day4prlm

# day4prlm - 1/30/12 PADP 8130: Linear Models Mul\$variate OLS...

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1/30/12 1 PADP 8130: Linear Models Mul\$variate OLS PRACTICE Angela Fer9g, Ph.D. Bivariate OLS use day2.dta drop if faminc==0 | faminc>200000 recode educhd (0/10=11) reg faminc educhd Source | SS df MS Number of obs = 7913 -------------+------------------------------ F( 1, 7911) = 1337.84 Model | 2.0229e+12 1 2.0229e+12 Prob > F = 0.0000 Residual | 1.1962e+13 7911 1.5121e+09 R-squared = 0.1446 -------------+------------------------------ Adj R-squared = 0.1445 Total | 1.3985e+13 7912 1.7676e+09 Root MSE = 38886 ------------------------------------------------------------------------------ faminc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educhd | 8175.432 223.5163 36.58 0.000 7737.281 8613.583 _cons | -50710.55 2986.183 -16.98 0.000 -56564.26 -44856.84 ------------------------------------------------------------------------------

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1/30/12 2 Graphically twoway lfit faminc educhd, ysc(r(0 100000)) ylabel(0 25000 50000 75000 100000) 0 25000 50000 75000 100000 Fitted values 10 12 14 16 18 COMPLETED ED-HD Mul9variate OLS . reg faminc educhd femalehd Source | SS df MS Number of obs = 7913 -------------+------------------------------ F( 2, 7910) = 1449.03 Model | 3.7500e+12 2 1.8750e+12 Prob > F = 0.0000 Residual | 1.0235e+13 7910 1.2940e+09 R-squared = 0.2681 -------------+------------------------------
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## This note was uploaded on 03/28/2012 for the course PADP 8130 taught by Professor Fertig during the Spring '12 term at LSU.

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day4prlm - 1/30/12 PADP 8130: Linear Models Mul\$variate OLS...

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