library(wooldridge)
data(bwght, package = 'wooldridge')
library(rmarkdown)
paged_table(bwght)
lm(bwght ~ cigs + faminc, data = bwght)
##
## Call:
## lm(formula = bwght ~ cigs + faminc, data = bwght)
##
## Coefficients:
## (Intercept) cigs faminc
## 116.97413 -0.46341 0.09276
lm(bwght$bwght ~ bwght$cigs + bwght$faminc)
##
## Call:
## lm(formula = bwght$bwght ~ bwght$cigs + bwght$faminc)
##
## Coefficients:
## (Intercept) bwght$cigs bwght$faminc
## 116.97413 -0.46341 0.09276
bwght$bwghtlbs <- bwght$bwght / 16
lm(bwght$bwghtlbs ~ bwght$cigs + bwght$faminc)
##
## Call:
## lm(formula = bwght$bwghtlbs ~ bwght$cigs + bwght$faminc)
##
## Coefficients:
## (Intercept) bwght$cigs bwght$faminc
## 7.310883 -0.028963 0.005798
lm(I(bwght/16) ~ cigs + faminc, data = bwght)
##
## Call:
## lm(formula = I(bwght/16) ~ cigs + faminc, data = bwght)
##
## Coefficients:
## (Intercept) cigs faminc
## 7.310883 -0.028963 0.005798
lm(bwght ~ I(cigs/20) + faminc, data = bwght)
##
## Call:
## lm(formula = bwght ~ I(cigs/20) + faminc, data = bwght)
##
## Coefficients:
## (Intercept) I(cigs/20) faminc
## 116.97413 -9.26815 0.09276
model_1<- lm(bwght ~ cigs + faminc, data = bwght)
model_2<- lm(I(bwght/16) ~ cigs + faminc, data = bwght)
model_3<- lm(bwght ~ I(cigs/20) + faminc, data = bwght)
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
stargazer(list(model_1,model_2,model_3), type = "text")
##
## =================================================================
## Dependent variable:
## ---------------------------------
## bwght I(bwght/16) bwght
## (1) (2) (3)
## -----------------------------------------------------------------
## cigs -0.463*** -0.029***
## (0.092) (0.006)
##
## I(cigs/20) -9.268***
## (1.832)
##
## faminc 0.093*** 0.006*** 0.093***
## (0.029) (0.002) (0.029)
##
## Constant 116.974*** 7.311*** 116.974***
## (1.049) (0.066) (1.049)
##
## -----------------------------------------------------------------
## Observations 1,388 1,388 1,388
## R2 0.030 0.030 0.030
## Adjusted R2 0.028 0.028 0.028
## Residual Std. Error (df = 1385) 20.063 1.254 20.063
## F Statistic (df = 2; 1385) 21.274*** 21.274*** 21.274***
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
library(wooldridge)
data(hprice2)
library(rmarkdown)
paged_table(hprice2)
lm(scale(price) ~ 0 + scale(nox) + scale(crime) + scale(rooms) + scale(dist) + scale(stratio), data = hprice2)
##
## Call:
## lm(formula = scale(price) ~ 0 + scale(nox) + scale(crime) + scale(rooms) +
## scale(dist) + scale(stratio), data = hprice2)
##
## Coefficients:
## scale(nox) scale(crime) scale(rooms) scale(dist) scale(stratio)
## -0.3404 -0.1433 0.5139 -0.2348 -0.2703
lm(log(price) ~ log(nox) + rooms , data = hprice2)
##
## Call:
## lm(formula = log(price) ~ log(nox) + rooms, data = hprice2)
##
## Coefficients:
## (Intercept) log(nox) rooms
## 9.2337 -0.7177 0.3059
ornek6_2<-lm(log(price) ~ log(nox) + log(dist)+ rooms + I(rooms^2) + stratio , data = hprice2)
summary(ornek6_2)
##
## Call:
## lm(formula = log(price) ~ log(nox) + log(dist) + rooms + I(rooms^2) +
## stratio, data = hprice2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04285 -0.12774 0.02038 0.12650 1.25272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.385477 0.566473 23.630 < 2e-16 ***
## log(nox) -0.901682 0.114687 -7.862 2.34e-14 ***
## log(dist) -0.086781 0.043281 -2.005 0.04549 *
## rooms -0.545113 0.165454 -3.295 0.00106 **
## I(rooms^2) 0.062261 0.012805 4.862 1.56e-06 ***
## stratio -0.047590 0.005854 -8.129 3.42e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2592 on 500 degrees of freedom
## Multiple R-squared: 0.6028, Adjusted R-squared: 0.5988
## F-statistic: 151.8 on 5 and 500 DF, p-value: < 2.2e-16
ornek6_2_poly<-lm(log(price) ~ log(nox) + log(dist)+ poly(rooms, 2, raw = TRUE ) + stratio , data = hprice2)
summary(ornek6_2_poly)
##
## Call:
## lm(formula = log(price) ~ log(nox) + log(dist) + poly(rooms,
## 2, raw = TRUE) + stratio, data = hprice2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04285 -0.12774 0.02038 0.12650 1.25272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.385477 0.566473 23.630 < 2e-16 ***
## log(nox) -0.901682 0.114687 -7.862 2.34e-14 ***
## log(dist) -0.086781 0.043281 -2.005 0.04549 *
## poly(rooms, 2, raw = TRUE)1 -0.545113 0.165454 -3.295 0.00106 **
## poly(rooms, 2, raw = TRUE)2 0.062261 0.012805 4.862 1.56e-06 ***
## stratio -0.047590 0.005854 -8.129 3.42e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2592 on 500 degrees of freedom
## Multiple R-squared: 0.6028, Adjusted R-squared: 0.5988
## F-statistic: 151.8 on 5 and 500 DF, p-value: < 2.2e-16
library(car)
## Zorunlu paket yükleniyor: carData
Anova(ornek6_2_poly)
## Anova Table (Type II tests)
##
## Response: log(price)
## Sum Sq Df F value Pr(>F)
## log(nox) 4.153 1 61.8129 2.341e-14 ***
## log(dist) 0.270 1 4.0204 0.04549 *
## poly(rooms, 2, raw = TRUE) 14.838 2 110.4188 < 2.2e-16 ***
## stratio 4.440 1 66.0848 3.423e-15 ***
## Residuals 33.595 500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
data("attend")
(ornek6_3 <- lm(stndfnl~ atndrte*priGPA + ACT + I(priGPA^2) + I(ACT^2), data=attend))
##
## Call:
## lm(formula = stndfnl ~ atndrte * priGPA + ACT + I(priGPA^2) +
## I(ACT^2), data = attend)
##
## Coefficients:
## (Intercept) atndrte priGPA ACT I(priGPA^2)
## 2.050293 -0.006713 -1.628540 -0.128039 0.295905
## I(ACT^2) atndrte:priGPA
## 0.004533 0.005586
max(attend$priGPA)
## [1] 3.93
min(attend$priGPA)
## [1] 0.857
mean(attend$priGPA)
## [1] 2.586775
katsayi <- coef(ornek6_3)
katsayi["atndrte"]
## atndrte
## -0.006712928
katsayi["atndrte:priGPA"]
## atndrte:priGPA
## 0.005585907
katsayi["atndrte"] + mean(attend$priGPA)*katsayi["atndrte:priGPA"]
## atndrte
## 0.007736558
library(car)
linearHypothesis(ornek6_3, c("atndrte + 2.59*atndrte:priGPA"))
## Linear hypothesis test
##
## Hypothesis:
## atndrte + 2.59 atndrte:priGPA = 0
##
## Model 1: restricted model
## Model 2: stndfnl ~ atndrte * priGPA + ACT + I(priGPA^2) + I(ACT^2)
##
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 674 519.34
## 2 673 512.76 1 6.5772 8.6326 0.003415 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
data(gpa2)
ornek6_5 <- lm(colgpa~sat+ hsperc + hsize + I(hsize^2), data=gpa2 )
summary(ornek6_5)
##
## Call:
## lm(formula = colgpa ~ sat + hsperc + hsize + I(hsize^2), data = gpa2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57543 -0.35081 0.03342 0.39945 1.81683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.493e+00 7.534e-02 19.812 < 2e-16 ***
## sat 1.492e-03 6.521e-05 22.886 < 2e-16 ***
## hsperc -1.386e-02 5.610e-04 -24.698 < 2e-16 ***
## hsize -6.088e-02 1.650e-02 -3.690 0.000228 ***
## I(hsize^2) 5.460e-03 2.270e-03 2.406 0.016191 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5599 on 4132 degrees of freedom
## Multiple R-squared: 0.2781, Adjusted R-squared: 0.2774
## F-statistic: 398 on 4 and 4132 DF, p-value: < 2.2e-16
tahmin_verileri = data.frame(sat=1200, hsperc=30, hsize=5)
tahmin_verileri
## sat hsperc hsize
## 1 1200 30 5
tahmin_verileri = data.frame(sat=1200, hsperc=30, hsize=5)
tahmin_verileri
## sat hsperc hsize
## 1 1200 30 5
predict(ornek6_5, tahmin_verileri, interval = "confidence" )
## fit lwr upr
## 1 2.700075 2.661104 2.739047