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T HE SAT S CORES TELL A STORY OF THE U.S. D EMOGRAPHIC ECON 240A.

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Presentation on theme: "T HE SAT S CORES TELL A STORY OF THE U.S. D EMOGRAPHIC ECON 240A."— Presentation transcript:

1 T HE SAT S CORES TELL A STORY OF THE U.S. D EMOGRAPHIC ECON 240A

2 T EAM M EMBERS Tore Stautland Bjøndal Chungkai Gao Eric Howard Dan Helling Chien-Ju Lin Matt Mullens

3 C HOICE OF S TUDY : SAT S CORES Everyone can relate to the SATs No ambiguity in the numbers Source: Moore, The Basic Practice of Statistics, 2nd Edition

4 W HY ARE SAT S CORES INTERESTING ? Can check dependencies on other variables Standardized test, highly valued as a college admission criteria Locate differences throughout the U.S.

5 A NGLE OF A TTACK Scatterplots Bar-Charts Regressions Wald Test Test for equality of means

6 W HAT ARE WE LOOKING AT ? How does teacher salary affect the SAT Scores of a particular state or region? How does region play in to SAT Scores? How does population without a high school education affect the SAT Scores? Does percentage of population taking the test matter?

7 D EPENDENT V ARIABLE : SAT S CORES Independents: Teacher average salary Region ( 9 different ) Percentage of population taking SAT Percentage of population with no high school diploma Regions: ENC (East North Central) - Illinois, Indiana, Michigan, Ohio, Wisconsin ESC (East South Central) – Alabama, Kentucky, Mississippi, Tennessee MA (Mid-Atlantic) – New Jersey, New York, Pennsylvania MTN (Mountain) – Arizona, Colorado, Idaho, Montana Nevada, New Mexico, Utah, Wyoming NE (New England) – Connecticut, Maine, Mssachusetts, New Hampshire, Rhode Island, Vermont PAC (Pacific) – Alaska, California, Hawaii, Oregon, Washington SA (South Atlantic) – Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, District of Columbia WNC (West North Central) – Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota WSC (West South Central) – Arkansas, Louisiana, Oklahoma, and Texas

8 E XPLORATORY D ATA A NALYSIS

9

10 D ESCRIPTIVE S TATISTICS

11 D ESCRIPTIVE S TATISTICS #2

12 D ESCRIPTIVE S TATISTICS #3

13 S TATISTICAL A NALYSIS

14 S TATISTICAL A NAYLSIS Dependent Variable: SAT Method: Least Squares Date: 12/03/08 Time: 13:07 Sample: 1 51 Included observations: 51 VariableCoefficientStd. Errort-StatisticProb. TEACHER_PAY-4.8234821.401967-3.4405100.0012 C1234.29251.0537324.176340.0000 R-squared0.194571 Mean dependent var1061.176 Adjusted R-squared0.178133 S.D. dependent var68.08134 S.E. of regression61.72041 Akaike info criterion11.12153 Sum squared resid186661.0 Schwarz criterion11.19729 Log likelihood-281.5991 F-statistic11.83711 Durbin-Watson stat1.053667 Prob(F-statistic)0.001196 Table 2. Regress SAT on TEACHER_PAY

15 S TATISTICAL A NAYLSIS Dependent Variable: SAT Method: Least Squares Date: 12/03/08 Time: 13:10 Sample: 1 51 Included observations: 51 VariableCoefficientStd. Errort-StatisticProb. REGION_WSC62.3500024.435492.5516160.0145 REGION_WNC137.171421.329056.4312030.0000 REGION_SA-34.9555620.31761-1.7204560.0927 REGION_NE-14.0666722.05721-0.6377360.5271 REGION_MTN58.4750020.766182.8158770.0074 REGION_MA-27.0666726.60200-1.0174680.3148 REGION_ESC90.8500024.435493.7179520.0006 REGION_ENS73.2000023.038003.1773590.0028 C1023.40016.2903362.822550.0000 R-squared0.759534 Mean dependent var1061.176 Adjusted R-squared0.713731 S.D. dependent var68.08134 S.E. of regression36.42628 Akaike info criterion10.18724 Sum squared resid55728.71 Schwarz criterion10.52815 Log likelihood-250.7747 F-statistic16.58265 Durbin-Watson stat2.296261 Prob(F-statistic)0.000000 Table 3. Regress SAT on all the Regions

16 S TATISTICAL A NAYLSIS Wald Test: Equation: Untitled Null Hypothesis:C(3)=C(10) C(4)=C(10) C(5)=C(10) C(6)=C(10) C(7)=C(10) C(8)=C(10) C(9)=C(10) F-statistic8.370558Probability0.000003 Chi-square58.59391Probability0.000000 Table 4. Wald Test for all the Regions

17 S TATISTICAL A NAYLSIS Dependent Variable: SAT Method: Least Squares Date: 12/03/08 Time: 13:19 Sample: 1 51 Included observations: 51 VariableCoefficientStd. Errort-StatisticProb. PERCENT_NO_HS-4.6038351.060346-4.3418240.0001 PERCENT_TAKING-2.5596090.289039-8.8555740.0000 POPULATION-2.76E-060.000633-0.0043540.9965 TEACHER_PAY-0.1883690.834608-0.2256970.8226 REGION_WSC33.1969419.442611.7074330.0959 REGION_WNC41.5866017.304772.4031870.0212 REGION_SA20.9528415.596101.3434670.1871 REGION_NE60.2215115.999223.7640290.0006 REGION_MTN-14.8267915.55241-0.9533440.3464 REGION_MA60.1660918.879893.1867820.0029 REGION_ESC62.1584822.313792.7856530.0083 REGION_ENS30.2057515.312251.9726520.0558 C1240.11146.1399126.877200.0000 R-squared0.929925 Mean dependent var1061.176 Adjusted R-squared0.907796 S.D. dependent var68.08134 S.E. of regression20.67293 Akaike info criterion9.111091 Sum squared resid16240.05 Schwarz criterion9.603517 Log likelihood-219.3328 F-statistic42.02320 Durbin-Watson stat2.834804 Prob(F-statistic)0.000000 Table 5. Regress SAT on all the independent variables

18 C ONCLUSIONS As education level of state increases, the average SAT Scores decreases As average teacher pay increases test scores tend to decrease Differences in regional test scores are significant Test scores decreased as the percentage of the population taking the test increased

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