library(wooldridge)## Warning: package 'wooldridge' was built under R version 4.1.3library(rmarkdown)
data(savings)## Warning in data(savings): data set 'savings' not foundpaged_table(saving)summary(saving)## sav inc size educ
## Min. :-5577.0 Min. : 750 Min. : 2.00 Min. : 2.00
## 1st Qu.: 194.5 1st Qu.: 6510 1st Qu.: 3.00 1st Qu.: 9.00
## Median : 982.0 Median : 8776 Median : 4.00 Median :12.00
## Mean : 1582.5 Mean : 9941 Mean : 4.35 Mean :11.58
## 3rd Qu.: 1834.8 3rd Qu.:11903 3rd Qu.: 5.00 3rd Qu.:13.00
## Max. :25405.0 Max. :32080 Max. :10.00 Max. :20.00
## age black cons
## Min. :26.00 Min. :0.00 Min. :-13055
## 1st Qu.:33.00 1st Qu.:0.00 1st Qu.: 5732
## Median :38.50 Median :0.00 Median : 7562
## Mean :38.77 Mean :0.07 Mean : 8359
## 3rd Qu.:44.00 3rd Qu.:0.00 3rd Qu.: 9864
## Max. :54.00 Max. :1.00 Max. : 30280lm(formula = sav~inc+size+educ+age+black+cons, data = saving)##
## Call:
## lm(formula = sav ~ inc + size + educ + age + black + cons, data = saving)
##
## Coefficients:
## (Intercept) inc size educ age black
## 7.276e-12 1.000e+00 -2.328e-14 -1.750e-13 6.048e-14 4.514e-12
## cons
## -1.000e+00lm(scale(sav)~0+scale(inc)+scale(size)+scale(educ)+scale(age)+scale(cons),data = saving)##
## Call:
## lm(formula = scale(sav) ~ 0 + scale(inc) + scale(size) + scale(educ) +
## scale(age) + scale(cons), data = saving)
##
## Coefficients:
## scale(inc) scale(size) scale(educ) scale(age) scale(cons)
## 1.700e+00 4.020e-17 1.297e-16 9.930e-18 -1.744e+00bu eşitlik bizi şöyle gösteriyor yıllık gelirinde bir artiş meydana geldiğinde tasarrufta 1.7 artişi olacak bunanla birlikte aile boyutunda bir artiş tasarrufa 4.02 artırır eğitim ise 1.29 artişa sepeb olur ancak yıllık tüketim harcamalarinda bir artiş tasarrufta 1.744 bir azalişa neden olur.
##log
x2<- lm((sav)~log(inc)+size+age+log(educ)+(cons)+black, data = saving)
summary(x2)##
## Call:
## lm(formula = (sav) ~ log(inc) + size + age + log(educ) + (cons) +
## black, data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3274.6 -1071.7 -452.4 169.0 8513.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.204e+04 5.153e+03 -10.099 < 2e-16 ***
## log(inc) 6.416e+03 6.703e+02 9.572 1.65e-15 ***
## size -1.167e+02 1.450e+02 -0.805 0.423
## age 4.383e+01 3.185e+01 1.376 0.172
## log(educ) -1.972e+02 7.866e+02 -0.251 0.803
## cons -6.395e-01 5.518e-02 -11.590 < 2e-16 ***
## black 8.758e+02 8.636e+02 1.014 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2096 on 93 degrees of freedom
## Multiple R-squared: 0.6177, Adjusted R-squared: 0.593
## F-statistic: 25.04 on 6 and 93 DF, p-value: < 2.2e-16anova(x2)## Analysis of Variance Table
##
## Response: (sav)
## Df Sum Sq Mean Sq F value Pr(>F)
## log(inc) 1 61651293 61651293 14.0376 0.0003107 ***
## size 1 372158 372158 0.0847 0.7716243
## age 1 796910 796910 0.1815 0.6711129
## log(educ) 1 4748654 4748654 1.0812 0.3011161
## cons 1 587738578 587738578 133.8246 < 2.2e-16 ***
## black 1 4517257 4517257 1.0286 0.3131301
## Residuals 93 408442746 4391857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##stargazer
model1<- lm(formula =sav~inc+age+educ, data = saving)
summary(model1)##
## Call:
## lm(formula = sav ~ inc + age + educ, data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7340 -1153 -498 451 22634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -995.12489 2390.34748 -0.416 0.678
## inc 0.10794 0.07071 1.527 0.130
## age -3.58236 48.59161 -0.074 0.941
## educ 141.92262 113.84312 1.247 0.216
##
## Residual standard error: 3199 on 96 degrees of freedom
## Multiple R-squared: 0.08041, Adjusted R-squared: 0.05167
## F-statistic: 2.798 on 3 and 96 DF, p-value: 0.04421##yorum bu durumda bakıldığında gelirdeki bir artiş tasarrufa 0.107 artiş neden olur,eğitim ise 141.92 bir artişi olacak, ancak yaştaki bir artişi tasarrufa 3.58 bir azalişa neden olur.
model2<- lm(formula =sav~inc+age+cons, data = saving)
summary(model2)##
## Call:
## lm(formula = sav ~ inc + age + cons, data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.93e-11 -5.92e-13 -2.05e-13 3.19e-13 3.55e-11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.821e-12 2.291e-12 2.540e+00 0.0127 *
## inc 1.000e+00 1.384e-16 7.227e+15 <2e-16 ***
## age 5.426e-14 5.976e-14 9.080e-01 0.3661
## cons -1.000e+00 1.345e-16 -7.436e+15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.249e-12 on 96 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.972e+31 on 3 and 96 DF, p-value: < 2.2e-16##yorum burda baktiğimizda gelirde bir artiş tasarrufa 1.00 bi artişi olacak, bununla birlikte yaş artiğinda tasarrufta 5.42 bir artişi olur, şayet tüketim harcamalarda bir artiş tasarrufa 1.00 bir azaliş gösterir.
model3<- lm(formula =sav~inc+size+cons, data = saving)
summary(model3)##
## Call:
## lm(formula = sav ~ inc + size + cons, data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.047e-11 -5.160e-13 -2.010e-13 7.400e-14 3.568e-11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.731e-12 1.619e-12 5.392e+00 5e-07 ***
## inc 1.000e+00 1.404e-16 7.123e+15 <2e-16 ***
## size -1.231e-13 2.932e-13 -4.200e-01 0.676
## cons -1.000e+00 1.369e-16 -7.305e+15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.33e-12 on 96 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.899e+31 on 3 and 96 DF, p-value: < 2.2e-16##yorum bu durumda da gelirde bir artiş olduğunda tasarrufta da 1.00 artiş olur, aile boyutunda bir artiş meydana gelirse tasarrufta 1.23 bir azaliş olacak, bununla birlikte tüketim harcamalarda bir artişi tasarrufa 1.00 bir azalişa sepeb olur.
stargazer::stargazer(model1,model2,model3, type = "text")##
## =============================================================================================================================================
## Dependent variable:
## ---------------------------------------------------------------------------------------------------------------
## sav
## (1) (2) (3)
## ---------------------------------------------------------------------------------------------------------------------------------------------
## inc 0.108 1.000*** 1.000***
## (0.071) (0.000) (0.000)
##
## age -3.582 0.000
## (48.592) (0.000)
##
## educ 141.923
## (113.843)
##
## size -0.000
## (0.000)
##
## cons -1.000*** -1.000***
## (0.000) (0.000)
##
## Constant -995.125 0.000** 0.000***
## (2,390.347) (0.000) (0.000)
##
## ---------------------------------------------------------------------------------------------------------------------------------------------
## Observations 100 100 100
## R2 0.080 1.000 1.000
## Adjusted R2 0.052 1.000 1.000
## Residual Std. Error (df = 96) 3,198.910 0.000 0.000
## F Statistic (df = 3; 96) 2.798** 19,721,230,461,319,009,982,424,668,602,660.000*** 18,994,891,856,087,826,118,828,200,666,486.000***
## =============================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##interceptli fonksiyonu
summary(lm(formula = sav~inc+size+educ+age+black+cons, data = saving))##
## Call:
## lm(formula = sav ~ inc + size + educ + age + black + cons, data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.980e-11 -5.037e-13 2.110e-14 6.543e-13 2.992e-11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.276e-12 3.556e-12 2.046e+00 0.04359 *
## inc 1.000e+00 1.459e-16 6.852e+15 < 2e-16 ***
## size -2.328e-14 2.798e-13 -8.300e-02 0.93386
## educ -1.750e-13 1.484e-13 -1.179e+00 0.24130
## age 6.048e-14 6.275e-14 9.640e-01 0.33764
## black 4.514e-12 1.642e-12 2.749e+00 0.00718 **
## cons -1.000e+00 1.294e-16 -7.730e+15 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.049e-12 on 93 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.086e+31 on 6 and 93 DF, p-value: < 2.2e-16##interceptsiz fonksiyonu
summary(lm(formula = sav~inc+size+educ+age+black+cons-1, data = saving))## Warning in summary.lm(lm(formula = sav ~ inc + size + educ + age + black + :
## essentially perfect fit: summary may be unreliable##
## Call:
## lm(formula = sav ~ inc + size + educ + age + black + cons - 1,
## data = saving)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.080e-12 -4.819e-13 -7.020e-14 4.106e-13 8.331e-12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## inc 1.000e+00 4.322e-17 2.314e+16 < 2e-16 ***
## size 8.796e-14 7.344e-14 1.198e+00 0.234026
## educ -3.398e-14 3.495e-14 -9.720e-01 0.333509
## age -7.193e-15 1.037e-14 -6.940e-01 0.489582
## black 1.857e-12 4.879e-13 3.806e+00 0.000251 ***
## cons -1.000e+00 3.904e-17 -2.562e+16 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.224e-12 on 94 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.467e+32 on 6 and 94 DF, p-value: < 2.2e-16