library(wooldridge)## Warning: package 'wooldridge' was built under R version 4.4.2data("discrim")head(discrim)## psoda pfries pentree wagest nmgrs nregs hrsopen emp psoda2 pfries2 pentree2
## 1 1.12 1.06 1.02 4.25 3 5 16.0 27.5 1.11 1.11 1.05
## 2 1.06 0.91 0.95 4.75 3 3 16.5 21.5 1.05 0.89 0.95
## 3 1.06 0.91 0.98 4.25 3 5 18.0 30.0 1.05 0.94 0.98
## 4 1.12 1.02 1.06 5.00 4 5 16.0 27.5 1.15 1.05 1.05
## 5 1.12 NA 0.49 5.00 3 3 16.0 5.0 1.04 1.01 0.58
## 6 1.06 0.95 1.01 4.25 4 4 15.0 17.5 1.05 0.94 1.00
## wagest2 nmgrs2 nregs2 hrsopen2 emp2 compown chain density crmrte state
## 1 5.05 5 5 15.0 27.0 1 3 4030 0.0528866 1
## 2 5.05 4 3 17.5 24.5 0 1 4030 0.0528866 1
## 3 5.05 4 5 17.5 25.0 0 1 11400 0.0360003 1
## 4 5.05 4 5 16.0 NA 0 3 8345 0.0484232 1
## 5 5.05 3 3 16.0 12.0 0 1 720 0.0615890 1
## 6 5.05 3 4 15.0 28.0 0 1 4424 0.0334823 1
## prpblck prppov prpncar hseval nstores income county lpsoda
## 1 0.1711542 0.0365789 0.0788428 148300 3 44534 18 0.11332869
## 2 0.1711542 0.0365789 0.0788428 148300 3 44534 18 0.05826885
## 3 0.0473602 0.0879072 0.2694298 169200 3 41164 12 0.05826885
## 4 0.0528394 0.0591227 0.1366903 171600 3 50366 10 0.11332869
## 5 0.0344800 0.0254145 0.0738020 249100 1 72287 10 0.11332869
## 6 0.0591327 0.0835001 0.1151341 148000 2 44515 18 0.05826885
## lpfries lhseval lincome ldensity NJ BK KFC RR
## 1 0.05826885 11.90699 10.70401 8.301521 1 0 0 1
## 2 -0.09431065 11.90699 10.70401 8.301521 1 1 0 0
## 3 -0.09431065 12.03884 10.62532 9.341369 1 1 0 0
## 4 0.01980261 12.05292 10.82707 9.029418 1 0 0 1
## 5 NA 12.42561 11.18840 6.579251 1 1 0 0
## 6 -0.05129331 11.90497 10.70358 8.394799 1 1 0 0mean(discrim$prpblck)## [1] NAsd(discrim$prpblck)## [1] NAmean(discrim$income)## [1] NAsd(discrim$income)## [1] NAsum(is.na(discrim$prpblck))## [1] 1sum(is.na(discrim$income))## [1] 1mean(discrim$prpblck,na.rm = TRUE)## [1] 0.1134864sd(discrim$prpblck,na.rm = TRUE)## [1] 0.1824165mean(discrim$income, na.rm = TRUE)## [1] 47053.78sd(discrim$income, na.rm = TRUE)## [1] 13179.29library(vtable)## Warning: package 'vtable' was built under R version 4.4.2## Zorunlu paket yükleniyor: kableExtra## Warning: package 'kableExtra' was built under R version 4.4.2sumtable(discrim, summ=c('notNA(x)', 'countNA(x)', 'mean(x)','sd(x)'),out='return')## Variable NotNA CountNA Mean Sd
## 1 psoda 402 8 1 0.089
## 2 pfries 393 17 0.92 0.11
## 3 pentree 398 12 1.3 0.64
## 4 wagest 390 20 4.6 0.35
## 5 nmgrs 404 6 3.4 1
## 6 nregs 388 22 3.6 1.2
## 7 hrsopen 410 0 14 2.8
## 8 emp 404 6 18 9.4
## 9 psoda2 388 22 1 0.094
## 10 pfries2 382 28 0.94 0.11
## 11 pentree2 386 24 1.4 0.65
## 12 wagest2 389 21 5 0.25
## 13 nmgrs2 404 6 3.5 1.1
## 14 nregs2 388 22 3.6 1.2
## 15 hrsopen2 399 11 14 2.8
## 16 emp2 397 13 18 8.6
## 17 compown 410 0 0.34 0.48
## 18 chain 410 0 2.1 1.1
## 19 density 409 1 4562 5132
## 20 crmrte 409 1 0.053 0.047
## 21 state 410 0 1.2 0.39
## 22 prpblck 409 1 0.11 0.18
## 23 prppov 409 1 0.071 0.067
## 24 prpncar 409 1 0.11 0.12
## 25 hseval 409 1 147399 56070
## 26 nstores 410 0 3.1 1.8
## 27 income 409 1 47054 13179
## 28 county 410 0 14 8
## 29 lpsoda 402 8 0.04 0.085
## 30 lpfries 393 17 -0.088 0.12
## 31 lhseval 409 1 12 0.39
## 32 lincome 409 1 11 0.28
## 33 ldensity 409 1 8 1
## 34 NJ 410 0 0.81 0.39
## 35 BK 410 0 0.42 0.49
## 36 KFC 410 0 0.2 0.4
## 37 RR 410 0 0.24 0.43discrimreg <- lm(psoda~prpblck+income, data = discrim)
summary(discrimreg)##
## Call:
## lm(formula = psoda ~ prpblck + income, data = discrim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29401 -0.05242 0.00333 0.04231 0.44322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.563e-01 1.899e-02 50.354 < 2e-16 ***
## prpblck 1.150e-01 2.600e-02 4.423 1.26e-05 ***
## income 1.603e-06 3.618e-07 4.430 1.22e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08611 on 398 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.06422, Adjusted R-squared: 0.05952
## F-statistic: 13.66 on 2 and 398 DF, p-value: 1.835e-06basitdiscrimreg <- lm(psoda~prpblck, data = discrim)
summary(basitdiscrimreg)##
## Call:
## lm(formula = psoda ~ prpblck, data = discrim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30884 -0.05963 0.01135 0.03206 0.44840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.03740 0.00519 199.87 < 2e-16 ***
## prpblck 0.06493 0.02396 2.71 0.00702 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0881 on 399 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.01808, Adjusted R-squared: 0.01561
## F-statistic: 7.345 on 1 and 399 DF, p-value: 0.007015logdiscrimreg <- lm(log(psoda)~prpblck+log(income), data = discrim)
summary(logdiscrimreg)##
## Call:
## lm(formula = log(psoda) ~ prpblck + log(income), data = discrim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33563 -0.04695 0.00658 0.04334 0.35413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.79377 0.17943 -4.424 1.25e-05 ***
## prpblck 0.12158 0.02575 4.722 3.24e-06 ***
## log(income) 0.07651 0.01660 4.610 5.43e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0821 on 398 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.06809, Adjusted R-squared: 0.06341
## F-statistic: 14.54 on 2 and 398 DF, p-value: 8.039e-07paste( (0.2*100)*0.122, "yüzdelik artış")## [1] "2.44 yüzdelik artış"logdiscrimregprpov <- lm(log(psoda)~prpblck+log(income)+prppov, data = discrim)
summary(logdiscrimregprpov)##
## Call:
## lm(formula = log(psoda) ~ prpblck + log(income) + prppov, data = discrim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32218 -0.04648 0.00651 0.04272 0.35622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.46333 0.29371 -4.982 9.4e-07 ***
## prpblck 0.07281 0.03068 2.373 0.0181 *
## log(income) 0.13696 0.02676 5.119 4.8e-07 ***
## prppov 0.38036 0.13279 2.864 0.0044 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08137 on 397 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.08696, Adjusted R-squared: 0.08006
## F-statistic: 12.6 on 3 and 397 DF, p-value: 6.917e-08cor(log(discrim$income), discrim$prppov, use = "complete.obs")## [1] -0.838467yorum:prblck katsayısı, tüm koşullar sabitken soda fiyatlarının artmasıyla ilişkilidir. Ancak gelir kontrol edilmediğinde ayrımcılık etkisi azalabilir. prppov (bölgedeki yoksulluk oranı) modele eklendiğinde, prblck (bölgedeki siyahi oran) katsayısının düşmesine neden olur. Bu ilişki negatif korelasyon gösterir ve mantıklıdır, çünkü gelir düşüşleri daha yüksek yoksulluk oranlarıyla ilişkilidir. Ancak, log(income) ve prppov arasındaki güçlü ilişki nedeniyle bu iki değişkenin aynı regresyon modeline dahil edilmesi, mükemmel bir doğrusal sonuç veremez.