .

------------------------------------------------------------------------------

. regress pct4col co, beta


-------------+------------------------------ F( 1, 90) = 4.17

Model | 1056.69105 1 1056.69105 Prob > F = 0.0441


-------------+------------------------------ Adj R-squared = 0.0337

Total | 23861.7391 91 262.216914 Root MSE = 15.918

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

co | 6.836538 3.347777 2.04 0.044 .2104374

_cons | 53.45192 5.081951 10.52 0.000 .

------------------------------------------------------------------------------

. regress pct4col enroll000 ppexp000 csize attend pass4 passregents medtchexp co, beta



-------------+------------------------------ F( 8, 83) = 19.24

Model | 15503.6188 8 1937.95235 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.6160

Total | 23861.7391 91 262.216914 Root MSE = 10.035

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

enroll000 | .0302549 .4037618 0.07 0.940 .0056507

ppexp000 | 2.082902 .5055581 4.12 0.000 .4054772

csize | .2630747 .7725277 0.34 0.734 .0272997

attend | 1.570267 .91467 1.72 0.090 .1369906

pass4 | .2538726 .2337281 1.09 0.281 .1113964

passregents | .7451234 .1280158 5.82 0.000 .560986

medtchexp | -1.3152 .5588814 -2.35 0.021 -.2003755

co | 1.073157 2.573468 0.42 0.678 .0330332

_cons | -200.811 83.39689 -2.41 0.018 .

------------------------------------------------------------------------------

. estat vif

Variable | VIF 1/VIF

-------------+----------------------

pass4 | 2.49 0.401233

ppexp000 | 2.30 0.435703

passregents | 2.20 0.454311

medtchexp | 1.72 0.582081

csize | 1.52 0.656663

attend | 1.51 0.662773


co | 1.49 0.672539

enroll000 | 1.35 0.742098

-------------+----------------------

Mean VIF | 1.82

. save "http://datalibrary.us/reportcards200405.dta", replace

may not write files over Internet

r(633);

. * Step 1

.

. * Load the data set from the web.

.

. use "http://myweb.liu.edu/~redowl/data/reportcards200405.dta", clear

.

.

.

. * Step 2

.

. set more off

.

.

.

. * Step 3

.

. * Turn on feature to place all graphs in tabs of graph window


.

. * instead of separate graph windows. (This may not work on Macs, but

.

. * that feature is just cosmetic and will not affect the ultimate graphs.)

.

. set autotabgraphs on

(set autotabgraphs preference recorded)

.

.

.

. * Step 4

.

. * This drops any graphs that may be in memory from previous analyses.

.

. * The "capture" command instructs Stata to ignore errors if no graphs

.

. * are already in memory and none need to be dropped.

.

. capture graph drop _all

.

.

.

. * Step 5


.

. * This loop produces the graph for each non-binary independent variable

.

. * in the varlist.

.

. foreach var of varlist enroll000 ppexp000 csize attend pass4 passregents medtchexp {

2.

. twoway (lpolyci pct4col `var') (lfit pct4col `var', lcolor(blue)) (sc pct4col

`var', msize(small) ) , legend(off) title(`var') scheme(s1color) name(gr_`var')

3.

. }

.

.

.

. * Step 6

.

. * This produces the graph for the single binary independent variable,

.

. * co, and omits the polynomial line. Any line between two points

.

. * (i.e., the averages of the categories of a binary variable) will always

.

. * be a straight line, so a polynomial fit would not be meaninful.


.

. twoway (lfitci pct4col co) (lfit pct4col co, lcolor(blue)) (sc pct4col co,

mcolor(maroon) msize(small) ) , xlabel(1(1)2, valuelabel) legend(off) title(co)

scheme(s1color) name(gr_co

> )

.

.

.

. * Step 7

.

. * This produces the first set of combined graphs from the loop above.

.

. graph combine gr_enroll000 gr_ppexp000 gr_csize gr_attend, cols(2) title(Review of

Linearity) scheme(s1color) name(gr_comb1)

.

.

.

. * Step 8

.

. * This produces the second set of combined graphs from the loop above.

.

. graph combine gr_pass4 gr_passregents gr_medtchexp gr_co, cols(2) title(Review of

Linearity) scheme(s1color) name(gr_comb2)

.

.


.

. * Step 9

.

. * This shows the nonlinear relationship between pct4col and ppexp

.

. * for all districts.

.

. twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000 if ppexp000<=19,

lcolor(blue) range(12.8 19)) (lfit pct4col ppexp000 if ppexp000>=19 &

ppexp000<=22, lcolor(blue) range(19 22

> )) (lfit pct4col ppexp000 if ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col

ppexp000, mcolor(orange) mlabel(district) msize(.5) mlabsize(1.5)

mlabcolor(black)) , ytitle(pct4col

> ) ylabel(10(10)100) xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off)

title(Regression Lines - All Districts) scheme(s1color) name(gr_ppexp000all)

.

.

.

. * Step 10

.

. * This adds an overall linear regression line to the polynomical and segmented

regression lines

.

. twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12.8

25)) (lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12.8 19)) (lfit

pct4col ppexp000 if p


> pexp000>=19 & ppexp000<=22, lcolor(blue) range(19 22)) (lfit pct4col ppexp000 if

ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col ppexp000, mcolor(orange)

mlabel(district) msize(

> .5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100)

xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off)

title(Regression Lines - All Districts) schem

> e(s1color) name(gr_ppexp000all2)

.

.

.

. * Step 11

.


.

. * for Suffolk County districts.

.

. preserve

.

. keep if co==1

(40 observations deleted)

.

. twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12 25))

(lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12 19)) (lfit pct4col

ppexp000 if ppexp


> 000>=22, lcolor(blue) range(22 26)) (sc pct4col ppexp000, mcolor(orange)

mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col)

ylabel(20(10)100) xtitle(ppexp00

> 0) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Suffolk County)

scheme(s1color) name(gr_ppexp000suf)

.

. restore

.

.

.

. * Step 12

.


.

. * for Nassau County districts.

.

. preserve

.

. keep if co==2


.

. twoway (lpolyci pct4col ppexp000) (lfit pct4col ppexp000, lcolor(red) range(12 25))

(lfit pct4col ppexp000 if ppexp000<=19, lcolor(blue) range(12 19)) (lfit pct4col

ppexp000 if ppex


> p000>=19 & ppexp000<=22, lcolor(blue) range(19 22)) (lfit pct4col ppexp000 if

ppexp000>=22, lcolor(blue) range(22 25)) (sc pct4col ppexp000, mcolor(orange)

mlabel(district) msize(.5)

> mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100)

xtitle(ppexp000) xlabel(12(2)26) xline(19) xline(22) legend(off) title(Nassau

County) scheme(s1color) name(gr_ppe

> xp000nas)

.

. restore

.

.

.

. * Step 13

.

. * This shows the nonlinear relationship between pct4col and csize

.

. * for all districts.

.

. twoway (lpolyci pct4col csize) (lfit pct4col csize if csize<=20, lcolor(blue)

range(16 20)) (lfit pct4col csize if csize>=20 & csize<=23, lcolor(blue)

range(20 23)) (lfit pct4col csi

> ze if csize>=23, lcolor(blue) range(23 25)) (sc pct4col csize, mcolor(orange)

mlabel(district) msize(.5) mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col)

ylabel(10(10)100) xtitle(cs

> ize) xlabel(16(2)26) xline(20) xline(23) legend(off) title(Regression Lines - All

Districts) scheme(s1color) name(gr_csizeall)


.

.

.

. * Step 14

.

. * This adds an overall linear regression line to the polynomical and segmented

regression lines

.

. twoway (lpolyci pct4col csize) (lfit pct4col csize, lcolor(red) range(16 25)) (lfit

pct4col csize if csize<=20, lcolor(blue) range(16 20)) (lfit pct4col csize if

csize>=20 & csize<=2

> 3, lcolor(blue) range(20 23)) (lfit pct4col csize if csize>=23, lcolor(blue)

range(23 25)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5)

mlabsize(1.5) mlabcolor(black)

> ) , ytitle(pct4col) ylabel(10(10)100) xtitle(csize) xlabel(16(2)26) xline(20)

xline(23) legend(off) title(Regression Lines - All Districts) scheme(s1color)

name(gr_csizeall2)

.

.

.

. * Step 15

.


.

. * for Suffolk County districts.

.


. preserve

.

. keep if co==1


.


pct4col csize if csize<=20 & csize<=24, lcolor(blue) range(16 20)) (lfit pct4col

csize if csize>=2

> 0 & csize<=24, lcolor(blue) range(20 24)) (lfit pct4col csize if csize>=24,

lcolor(blue) range(24 25)) (sc pct4col csize, mcolor(orange) mlabel(district)

msize(.5) mlabsize(1.5) mlab

> color(black)) , ytitle(pct4col) ylabel(10(10)100) xtitle(csize) xlabel(16(2)26)

xline(20) xline(24) legend(off) title(Regression Lines - Suffolk County)

scheme(s1color) name(gr_csize

> suf)

.

. restore

.

.

.

. * Step 16

.


.

. * for Nassau County districts.


.

. preserve

.

. keep if co==2


.


pct4col csize if csize<=20, lcolor(blue) range(18 20)) (lfit pct4col csize if

csize>=20, lcolor(bl

> ue) range(20 23)) (sc pct4col csize, mcolor(orange) mlabel(district) msize(.5)

mlabsize(1.5) mlabcolor(black)) , ytitle(pct4col) ylabel(10(10)100)

xtitle(csize) xlabel(18(2)24) xline

> (20) legend(off) title(Regression Lines - Nassau County) scheme(s1color)

name(gr_csizenas)

.

. restore


% Grads WhoGo to

College inYear afterGraduation

Enrollmentin

1000s

Per PupilExpenditures

in $1000s

AverageClassSize

AveragePercent

Attendance

Avg PassRate 4th

Grade Eng &Math

Regents

Avg PassRate Eng

MathSocStudRegents

MedianTeacher

WorkforceExperience

in Years

County

0 50 100

0

10

20

0 10 20

10

15

20

25

10 15 20 25

15

20

25

15 20 25

90

95

100

90 95 100

60

80

100

60 80 100

0

50

100

0 50 100

5

10

15

20

5 10 15 201

1.5

2


020

4060

8010

0

0 5 10 15 20

enroll000

. graph save gr_enroll000 "C:\Users\Owner\Documents\gr_enroll000.gph"

(file C:\Users\Owner\Documents\gr_enroll000.gph saved)


020

4060

8010

0

10 15 20 25

ppexp000

. graph save gr_ppexp000 "C:\Users\Owner\Documents\gr_ppexp000.gph"

(file C:\Users\Owner\Documents\gr_ppexp000.gph saved)


020

4060

8010

0

16 18 20 22 24 26

csize

. graph save gr_csize "C:\Users\Owner\Documents\gr_csize.gph"

(file C:\Users\Owner\Documents\gr_csize.gph saved)


020

4060

8010

0

90 92 94 96 98

attend

. graph save gr_attend "C:\Users\Owner\Documents\gr_attend.gph"

(file C:\Users\Owner\Documents\gr_attend.gph saved)


020

4060

8010

0

60 70 80 90 100

pass4

. graph save gr_pass4 "C:\Users\Owner\Documents\gr_pass4.gph"

(file C:\Users\Owner\Documents\gr_pass4.gph saved)


020

4060

8010

0

20 40 60 80 100

passregents

. graph save gr_passregents "C:\Users\Owner\Documents\gr_passregents.gph"

(file C:\Users\Owner\Documents\gr_passregents.gph saved)


020

4060

8010

0

5 10 15 20

medtchexp

. graph save gr_medtchexp "C:\Users\Owner\Documents\gr_medtchexp.gph"

(file C:\Users\Owner\Documents\gr_medtchexp.gph saved)


020

4060

8010

0

Suffolk Co. Nassau Co.County

co

. graph save gr_co "C:\Users\Owner\Documents\gr_co.gph"

(file C:\Users\Owner\Documents\gr_co.gph saved)


020

4060

8010

0

0 5 10 15 20

enroll000

020

4060

8010

0

10 15 20 25

ppexp000

020

4060

8010

0

16 18 20 22 24 26

csize

020

4060

8010

0

90 92 94 96 98

attend

Review of Linearity

. graph save gr_comb1 "C:\Users\Owner\Documents\gr_comb1.gph"

(file C:\Users\Owner\Documents\gr_comb1.gph saved)


020

4060

8010

0

60 70 80 90 100

pass4

020

4060

8010

0

20 40 60 80 100

passregents

020

4060

8010

0

5 10 15 20

medtchexp

020

4060

8010

0


co

Review of Linearity

. graph save gr_comb2 "C:\Users\Owner\Documents\gr_comb2.gph"

(file C:\Users\Owner\Documents\gr_comb2.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park

East HamptonEast Islip

East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst

LongwoodMiddle Country

Miller Place

Mount Sinai

North Babylon

Northport-East Northport

Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville

Shelter IslandShoreham-Wading RiverSmithtown

South Country

South Huntington

Southampton

Southold

Three Village

West Babylon

West IslipWesthampton Beach

William Floyd

Wyandanch

Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside

Oyster Bay-East Norwich

Plainedge

Plainview-Old Bethpage

Port WashingtonRockville Centre

Roosevelt

Roslyn

Seaford

Syosset

UniondaleWantaghWest Hempstead

Westbury

1020

3040

5060

7080

9010

0pc

t4co

l

12 14 16 18 20 22 24 26ppexp000

Regression Lines - All Districts

. graph save gr_ppexp000all "C:\Users\Owner\Documents\gr_ppexp000all.gph"

(file C:\Users\Owner\Documents\gr_ppexp000all.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park


East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst


Miller Place

Mount Sinai

North Babylon


Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville


South Country

South Huntington

Southampton

Southold

Three Village

West Babylon


William Floyd

Wyandanch

Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside


Plainedge



Roosevelt

Roslyn

Seaford

Syosset


Westbury

1020

3040

5060

7080

9010

0pc

t4co

l

12 14 16 18 20 22 24 26ppexp000


. graph save gr_ppexp000all2 "C:\Users\Owner\Documents\gr_ppexp000all2.gph"

(file C:\Users\Owner\Documents\gr_ppexp000all2.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park


East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst


Miller Place

Mount Sinai

North Babylon


Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville


South Country

South Huntington

Southampton

Southold

Three Village

West Babylon


William Floyd

Wyandanch

2030

4050

6070

8090

100

pct4

col

12 14 16 18 20 22 24 26ppexp000

Suffolk County

. graph save gr_ppexp000suf "C:\Users\Owner\Documents\gr_ppexp000suf.gph"

(file C:\Users\Owner\Documents\gr_ppexp000suf.gph saved)


Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside


Plainedge



Roosevelt

Roslyn

Seaford

Syosset


Westbury

1020

3040

5060

7080

9010

0pc

t4co

l

12 14 16 18 20 22 24 26ppexp000

Nassau County

. graph save gr_ppexp000nas "C:\Users\Owner\Documents\gr_ppexp000nas.gph"

(file C:\Users\Owner\Documents\gr_ppexp000nas.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park


East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst


Miller Place

Mount Sinai

North Babylon


Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville

Shelter Island Shoreham-Wading River Smithtown

South Country

South Huntington

Southampton

Southold

Three Village

West Babylon


William Floyd

Wyandanch

Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside


Plainedge



Roosevelt

Roslyn

Seaford

Syosset

Uniondale WantaghWest Hempstead

Westbury

1020

3040

5060

7080

9010

0pc

t4co

l

16 18 20 22 24 26csize


. graph save gr_csizeall "C:\Users\Owner\Documents\gr_csizeall.gph"

(file C:\Users\Owner\Documents\gr_csizeall.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park


East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst


Miller Place

Mount Sinai

North Babylon


Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville


South Country

South Huntington

Southampton

Southold

Three Village

West Babylon


William Floyd

Wyandanch

Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside


Plainedge



Roosevelt

Roslyn

Seaford

Syosset


Westbury

1020

3040

5060

7080

9010

0pc

t4co

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(file C:\Users\Owner\Documents\gr_csizeall2.gph saved)


Amityville

Babylon

Bay Shore

Bayport-Blue Point

Brentwood

Center Moriches

Central Islip

Cold Spring Harbor

Commack

Comsewogue

Connetquot

Copiague

Deer Park


East Meadow

Elwood

Greenport

Half Hollow Hills

Hampton Bays

Harborfields

Hauppauge

Huntington

Islip

Kings Park

Lindenhurst


Miller Place

Mount Sinai

North Babylon


Patchogue-Medford

Port Jefferson

Riverhead

Rocky Point

Sachem

Sag Harbor

Sayville


South Country

South Huntington

Southampton

Southold

Three Village

West Babylon


William Floyd

Wyandanch

1020

3040

5060

7080

9010

0pc

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l

16 18 20 22 24 26csize

Regression Lines - Suffolk County

. graph save gr_csizesuf "C:\Users\Owner\Documents\gr_csizesuf.gph"

(file C:\Users\Owner\Documents\gr_csizesuf.gph saved)


Baldwin

Bethpage

Carle Place

East Rockaway

East Williston

Farmingdale

Freeport

Garden City

Glen Cove

Great Neck

Hempstead

Herricks

Hewlett-Woodmere

Hicksville

Island Trees

Jericho

Lawrence

Levittown

Locust Valley

Long Beach

Lynbrook

Malverne

Manhasset

Massapequa

Mineola

North Shore

Oceanside


Plainedge



Roosevelt

Roslyn

Seaford

Syosset


Westbury

1020

3040

5060

7080

9010

0pc

t4co

l

18 20 22 24csize

Regression Lines - Nassau County

. graph save gr_csizenas "C:\Users\Owner\Documents\gr_csizenas.gph"

(file C:\Users\Owner\Documents\gr_csizenas.gph saved)


Results and Discussion

The purpose of this project was to conduct a multiple regression analysis on a set of

select variables from the 2004-05 New York State Education Department school district report

cards http://nysed.gov. The goal is to see if any correlation exists between certain independent

variable in the data and the prediction of the number of post one year high school graduates who

enroll in a four year college. An important factor in determining the effectiveness of high school

educational procedures and study material content comprising student experiential process is the

ratio of graduation rate to post-secondary education enrollment ( ). The New York State

Report Cards annual report provides information on the “enrollment, demographic, attendance,

suspension, dropout, teacher, assessment, accountability, graduation rate, post-graduate plan,

career and technical education, and fiscal data for public and charter schools, districts, and the

State” ( ). From the report a set of seven research questions were developed to be answered

by multiple regression analysis for the project. The questions main focus of concern; school

district factors that affect the rates at which their students enroll in four-year colleges

immediately after high school. The districts of focus were Nassau and Suffolk counties. From

these two counties there were 92 school districts contained in the data. The questions will be

addressed later in this report based on the analysis of the data.

Regression Method

The data submitted to the New York State Education Department’s report cards for

documentation were collected from all state school districts and their reporting elements; school

district officials, school superintendents and school principles. All efforts were afforded to those

reporting elements to review verify and correct reporting data. The data collected concerning

graduate rates is for a specific academic graduate year. The academic graduate grade year is

designated as a cohort from whom all reference will be made. This project sought to analyze the

http://nysed.gov/


2004-2005 cohorts graduation rate as it pertained to the cohort’s total populous that enrolled in a

4 year college within one year of graduation. The school districts of concern were Nassau

County and Suffolk County. From these two counties there were n = 92 separate reporting school

districts or observations that encompassed the submitted data.

The initial data reported contained 22 variables; some of these variables for the districts

had incomplete information and therefore were excluded. A multiple regression analysis was

conducted with the following predictor independent variables (IVs): County (co), Enrollment in

1000s (enroll000), Per Pupil Expenditures in $1000s (ppexp000), Average Percent Attendance

(attend), Average Class Size (csize), Median Teacher Workforce Experience in Years

(medtchexp), Average Pass Rate 4th Grade English and math Regents (pass4), and Average Pass

rate English Math Social Studies Regents (passregents), with Percent Graduates Who Go to

College in Year After Graduation (pct4col) as the outcome dependent variable (DV).

First we see that the F-test is statistically significant, hence the model is significantly

significant. The model produced an R squared of .6497 or that means approximately 65% of the

variances of pct4col is accounted for by the model, which was statistically significant, and with

[F (8, 83) = 19.24, p < .00005] see (Table 1). The overall regression model shows that we can

have a good confidence level that the results are not merely from chance. However this does not

indicate that every IV is significant. Some may even warrant removal later. With the F statistic

and its p-value (Prob > F) telling us that the overall regression model is statistically significant

we can therefore can use the model and interpret its results. The following discussion will focus

on the eight independent variable predictors accounting for 65.0% (Table 1) of the variance in

the percent of high school graduates who go on to college in the year after their high school

graduation in Nassau and Suffolk counties when all 92 school districts are taken into account.


(Insert Table 1 Approximately Here)

In the Stata regression shown in (Table 1), the predicted regression equation is:

pct4col = -200.81 + 3.03*enroll000 + 2.08*ppexp000 + .26*csize + 1.57*attend + .25*pass4

+ .75*passregents + -1.32*medtchexp + 1.07*co

The equation shows that the dependent variable’s constant coefficient -200.81 the percent of

graduates who go to college in one year after graduation high school (pct4col) remains constant

when all independent variables are held constant at zero. The dependent variable (pct4col) is

predicted to react accordingly with the related influences of the select independent predictor

variables as references in (Table 1) showing Multiple Regression variances and in (Table 3)

when only simple linear regression variances are considered. For this project the data set from

(Table 1) will be discussed in length to show that the dependent variable (pct4col) will:

Increase by 3.03 units when the enrollment in the 1000s (enroll000) goes up by one unit.

Increase by 2.08 units when per pupil expenditures in the $1000s (ppexp000) goes up by

one unit

Increase by .26 units when the average class size (csize) goes up by one unit.

Increase by 1.57 units when the average percent attendance (attend) goes up by one unit.

Increase by .25 units by when average pass rate of 4th grade English and Math Regents

(pass4) goes up by one unit.

Increase by .75 units when the average pass rate on English, Math, and Social Studies

Regents (passregents) goes up by one unit.

Decreaseby by -1.32 units when the median teacher workforce experience increases in

years (medtchexp) goes up by one unit.


The final independent variable shows an increase of 1.07 units when all 92 school

districts of Nassau and Suffolk counties (co) are considered.



Let’s begin to focus more precisely on the eight predictors and whether they are statically

significant and, if so, the direction of their relationship to the dependent variable the percent of

high school graduates who go on to a four year college in a year after graduation (pct4col). This

will be specifically addressed by answering the seven research questions posed in this class

project of multiple regression analysis. The following represents the research questions needed

to be addressed given the data set in the pre-report STATA section:

Research Question 1

The enrollment size in 1000’s (enroll000, b=0.030), is not significant (p=0.94). The

coefficient is positive which would indicate that the larger the enrollment of a school district is

related to a higher percentage of that particular school districts senior graduates who would go

on to a four year college a year after their graduation date, which is what we would expect.

Research Question 2


The budgetary resources or per pupil expenditures in $1000’s (ppexp000, b=2.082), is

highly significant (p=0.000). The coefficient is positive which would indicate that the greater the

amount of budgetary resources spent on student learning is related to a higher percentage of that

particular school districts senior graduates who would go on to a four year college a year after

their graduation date, which is what we would expect.

Research Question 3

The average class size in a school district (csize, b=0.263), is not significant (p=0.734).

The coefficient is positive which would indicate that the smaller the class size allowing for a

smaller ratio of student per teacher is related to a higher percentage of that particular school

districts senior graduates who would go on to a four year college a year after their graduation

date, which is what we would expect. Indicating more individualized per student instruction

times and personalized instruction methods being utilized by teachers.

Research Question 4

The average percent of attendance in a school district (attend, b=1.570), is not significant

(p=0.90). The coefficient is positive which would indicate that the greater the overall attendance

rate of a particular school districts is related to a higher percentage of their senior graduates who

would go on to a four year college a year after their graduation date, which is what we would

expect. Indicating that with a higher average percentage attendance the students on the whole

received more in class instruction time allowing for greater retention and understanding of

instructional materials.


(Insert Graph 1 Approximately Here)

Research Question 5

The academic preparation of a school districts students looked at by two of the

independent variable predictors are represented by the following:

The average pass rate of 4th grade English and Math regents (pass4, b=0.254), is not significant

(p=0.281). The coefficient is positive which would indicate that the higher the average pass rate

is related to a to a higher percentage of that particular school districts senior graduates who

would go on to a four year college a year after their graduation date, which is what we would

expect.

The average pass rate of English, Math, and Social Studies regents (passregents, b=0.745), is

highly significant (p=0.000). The coefficient is positive which would indicate that the higher the

average pass rate of students on the English, math, and Social Studies regents is related to a

higher percentage of that particular school districts senior graduates who would go on to a four

year college a year after their graduation date, which is what we would expect. Results

indicating students received proper instructional preparation and educational motivation for

future advancement to post-secondary education levels.

Research Question 6

The median teacher workforce experience in years (medtchexp, b=-1.315), is seemingly

not significant (p=0.021). The coefficient is negative which would indicate that the higher the

teacher workforce in years of experience is related to a lower percentage of that particular school


districts senior graduates who would go on to a four year college a year after their graduation

date. The percentage of teacher workforce experience is at best seemingly unrelated to graduates

who seek enrollment in a four year college within one year of graduation; at worst it would

indicate that there is a negative effect as it relates to the independent variables relation to the

dependent variable. This all would indicate that long time teacher experience in years is not an

important factor in predicting college enrollment; this is a somewhat unexpected result.

Research Question 7

The county from which the districts are located (co, b= 1.073), is not significant

(p=0.678). The coefficient is positive which would indicate that the county of a particular school

districts is related to a higher percentage of their senior graduates who would go on to a four year

college a year after their graduation date, which is what we would expect if we could assume that

these two select counties are made up of affluent areas in relationship to all other counties in

New York State. Without this extended comparison it is difficult to make this assumption based

on the presented data set. It seems these two counties are related in equal fashion when looking

at the percentage of graduates who go on to college in the year after graduation



Conclusion


From these results, based on the data set presented, one would conclude that the most

statistically significantly coefficients were (ppexp000) and (passregents) as they both met the

statistical requirements of the F-test and p-probability parameter. They were both directly

related to either higher or lower percentage rates of high school graduates who enroll in college

within one year of graduation from any one particular school district. All of the other

coefficients (enroll000, csize, attend, pass4, medtchexp, & co) did have either a positive or

negative effect on the dependent variables final percent outcome but were all at seemingly

insignificant levels of related probability only lessoning the adjusted R-squared rate to

approximately 62%. This still leaves approximately 35% of unknown probable independent

variable out there to consider in future studies relating to the percentage of high school graduates

who enroll in college within a year after graduation. One might consider searching in the socio-

economic backgrounds of student population (family’s ability to pay for a post-secondary college

education) for other independent predictor variable coefficients that might be found to hold

statistically significant data results. Notwithstanding it is clear that this particular area of

educational data sets has much further investigative analysis ahead.


References

Table 1

Multiple Regression Percent High School Graduates Who Go to College Year after Graduation

Source SS df MS Number of obs = 92 F ( 8, 83) = 19.24

Model15503.61

9 8 1937.9524 Prob > F = 0.0000

Residual8358.120

3 83 100.70025 R-squared = 0.6497 Adj R-squared = 0.6160

Total23861.73

9 91 262.21691 Root MSE = 10.035

pct4col Coef.Std. Err. t P> l t l Beta

enroll0000.030254

9 0.40376 0.07 0.940 0.0056507ppexp000 2.082902 0.50556 4.12 0.000 0.4054772

csize0.263074

7 0.77253 0.34 0.734 0.0272997


attend 1.570267 0.91467 1.72 0.090 0.1369906

pass40.253872

6 0.23373 1.09 0.281 0.1113964

passregents0.745123

4 0.12802 5.82 0.000 0.560986medtchexp -1.3152 0.55888 -2.35 0.021 -0.2003755

co 1.073157 2.57347 0.42 0.678 0.0330332_cons -200.811 83.3969 -2.41 0.018 .

Table 2

Correlatation of Variables

pct4col

enr~00

ppe~00 csize attend pass4

pas~nts

med~xp co

pct4col1.000

0

enroll000

-0.205

61.000

0

ppexp0000.348

3

-0.438

21.000

0

csize

-0.187

20.345

8 -0.5591.000

0attend 0.515

7-

0.2110.100

8-

0.0561.000

0


4 2

pass40.597

5 -0.1610.179

5

-0.147

60.494

61.000

0

passregents

0.7121

-0.115

40.062

7

-0.023

50.525

40.677

1 1.0000

madtchexp 0.144

-0.370

10.603

4

-0.277

20.065

90.211

7 0.115 1.0000

co0.210

4

-0.165

90.378

6

-0.211

60.118

70.395

1 0.0777 0.36561.00

0

Table 3

Simple Linear Regression of All Independent Predictor Variables

Source SS df MS Number of obs = 92

Model 1008.3133 1 1008.31333 Prob > F = 0.0493Residual 22853.4258 90 253.926953 R-squared = 0.0423

Adj R-squared= 0.0316Total 23861.7391 91 262.216914 Root MSE = 15.935

pct4col Coef. Std. Err. t P> l t l Beta

enroll000 -1.100622 0.5523253 -1.99 0.049 -0.2055638_cons 68.35374 3.04827 22.42 0.000 .

Source SS df MS Number of obs = 92


F ( 1, 90) = 12.43Model 2895.02137 1 2895.02137 Prob > F = 0.0007

Residual 20966.7178 90 232.963531 R-squared = 0.1213Adj R-squared= 0.1116

Total 23861.7391 91 262.216914 Root MSE = 15.263


ppexp000 1.789275 0.5075692 3.53 0.001 0.3483171_cons 32.97287 8.738007 3.77 0.000 .

Source SS df MS Number of obs = 92F ( 1, 90) = 3.27




csize -1.803723 0.9978284 -1.81 0.074 -1.871752_cons 102.0017 21.4964 4.75 0.000 .






attend 5.91142 1.035192 5.71 0.000 0.5157142_cons -497.9385 98.28651 -5.07 0.000 .





pass4 1.36142 0.1926361 7.07 0.000 0.597473_cons -57.31848 17.113 -3.35 0.001 .



Adj R-squared= 0.5015


Total 23861.7391 91 262.216914 Root MSE = 11.433


passregents 0.9457814 0.0983032 9.62 0.000 0.7120567

_cons -18.73084 8.605051 -2.18 0.032 .





medtchexp 0.9448936 0.6846658 1.38 0.171 0.1439579_cons 52.72325 7.818131 6.74 0.000 .






co 6.836538 3.347777 2.04 0.044 0.2104374_cons 53.45192 5.081951 10.52 0.000 .

Graph 1

Scatterplot Matrix for All Variables


% Grads WhoGo to

College inYear afterGraduation

Enrollmentin

1000s

Per PupilExpenditures

in $1000s

AverageClassSize

AveragePercent

Attendance

Avg PassRate 4th

Grade Eng &Math

Regents

Avg PassRate Eng

MathSocStudRegents

MedianTeacher

WorkforceExperience

in Years

County

0 50 100

0

10

20

0 10 20

10

15

20

25

10 15 20 25

15

20

25

15 20 25

90

95

100

90 95 100

60

80

100

60 80 100

0

50

100

0 50 100

5

10

15

20

5 10 15 201

1.5

2

Graph 2

Scatterplots of Linearity Observations Enrollment, Expenditures, Class Size, & Attendance


020

4060

8010

0

0 5 10 15 20

enroll000

020

4060

8010

0

10 15 20 25

ppexp000

020

4060

8010

0

16 18 20 22 24 26

csize

020

4060

8010

0

90 92 94 96 98

attend

Review of Linearity

Graph 3


Scatterplots of Linearity Observations 4th Grade Regents Pass Rate, All Regents Pass Rate,

Median Teacher Workforce Experience, & County0

2040

6080

100

60 70 80 90 100

pass4

020

4060

8010

020 40 60 80 100

passregents

020

4060

8010

0

5 10 15 20

medtchexp

020

4060

8010

0


co

Review of Linearity

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