asighnment1

library(wooldridge)

data("hprice2")

library(wooldridge)
tail(hprice2)

##     price crime  nox rooms dist radial proptax stratio lowstat    lprice
## 501 16800 0.224 5.85  6.03 2.50      6    39.1    19.2   14.33  9.729135
## 502 22400 0.063 5.73  6.59 2.48      1    27.3    21.0    9.67 10.016816
## 503 20600 0.045 5.73  6.12 2.29      1    27.3    21.0    9.08  9.933046
## 504 23899 0.061 5.73  6.98 2.17      1    27.3    21.0    5.64 10.081592
## 505 22000 0.110 5.73  6.79 2.39      1    27.3    21.0    6.48  9.998797
## 506 11900 0.047 5.73  6.03 2.51      1    27.3    21.0    7.88  9.384294
##         lnox lproptax
## 501 1.766442 5.968708
## 502 1.745715 5.609472
## 503 1.745715 5.609472
## 504 1.745715 5.609472
## 505 1.745715 5.609472
## 506 1.745715 5.609472

lm(price ~ crime + nox + rooms + dist + radial + stratio, data = hprice2)

## 
## Call:
## lm(formula = price ~ crime + nox + rooms + dist + radial + stratio, 
##     data = hprice2)
## 
## Coefficients:
## (Intercept)        crime          nox        rooms         dist       radial  
##     25177.7       -190.1      -3042.3       6597.6      -1052.6         93.9  
##     stratio  
##     -1270.7

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

Logaritmik fonksiyonel form

lm(formula = 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

6.2.1 Karasel modeller, Örnek 6.2

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

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)

## Zorunlu paket yükleniyor: carData

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