Econ 175 Term Paper - F]; KST D RAFT Academic Performance...

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Unformatted text preview: F]; KST D RAFT Academic Performance Index for K-12 Asian and White Population in Santa Clara County 2008 David Chock Economics 175 April 8, 2010 Growing up in Silicon Valley, I often heard about how Asians study too hard and that they are overachievers in school. This stereotype was picked up by the Wall Street Journal in a 2005 article stating: Some whites fear that by avoiding schools with large Asian populations parents are short changing their own children, giving them the idea that they can't compete with Asian kids. ...To many of Cupertino's Asians, some of the assumptions made by white paren -- that Asians are excessively competitive and single—minded -- play into stereotypes. . .. “Certain stereotypes come to mind -- 'those people [Asian] are good at math,‘ " he [Principal Michael Doran of a high school in Rockville, Md] says.l began my research to search for sufficient evidence for these stereot es, and to see if Asians really noticeabl ou erform in school or that artic 'ke these are way overblown out of proportion. I first obtained educational data for Santa Clara County, which is at the heart of Silicon Valley, and found that Asians may have some edge in certain cases, but not overall. Even with this in mind, I believe that Asian students are not more intelligent than non-Asian students. JEWWWW- While Asians have a consistent overall Academic Performance Index (API) advantage from the same school; Whites from one school can do better than Asians from another school; Asians don’t necessarily have higher API scores in schools where they are a larger percentage of the population compared to schools where they have a lower percentage; and young Asians definitely take advantage of pre-school and afterschool programs. I began my journey looking for the data that is the basis of the “Asians are over achievers” stereotype. Figure 1 displays the 2008 API scores for Asian and White populations at Santa Clara County high schools where both scores are defined. Since the majority of the students are of Asian and White descent in Santa Clara County, I have decided to simplify comparisons with just these two ethnicities. The API Score is the composite score for all of the students taking all of the California Standard Tests (CST) state tests at that school from grades 2-11. It has a range Chock - 1 - from 200 to 1000 with 800 as the statewide performance target for that school. From the data in the figure, except for High School #5 in the chart, Asian API scores are slightly higher than the White API score for the same school. The difference between the API score ranges between -2 and 102 with the average of 55.9 and a median of 62. In a valid range of 800 for API scores a ________________________.1 score of 62 is about 7.8% of the overall range. It is quite surprising that the edge is pervasive, but the data doesn’t show sheer dominance. Figure 1 is also constructed so that Asian API scores ascend from left to right. What should be noted is that when all factors for a school are held constant, Asians almost always perform slightly better on average. However, the White API at a high Asian performing school is higher than the Asian API at the lower performing Asian schools on the far left of Figure 1. In fact looking at Table l, the White API score of High School #23 is 897 and it is higher than the Asian API scores for High Schools #8, #10, and #12, which have large Asian populations of 40%, 48%, and 52% of their overall respective school populations. Furthermore, for High School #23, the white population is 43%, which is also a sizable figure. Instead of the Asian population of a high school engendering “White flight” or fear as stated in the Wall Street Journal article in the introduction , the Asian API and the White API are correlated with R=0.90. R is the correlation coefficientz, which can be between -1 to 1. If R is highly positive, then when one variable goes up, so does the other. If R is highly negative, when one variable goes up, the other one goes down. For social science work, R with a magnitude or absolute value less than 0.50 shows that the two variables may have no correlation. R2 is used to predict future occurrences. It is the proportion of variability in a data set that is accounted for by the correlation. R2 provides a measure of how likely the correlation will be predicted.3 Ideally R2 > 0.50 for a strong correlation; this means that the probability of an expected result for a future Chock - 2 - occurrence is greater than 50%. In this case, R2 =0.8 l , which explains how close the Asian API and White API correlate with each other in a school. This might imply that an advance in W success in test scores for either of these two races usually creates a more competitive environment which induces a rise in test segres overall.</(W‘l\“L ‘Wc’k/ k/lo/N‘v ' “~\. V— , Al‘fi Figure 2 examines whether more Asians mean a higher Asian score and how does that W wk M affect its counterpart White API score. According to the graph, there is no correlation in Asian a W Wins; API and Asian percent of school population, since R2 =O.20. This could be explained because it , {7, is easier to have a small population score well on tests than a very large population. However, this also says that with a large population of Asians, sometimes certain schools do very well and some do not do as well. The ones that do well may be more conducive to a good, competitive educational environment. The stereotypical hard working nerd character on television shows may be the outcast, but when most people are studying hard at school, “It’s cool to be smart”. In any case, it is beyond the scope of this data to know why some schools perform better with a large Asian population and some not as well. When the Asian p0pulation increases, there is no relationship to the White API as R2 =0.06.> These measures imply that a difference in Asian ‘population does not stron 1 affect overall test scores. As the data examined so far has been high school data, the next step is to compare that with middle and elementary school data. Since many of these schools in Santa Clara County don’t have both Asian API and White API numbers and since there are so many of these schools, I chose to focus on the schools that are in wealthier cities, which in this study are Cupertino, Los Altos, Palo Alto, and Saratoga. one of the reasons for choosing wealthy schools in particular to focus on is because parents and students are more likely to be able to have financial resources for tutoring, pre-school, afterschool programs and other academic endeavors should they choose to Chock - 3 - participate in them. The schools in these cities all have both Asian and White API scores. In Table 2, note how wealthy these cities are compared to the largest city of San Jose in Santa Clara County. While San Jose has an approximate median house value of $483,000, the four wealthy cities have median house values approaching or over a $1,000,000. Asians have been flocking to these cities much more than they have to San Jose or California overall. This doesn’t mean that Asians are generally wealthy, but it does mean that when they have the financial means, they are congregating in areas with good schools as shown in Figure 3. California schools have a target API of 800 with 900 being for top schools. Note that Cupertino, which is known for top schools, has an Asian population of 57%, whereas it just had a 23% Asian population just 18 years ago in 1990; it’s one of the first majority Asian cities in the country. In Figure 3, the Asian API is always above the White API for each middle and elementary school for the four wealthy cities in Santa Clara County. The White API doesn’t closely follow the Asian API as closely or as smoothly as in Figure 1. Their R=0.78 with R2 =0.61 meaning that they are definitely correlated, but not as closely as in Figure 1. The API differences are actually closer in the middle/elementary schools than in the high school with the average difference between the Asian and White API is 51.5 with the median being 38. So sometimes the APIs are very close, but at times they pull further away from each other. The average API difference is about 6.4% of the 800 range of valid API scores, so there is not an overwhelming, consistent advantage in favor of Asians. During elementary and middle school, parents spend more time educating their child and the child is more likely to accept what their parents. This wealthy group of parents may choose additional academic programs for their children. There are many pre—school programs and afterschool programs that are predominantly Asian. A blog and a picture for pre-school program Chock - 4 - Challenger Middle School in Saratoga show that there are 11 out of 15 Indians in a class and a 12 out of 15 Asians 'in another class. For afterschool program Kumon, the director says that almost all classes are all Asian. The manager at the Score! afterschool program said that their enrollment is approximately 80% Asian. This doesn’t mean that White people are not taking advantage of pre-school or afterschool academic programs, but that some Asian children are definitely participating in these programs and in some of these programs, Asians are the vast majority. One explanation for the graph is that sometimes White children get extra academic . @335“, mu” help and sometimes they don t and that shows the cho mess of the White API. 0 m: “LL ., w m rm, .After examining the available statistical data for all Santa Clara County high schools 311% t r the middle / elementary schools for four wealthy cities in Santa Clara County, schools with It 45W #9 MW Asian and White APIs track much closer than the hype given by the Wall Street Journal article in “Nb my]?! . . . . . . law Mew/c? ' the introduction. While it’s clear that the ASIan API scores are almost always higher than the p m M corresponding White API scores for the same school, the percent difference is not large. Also, ml 040 . the White API scores from some highly performing schools are higher than the Asian API scores PM for a great number of schools. The most significant outcome of a higher API for Asians is that colleges give a certain top percentage of students from each high school favoritism in the college admission rocess even if ercenta e of students are on] sli htl better e st. Chock - 5 - Tables and Figures Figure 1. High Schools in Santa Clara County with Asian and White API Index for 2008 E 8 a, 600 E < 2 3 4 5 e 7 a 91011121314151617181920212223 HighSchool +Asian API Whites API Source: Rand California4 htt ://ca.rand.or stats/education/a i.html Table 1. Selected Santa Clara County High School API Scores from Figure l -- School % Asian White API API Saratoga High - School 23 52 43 959 897 High 5 School 8 40 13 851 792 Piedmont Hills High - School 10 48 13 867 765 High - School 12 52 33 889 817 Source: Rand California4 htt ://ca.rand.or Istats/education/e i.html Chock - 6 - Figure 2. High Schools in Santa Clara County with Asian and White API Index 2008 S fokebij R2 = 0.20 Asian-API ' _ __ R2 = 0.06 White API 1‘ a: I— o u U) E < 30 40 50 Percent Asian Population 0- Asian API I Whites API —Linear (Asian API ) —Linear (Whites API) Source: Rand California4 http:f/ca.rand.org/stats/educatiOn/api.html Table 2: Median Household Income, Median Home Value, Growth of Asian Population from 1990-2008 in Selected Wealthy Santa Clara County Cities, California, and the United States Zillow Median Estimated Change . Geography Househmd Asian: 2000 Asian:1990 from 1990- med'an Income: 2006-8 2008 Home ACS Value 4I612010 Cupertino 3341.050 Los Altos 31.341700 Palo Alto 31.174333 Saratoa 31.351270 SanJose 3433.334 California 3327.653 States $52,029 4.50% 3.60% 2.90% 55% Source: Income Distribution in 1999 of Households, 1990, 2000 for Santa Clara County, California, USA. US. Census Bureau, Census 2000 Summary File 3, Matrices P52, P53, P54, P79, P80, P81, PCT38, PCT40, and PCT41. Approximate data retrieved from ACS 2006-2008 surveys from US. Census Bureauf> http:f/factfinder.censusgov $181,359 Chock - 7 - Figure 3. Middle and Elementary Schools in Cupertino, Los Altos, Palo Alto, Saratoga in Santa Clara County with Asian and White API Index 2008 +Asian API 7' +Whites API API Score 3 5 7 91‘l13151719212325272931333537 Middle or Elementary School Source: Rand California4 http://ca.rand.org/stats/education/agi.html Chock - 8 - References Internet Sources: 1. “White Flight in Silicon Valley As Asian Students Move In”, Wall Street Journal Online, November 23, 2005, hgp://homes.wsj.com/buysell/markettrends/ZOOS1123-hwang.html (accessed 3/16/2010) 2. Wikipedia. “Correlation and Dependence”, hm;://en.wikipedia.org/wiki/Correlation coefficient (accessed 3/16/2010) 3. Wikipedia. “Coefficient of Determination”, hm://en.wikipedia.org/wiki/Coefficient of determination (accessed 3/16/2010) 4. Rand California. California Education Statistics. Source: Rand California. hgpz/lca.rand.org/stats/education/apihtml [Accessed through hng/www.santaclaracounglib.org/database/subiects/statistics mahtml and using a library card for free access] (accessed 4/8/2010) 5. Source: Income Distribution in 1999 of Households, 1990, 2000 for Santa Clara County, California, USA. US. Census Bureau, Census 2000 Summary File 3, Matrices P52, P53, P54, P79, P80, P81, PCT38, PCT40, and PCT41. Approximate data retrieved from ACS 2006-2008 surveys from US. Census Bureau. http://factfinder.census.gov (accessed 4/8/2010) Chock - 9 - C175 SPRING 2010 Academic Performance Index for K-12 Asian and White Population in Santa Clara County 2008 Clear motivation for your choice of tepic. Nice addition to include journal article quote as an illustration of popular opinions on this issue. I like your summary of findings in the introduction. This provides a succinct overview of the paper. Good, thorough description of the data. Well—cited references Good job using a range of sources to address various aspects of the question at hand. Interesting background information on the distribution of Asian students across California and recent trends. AUTHOR: David Chock TITLE: TOPIC - . DATA 0 D 0 ANALYSIS 0 CHARTS/GRAPES 0 CONCLUSION Q WRITING . . GRADE: A— GRADER: Creative, sensible analysis You make some interesting points in your analysis of the data. It would be nice if you could expand on some of them.‘ For example, concerning the correlation between test scores of students within schools — can you check if other factors also correlated, such as the education of parents or household income? You stated that one of your objectives is to determine “where and why” M the Asian test score advantage exists. Do you find any factors that appear to be correlated with the size of the API gap? Well-labeled figures and tables Great idea to include a table with more detailed information on a sample of schools (Table 1). Additional things you could include in Table 1 are: difference in API scores and total number of students. How many schools are included in Figure 2? Did you include all of the same schools in Figure 1? You note the important implication that class rank (or relative performance) has for college admissions. One extension of this might be to see if people from the same high school end up going to different colleges. Are there any other ways in which you could think of expanding your study? What data would you ideally have and what additional analysis would you perform? Clear progression of thoughts, well-structured arguments, thoughtful discussion of findings. You tackle a commonly-studied question but do a good job of presenting a fresh perspective and unique analysis. _ Kenny Aj ayi (kajayi@berkeley.edu) wad writ—tam 1714064? (Wt—cme [4440‘ we c ,4" Office Hours: Friday April 29"“, 10am—12pm in the Peixotto Room (6th floor of Evans Hall). Email me by Friday if you a-.. .......1..1... a... an“... 5.. 4......“ "£12.". La"... .—.1 .-. ....IA 111... 4.. «AL—Jul.— n— F.__-J—-4-—n nua- 5‘..- n—fisL.-_ +2-un gig—yr Giana 0!? ‘7?th {I Sontag / he PM At- “MT Academic Performance Index for K912 Asian and White Population in Santa Clara County ' 2008 David Chock Economics 175 May 3, 2010 Growing up in Silicon Valley, I ofien heard about how Asians study too hard and that they are overachievers in school. This stereotype was picked up by the Wall Street Journal in a 2005 article stating: Some whites fear that by avoiding schools with large Asian populations parents are short changing their own children, giving them the idea that they can't compete with Asian kids. . . .To many of Cupertino's Asians, some of the assumptions made by white parents -- that Asians are excessively competitive and single-minded -- play into stereotypes. . .. “Certain stereotypes come to mind -- 'those people [Asian] are good at math,’ " he [Principal Michael Doran of a high school in Rockville, Md] says.1 I began my research to search for sufficient evidence for these stereotypes, and to see if Asians really noticeably outperform in school or that articles like these are way overblown out of proportion. I first obtained educational data for Santa Clara County, which is at the heart of Silicon Valley, and found that Asians may have some edge in certain cases, but not overall. Even with this in mind, I believe that Asian students are not more intelligent than non-Asian students. If a certain ethnicity has an advantage, I would like to understand where and why. While Asians have a consistent overall Academic Performance Index (API) advantage from the same school; Whites fi'om one school can do better than Asians from another school; Asians don’t necessarily have higher API scores in schools where they are a larger percentage of the population compared to schools where they have a lower percentage; and young Asians definitely take advantage of pre-school and after—school programs. I began my journey looking for the data that is the basis of the “Asians are overachievers” stereotype. I chose Santa Clara County as the point of interest because it is one of the more heavily populated Asian counties. In addition, since the majority of the students are of Asian and White descent in Santa Clara County, I have decided to simplify comparisons with just these two ethnicities. Figure 1 displays the 2008 API scores for Asian and White populations at Santa Clara County high schools where both scores are defined. The API Score is the composite score for all of the students taking all of the California Standard Tests (CST) state tests at that school fiom grades 2-11. It has a Chock - 1 - range fi-om 200 to 1000 with 800 as the statewide performance target for that school. From the data in the figure, except for High School #5 in the chart, Asian API scores are slightly higher than the White API score for the same school. The difference between the API score ranges between -2 and 102 with the average of 55.9 and a median of 62. In a valid range of 800 for API scores, a score of 62 is about 7.8% of the overall range. It is quite surprising that the edge is pervasive, but the data doesn’t show sheer dominance. Figure l is also constructed so that Asian API scores ascend fi'om left to right. What should be noted is that when all factors for a school are held constant, Asians almost always perform slightly better on average. However, the White API at a high Asian performing school is higher than the Asian API at the lower performing Asian schools on the far left of Figure 1. In fact looking at Table 1, the White API score of High School #23 is 897 and it is higher than the Asian API scores for High Schools #8, #10, and #12, which have large Asian populations of 40%, 48%, and 52% of their overall respective school populations. Furthermore, for High School #23, the white population is 43%, which is also a sizable figure. Instead of the Asian population of a high school engendering “White fligh ” or fear as stated in the Wall Street Journal article in the introduction , the Asian API and the White API are correlated with R=0.90. R is the correlation coefficientz, which can be between -1 to 1. If R is highly positive, then when one variable goes up, so does the other. If R is highly negative, when one variable goes up, the other one goes down. For social science work, R with a magnitude or absolute value less than 0.50 shows that the two variables may have no correlation. R2 is used to predict future occurrences. It is the proportion of variability in a data set that is accounted for by the correlation. R2 provides a measure of how likely the correlation will be predicted.3 Ideally R2 > 0.50 for a strong correlation; this means that the probability of an expected result for a future occurrence is greater than 50%. In this case, R2 =0.81, which explains how close the Asian API and White API correlate with each other in a school. Chock - 2 - One reason for this may be that high test scores for either of these two races usually creates a more competitive environment which induces a rise in test scores overall. It could also suggest that other factors such as socioeconomic level of resident families and quality of teaching at a school cause similar test scores regardless of race. One of the barriers to understanding the persistent small magnitude of the gaps between White I and Asian API scores is the lack of data by race for each school. From the data sources that I could find, API and percentage of student body are the only data by ethnicity for each school. Ideally, such data would include other factors such as average household income and average education of parents. This kind of data would also allow comparisons among these factors regarding difference in API scores and show whether ethnicity and culture make a significant difference in test scores relative to other factors. Without this data, further study is beyond the score of this paper. Figure 2 examines whether more Asians mean a higher Asian score and how does that affect its counterpart White API score. According to the graph, there is no correlation in Asian API and Asian percent of school population, since R2 =0.20. This could be explained because it is easier to have a small population score well on tests than a very large population. However, this also says that with a large population of Asians, sometimes certain schools do very well and some do not do as well. The ones that do well may be more conducive to a good, competitive educational environment. The stereotypical hard-working nerd character on television shows may be the outcast, but when most people are studying hard at school, “It’s cool to be smart”. In any case, it is beyond the scope of this data to know why some schools perform better with a large Asian population and some not as well. When the Asian population increases, there is no relationship to the White API as R2 =0.06. These measures imply that a difference in Asian population does not strongly affect overall test scores. As the data examined so far has been high school data, the next step is to compare that with middle and elementary school data. Since many of these schools in Santa Clara County don’t have Chock - 3 - both Asian API and White API numbers and since there are so many of these schools, I chose to focus on the schools that are in wealthier cities, which in this study are Cupertino, Los Altos, Palo Alto, and ‘ Saratoga. One of the reasons for choosing wealthy schools in particular to focus on is because parents and students are more likely to be able to have financial resources for tutoring, pro-school, afier-school programs and other academic endeavors should they choose to participate in them. The schools in these cities all have both Asian and White API scores. In Table 2, note how wealthy these cities are compared to the largest city of San Jose in Santa Clara County. While San Jose has an approximate median house value of $483,000, the four wealthy cities have median house values approaching or over a $1,000,000. Asians have been flocking to these cities much more than they have to San Jose or California overall. This doesn’t mean that Asians are generally wealthy, but it does mean that when they have the financial means, they are congregating in areas with good schools as shown in Figure 3. California schools have a target API of 800 with 900 being for top schools. Note that Cupertino, which is known for top schools, has an Asian population of 57%, whereas it just had a 23% Asian population just 18 years ago in 1990; it’s one of the first majority Asian cities in‘ the country. In Figure 3, the Asian API is always above the White API for each middle and elementary school for the four wealthy cities in Santa Clara County. The White API doesn’t closely follow the Asian API as closely or as smoothly as in Figure 1. Their R=0.78 with R2 =0.61 meaning that they are definitely correlated, but not as closely as in Figure 1. The API differences are actually closer in the middle/elementary schools than in the high school with the average difi'erence between the Asian and White API is 51.5 with the median being 38. So sometimes the APIs are very close, but at times they pull fiirther away from each other. The average API difference is about 6.4% of the 800 range of valid API scores, so there is not an overwhelming, consistent advantage in favor of Asians. During elementary and middle school, parents spend more time educating their children, who are more likely to be influenced by their parent’s decisions for them. This wealthy group of parents ChoCk - 4 - may choose additional academic programs for their children. There are many pre-school programs and after-school programs that are predominantly Asian. A blog and a picture for pre-school program Challenger Middle School in Saratoga show that there are 11 out of 15 Indians in a class and a 12 out of 15 Asians in another class. For alter-school program Kumon, the director says that almost all classes are all Asian. The manager at the Score! after-school program said that their enrollment is approximately 80% Asian. This doesn’t mean that White people are not taking advantage of pre- school or after-school academic programs, but that some Asian children are definitely participating in these programs and in some of these programs, Asians are the vast majority. After examining the available statistical data for all Santa Clara County high schools and the middle / elementary schools for four wealthy cities in Santa Clara County, schools with Asian and White APIs track much closer than the hype given by the Wall Street Journal article in the introduction. While it’s clear that the Asian API scores are almost always higher than the corresponding White API scores for the same school, the percent difference is not large. Also, the White API scores from some highly performing schools are higher than the Asian API scores for a great number of schools. Ideally, data on average household income, average parent education, average number of hours spent after school in academic effort, and average immigrant generation for each ethnicity for every school would help bring together a more complete picture that accounts for differences in API scores. A future, well -funded study could garner data as to where students ended up matriculating to college. This would show whether Asian students having a slightly higher API ended up edging out their white counterparts or whether the API gap really did not make much of a difference in the end. Chock - 5 - Tables and Figures Figure 1. High Schools in Santa Clara County with Asian and White API Index for 2008 u: I- O u (D E < 2 3 4 5 6 7 8 91011121314151617181920212223 HighSchool '+"—/iéi"an API Whites—Rm Source: Rand California4 htt ://ca.rand.ore/stats/cducation/a i.html Table 1. Selected Santa Clara County High School API Scores from Figure 1 Difference Total % Asian Whites in API Number of School % Asian White API API Scores Students th— Saratoga PiEdmont 10 48 13 867 765 102 1646 SChOOI 23 959 397 981 Milpitas High - School 8 4O 13 851 792 59 2136 Hills High - School Cupertino High — School 12 52 33 889 817 72 1205 Somce: Rand California4 htt ://ca.rand.ore/stats/education/a i.html Chock - 6 - Figure 2. High Schools in Santa Clara County with Asian and White API Index 2008 ' R2 = 0.20 Asian APIE O API Score 30 40 50 Percent Asian Population r ; AslariAPl WITWhites KPI 4L1rié§ar (Asia—r1 API )1; LinearTWhites API)j * Note: Figure 2 plots the same 23 schools from Figure 1 Source: Rand California4 htt ://ca.rand.orJ stats/education/a i.html Table 2: Median Household Income, Median Home Value, Growth of Asian Population from 1990-2008 in Selected Wealthy Santa Clara County Cities, California, and the United States Value Zillow Median Household . , . _ Geography lncome:2006_8 Asnan. 2000 Asuan.1990 - ACS 41612010 Estimated $125,105 57.10% 44.40% 23.00% 148% $941,050 Change . from 1990- mire" 2008 20.60% 24.90% 37.30% 31.20% 12.50% $52,029 3.60% 2.90% 55% $181,359 States Source: Income Distribution in 1999 of Households, 1990, 2000 for Santa Clara County, California, USA. US. Census Bureau, Census 2000 Summary File 3, Matrices P52, P53, P54, P79, P80, P81, PCT38, PCT40, and PCT41. Approximate data retrieved from ACS 2006-2008 surveys from US. Census Bureau.5 http://factfinder.censusgov 4.50% Chock — 7 — Figure 3. Middle and Elementary Schools in Cupertino, Los Altos, Palo Alto, Saratoga in Santa Clara County with Asian i and White APl Index 2008 1050 1000 950 ,,i :o—Asiéh Am I: +Whites APIEE 900 API Score 850 800 13 5 7 91113151719212325272931333537 Middle or Elementary School Source: Rand California4 http://ca.rand.org/stats/educalion/agi.html Chock — 8 — References Internet Sources: 1. “White Flight in Silicon Valley As Asian Students Move In”, Wall Street Journal Online, November 23, 2005, http://homes.wsi.com/buysell/markettrends/ZOOS1 123-hwang.html (accessed 3/16/2010) 2. Wikipedia. “Correlation and Dependence”, ' hm» ://en.wikipedia.orglwiki/Correlation coefficient (accessed 3/16/2010) 3. Wikipedia. “Coefficient of Determination”; ht_tp://en.Wikipediaorngiki/Coeflicient of determination (accessed 3/ 16/2010) 4. Rand California. California Education Statistics. Source: Rand California. http://ca.rand.org[stats/education/api.html [Accessed through httg://www.santaclaracoungllib.orgidatabase/subjcots/statistics [pahtml and using a library card for free access] (accessed 4/8/2010) 5. Source: Income Distribution in 1999 of Households, 1990, 2000 for Santa Clara County, California, USA. US. Census Bureau, Census 2000 Summary File 3, Matrices P52, P53, P54, P79, P80, P81, PCT38, PCT40, and PCT41. Approximate data retrieved from ACS 2006-2008 surveys from US. Census Bureau. http://factfinder.census.gov (accessed 4/8/201 0) Chock - 9 — ...
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Econ 175 Term Paper - F]; KST D RAFT Academic Performance...

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