odev1

library(wooldridge)

 data("hprice2")

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

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

predict(ornek6_5, tahmin_verileri, interval = "confidence" )

##        fit      lwr      upr
## 1 2.700075 2.661104 2.739047