\[ log(price) = \beta_0 + \beta_1log(dist) + u\] ## cevap :
library(wooldridge)
data("kıelmc")help(kıelmc)## starting httpd help server ... donehead(kıelmc)## year age agesq nbh cbd intst lintst price rooms area land baths dist
## 1 1978 48 2304 4 3000 1000 6.9078 60000 7 1660 4578 1 10700
## 2 1978 83 6889 4 4000 1000 6.9078 40000 6 2612 8370 2 11000
## 3 1978 58 3364 4 4000 1000 6.9078 34000 6 1144 5000 1 11500
## 4 1978 11 121 4 4000 1000 6.9078 63900 5 1136 10000 1 11900
## 5 1978 48 2304 4 4000 2000 7.6009 44000 5 1868 10000 1 12100
## 6 1978 78 6084 4 3000 2000 7.6009 46000 6 1780 9500 3 10000
## ldist wind lprice y81 larea lland y81ldist lintstsq nearinc
## 1 9.277999 3 11.00210 0 7.414573 8.429017 0 47.71770 1
## 2 9.305651 3 10.59663 0 7.867871 9.032409 0 47.71770 1
## 3 9.350102 3 10.43412 0 7.042286 8.517193 0 47.71770 1
## 4 9.384294 3 11.06507 0 7.035269 9.210340 0 47.71770 1
## 5 9.400961 3 10.69195 0 7.532624 9.210340 0 57.77368 1
## 6 9.210340 3 10.73640 0 7.484369 9.159047 0 57.77368 1
## y81nrinc rprice lrprice
## 1 0 60000 11.00210
## 2 0 40000 10.59663
## 3 0 34000 10.43412
## 4 0 63900 11.06507
## 5 0 44000 10.69195
## 6 0 46000 10.73640kielmc Açıklama Wooldridge Kaynak: K.A. Kiel ve K.T. McClain (1995), “Yer Belirleme Karar Aşamalarında Ev Fiyatları: Operasyon Yoluyla Söylentilerden Bir Yakma Fırını Örneği,” Çevre Ekonomisi ve Yönetimi Dergisi 28, 241-255. Profesör McClain, yalnızca bir alt kümesini kullandığım verileri nazikçe sağladı. Veriler geç yükleniyor.
kullanım veri(‘kielmc’) Biçim 25 değişken üzerinde 321 gözlem içeren bir data.frame:
yıl: 1978 veya 1981
yaş: evin yaşı
yaşq: yaş^2
nbh: mahalle, 1-6
cbd: dist. sente. otobüs. bölge, ft.
ara: uzak. eyaletler arası, ft.
tiftik: günlük(intst)
fiyat: satış fiyatı
odalar: evde # oda
alan: evin kare görüntüleri
arazi: metrekare arsa
banyolar: # banyolar
dist. evden incin., ft.
ldist: günlük(dist)
rüzgar: prc. zaman rüzgarı acıtır. eve
lfiyat: günlük(fiyat)
y81: =1 ise yıl == 1981
larea: günlük(alan)
arazi: günlük (arazi)
y81ldist: y81*ldist
lintstsq: tiftik^2
yakın yakın: =1 eğer mesafe <= 15840
y81nrinc: y81*yakın
fiyat: fiyat, 1978 dolar
fiyat: log(fiyat)
ilkregresyon <- lm(kıelmc$lprice ~ kıelmc$ldist)summary(ilkregresyon)##
## Call:
## lm(formula = kielmc$lprice ~ kielmc$ldist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.22356 -0.28076 -0.05527 0.27992 1.29332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.25750 0.47383 17.427 < 2e-16 ***
## kielmc$ldist 0.31722 0.04811 6.594 1.78e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4117 on 319 degrees of freedom
## Multiple R-squared: 0.1199, Adjusted R-squared: 0.1172
## F-statistic: 43.48 on 1 and 319 DF, p-value: 1.779e-10\[ \beta_1 = 0.31722 \]
\[ log(price) = 8.25750 + 0.31722log(dist) \] ## soru(ii) ## (i) deki denklemeye log(inst) , (area) , (land) , (rooms) , (baths) , (age) , ekleyelim , şimdi çöp yakma fırının etkileri hakkında nasıl bir sonuca ulaşırsınız ? ## (i)ve(ii) neden çekişli sonuçlar verir ?
$$ log(price) = _0+_1log(dist)+ _2(area)+_3(land)+_4(rooms)+_5(baths)+_6(age) + u
$$ ## cevap (ii) :
reg2<-lm (log(price)~log(dist)+log(intst)+log(area)+log(land)+rooms+baths+age ,data = kielmc)summary(reg2)##
## Call:
## lm(formula = log(price) ~ log(dist) + log(intst) + log(area) +
## log(land) + rooms + baths + age, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.35838 -0.18220 0.00115 0.20532 0.82180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2996586 0.5960546 10.569 < 2e-16 ***
## log(dist) 0.0281887 0.0532130 0.530 0.59667
## log(intst) -0.0437804 0.0424359 -1.032 0.30302
## log(area) 0.5124071 0.0698229 7.339 1.87e-12 ***
## log(land) 0.0782098 0.0337206 2.319 0.02102 *
## rooms 0.0503129 0.0235113 2.140 0.03313 *
## baths 0.1070528 0.0352304 3.039 0.00258 **
## age -0.0035630 0.0005774 -6.171 2.10e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2828 on 313 degrees of freedom
## Multiple R-squared: 0.5925, Adjusted R-squared: 0.5834
## F-statistic: 65.02 on 7 and 313 DF, p-value: < 2.2e-16\[ \beta_1 = 0,0281887\]
\[ \beta_1 = -0,0437804 \] \[\beta_6= -0,0035630 \] ## soru (iii) ## (ii) şıktaki modele [log(intst)]^2 ekleyelim
\[log(price) = \beta_0+\beta_1log(dist)+ \beta_2(area)+\beta_3(land)+\beta_4(rooms)+\beta_5(baths)+\beta_6(age)+[log(intst)]^2+ u \]
reg3<-lm(log(price)~log(dist)+log(intst)+log(area)+log(land)+rooms+baths+age+(log(intst))^2,data = kielmc)
summary(reg3)##
## Call:
## lm(formula = log(price) ~ log(dist) + log(intst) + log(area) +
## log(land) + rooms + baths + age + (log(intst))^2, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.35838 -0.18220 0.00115 0.20532 0.82180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2996586 0.5960546 10.569 < 2e-16 ***
## log(dist) 0.0281887 0.0532130 0.530 0.59667
## log(intst) -0.0437804 0.0424359 -1.032 0.30302
## log(area) 0.5124071 0.0698229 7.339 1.87e-12 ***
## log(land) 0.0782098 0.0337206 2.319 0.02102 *
## rooms 0.0503129 0.0235113 2.140 0.03313 *
## baths 0.1070528 0.0352304 3.039 0.00258 **
## age -0.0035630 0.0005774 -6.171 2.10e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2828 on 313 degrees of freedom
## Multiple R-squared: 0.5925, Adjusted R-squared: 0.5834
## F-statistic: 65.02 on 7 and 313 DF, p-value: < 2.2e-16