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A researcher used 1998 GSS data (n=1235) and 2014 GSS data (n=2,426) to answer two questions: 1) whether the level of education is associated with occupational prestige and 2) whether the association is consistent across years (1994 vs. 2014). The level of education is measured in year of school completed and the occupational prestige ranges from 17 to 80 with a higher score indicating a more prestigious job. She used STATA to run the statistical models and her regression readouts in STATA are listed below. STATA OUTPUT 1: Association between respondent's education and respondent's occupational prestige in 1998 Source Model 67186.0723 161644.846 Residual Total prestg80 educ _cons Source Model Residual SS Total 228830.918 educ cons SS Coef. Std. Err. 2.553622 .1128019 11.26701 1.565999 116706.946 325840.772 df 442547.718 prestg10 Coefficient 1 1,233 1,234 185.438345 df 1 2,424 MS 67186.0723 131.098821 STATA OUTPUT 2: Association between respondent's education and occupational prestige in 2014 Std. err. 0770969 2.271688 12.39026 1.086972 t P>|t| 0.000 7.19 0.000 MS 2,425 182.493905 Number of obs F(1, 1233) Prob > F = R-squared Adj R-squared = Root MSE 116706.946 134.422761 t Number of obs F(1, 2424) Prob > F R-squared P>|t| = 0.000 29.47 11.40 0.000 = = [95% Conf. Intervall 2.332317 2.774927 8.194689 14.33932 = Adj R-squared = Root MSE = 10.25877 1,235 512.48 0.0000 0.2930 11.45 = 2,426 868.21 0.0000 0.2634 11.594 [95% conf. intervall 14.52175

A researcher used 1998 GSS data (n=1235) and 2014 GSS data (n=2,426) to answer two questions: 1) whether the level of education is associated with occupational prestige and 2) whether the association is consistent across years (1994 vs. 2014). The level of education is measured in year of school completed and the occupational prestige ranges from 17 to 80 with a higher score indicating a more prestigious job. She used STATA to run the statistical models and her regression readouts in STATA are listed below. STATA OUTPUT 1: Association between respondent's education and respondent's occupational prestige in 1998 Source Model 67186.0723 161644.846 Residual Total prestg80 educ _cons Source Model Residual SS Total 228830.918 educ cons SS Coef. Std. Err. 2.553622 .1128019 11.26701 1.565999 116706.946 325840.772 df 442547.718 prestg10 Coefficient 1 1,233 1,234 185.438345 df 1 2,424 MS 67186.0723 131.098821 STATA OUTPUT 2: Association between respondent's education and occupational prestige in 2014 Std. err. 0770969 2.271688 12.39026 1.086972 t P>|t| 0.000 7.19 0.000 MS 2,425 182.493905 Number of obs F(1, 1233) Prob > F = R-squared Adj R-squared = Root MSE 116706.946 134.422761 t Number of obs F(1, 2424) Prob > F R-squared P>|t| = 0.000 29.47 11.40 0.000 = = [95% Conf. Intervall 2.332317 2.774927 8.194689 14.33932 = Adj R-squared = Root MSE = 10.25877 1,235 512.48 0.0000 0.2930 11.45 = 2,426 868.21 0.0000 0.2634 11.594 [95% conf. intervall 14.52175

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 4BGP
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A researcher used 1998 GSS data (n=1235) and 2014 GSS data (n=2,426) to answer two questions: 1) whether the level of education is associated with occupational prestige and 2) whether the association is consistent across years (1994 vs. 2014). The level of education is measured in year of school completed and the occupational prestige ranges from 17 to 80 with a higher score indicating a more prestigious job. She used STATA to run the statistical models and her regression readouts in STATA are listed below. STATA OUTPUT 1: Association between respondent's education and respondent's occupational prestige in 1998 Source Model Residual Total prestg80 educ _cons Source Model Residual Total SS 67186.0723 161644.846 educ _cons 228830.918 Coef. Std. Err. SS df 2.553622 .1128019 11.26701 1.565999 116706.946 325840.772 442547.718 1 67186.0723 1,233 131.098821 1,234 185.438345 2.271688 12.39026 df MS STATA OUTPUT 2: Association between respondent's education and occupational prestige in 2014 1 2,424 prestg10 Coefficient Std. err. 0770969 1.086972 t P>|t| 0.000 7.19 0.000 Number of obs F(1, 1233) Prob > F R-squared Adj R-squared = Root MSE = = MS 2,425 182.493905 116706.946 134.422761 t P>|t| 2.332317 8.194689 Number of obs F(1, 2424) Prob > F R-squared Adj R-squared Root MSE 29.47 0.000 11.40 0.000 = [95% Conf. Interval] 10.25877 = = = 1,235 512.48 0.0000 = 0.2930 11.45 = 2.774927 14.33932 2,426 868.21 0.0000 0.2634 11.594 [95% conf. intervall 14.52175
Transcribed Image Text:A researcher used 1998 GSS data (n=1235) and 2014 GSS data (n=2,426) to answer two questions: 1) whether the level of education is associated with occupational prestige and 2) whether the association is consistent across years (1994 vs. 2014). The level of education is measured in year of school completed and the occupational prestige ranges from 17 to 80 with a higher score indicating a more prestigious job. She used STATA to run the statistical models and her regression readouts in STATA are listed below. STATA OUTPUT 1: Association between respondent's education and respondent's occupational prestige in 1998 Source Model Residual Total prestg80 educ _cons Source Model Residual Total SS 67186.0723 161644.846 educ _cons 228830.918 Coef. Std. Err. SS df 2.553622 .1128019 11.26701 1.565999 116706.946 325840.772 442547.718 1 67186.0723 1,233 131.098821 1,234 185.438345 2.271688 12.39026 df MS STATA OUTPUT 2: Association between respondent's education and occupational prestige in 2014 1 2,424 prestg10 Coefficient Std. err. 0770969 1.086972 t P>|t| 0.000 7.19 0.000 Number of obs F(1, 1233) Prob > F R-squared Adj R-squared = Root MSE = = MS 2,425 182.493905 116706.946 134.422761 t P>|t| 2.332317 8.194689 Number of obs F(1, 2424) Prob > F R-squared Adj R-squared Root MSE 29.47 0.000 11.40 0.000 = [95% Conf. Interval] 10.25877 = = = 1,235 512.48 0.0000 = 0.2930 11.45 = 2.774927 14.33932 2,426 868.21 0.0000 0.2634 11.594 [95% conf. intervall 14.52175
2-5) Using information STATA OUTPUT and OUTPUT2, explain whether the association between education and occupational prestige is consistent across years (1994 vs. 2014) Using information STATA OUTPUT1, calculate t-statistics (orang box). Is the association between respondent's education and respondent's occupational prestige statistically significant? How can you tell? Using information OUTPUT2, calculate 95% confidence interval of the coefficient of spousal's education (green box). Interpret 95% confidence interval
Transcribed Image Text:2-5) Using information STATA OUTPUT and OUTPUT2, explain whether the association between education and occupational prestige is consistent across years (1994 vs. 2014) Using information STATA OUTPUT1, calculate t-statistics (orang box). Is the association between respondent's education and respondent's occupational prestige statistically significant? How can you tell? Using information OUTPUT2, calculate 95% confidence interval of the coefficient of spousal's education (green box). Interpret 95% confidence interval
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