R Markdown
Dogrusal_reg_se <- function(X, Y)
sqrt(as.numeric(var(Y - X%*%solve(t(X) %*% X)%*%t(X) %*% Y))*solve(t(X) %*% X))
dr <- function(X, Y) {
A <- solve(t(X) %*% X)%*%t(X) %*% Y
B <- diag(sqrt(as.numeric(t(Y-as.numeric(t(Y)%*%rep(1, nrow(Y))/nrow(Y)))%*%(Y-as.numeric(t(Y)%*%rep(1, nrow(Y))/nrow(Y)))/(nrow(Y)-1))*solve(t(X) %*% X)))
result <- list(t(A), B)
names(result) <- c("katsayılar", "Standart Hatalar")
suppressMessages(return(result))
}
library(wooldridge) # data komutuyla fertil1 verisetini indirmek içim
library(rmarkdown) # page_table komutuyla veri setini rmarkdown üzerinde gösterebilmek için
data("fertil1")
paged_table(fertil1)
require(dplyr)
## Zorunlu paket yükleniyor: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
fertil1 %>%
group_by(year) %>%
summarise("ortalama çocuk sayısı (kids)" = mean(kids))
## # A tibble: 7 × 2
## year `ortalama çocuk sayısı (kids)`
## <int> <dbl>
## 1 72 3.03
## 2 74 3.21
## 3 76 2.80
## 4 78 2.80
## 5 80 2.82
## 6 82 2.40
## 7 84 2.24
require(dplyr)
fertil1 %>%
group_by(year) %>%
summarise(across(everything(), mean))
## # A tibble: 7 × 27
## year educ meduc feduc age kids black east northcen west farm othru…¹
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 72 12.2 8.33 8.90 44.9 3.03 0.0833 0.333 0.231 0.128 0.173 0.0833
## 2 74 12.3 8.94 9.29 44.1 3.21 0.0578 0.237 0.353 0.110 0.208 0.110
## 3 76 12.2 8.25 8.99 43.5 2.80 0.0461 0.263 0.316 0.0855 0.237 0.112
## 4 78 12.6 9.07 9.80 43.4 2.80 0.0490 0.273 0.329 0.105 0.203 0.112
## 5 80 12.9 9.40 9.95 43.7 2.82 0.0704 0.141 0.394 0.155 0.218 0.106
## 6 82 13.2 9.56 10.2 43.2 2.40 0.199 0.231 0.290 0.0806 0.167 0.118
## 7 84 13.3 10.2 10.7 41.8 2.24 0.0678 0.260 0.333 0.102 0.192 0.0734
## # … with 15 more variables: town <dbl>, smcity <dbl>, y74 <dbl>, y76 <dbl>,
## # y78 <dbl>, y80 <dbl>, y82 <dbl>, y84 <dbl>, agesq <dbl>, y74educ <dbl>,
## # y76educ <dbl>, y78educ <dbl>, y80educ <dbl>, y82educ <dbl>, y84educ <dbl>,
## # and abbreviated variable name ¹othrural
require(dplyr)
fertil1 %>%
group_by(year) %>%
summarise("ortalama çocuk sayısı (kids)" = mean(kids), "stadandart sapma çocuk sayısı (kids)" = sd(kids) )
## # A tibble: 7 × 3
## year `ortalama çocuk sayısı (kids)` `stadandart sapma çocuk sayısı (kids)`
## <int> <dbl> <dbl>
## 1 72 3.03 1.83
## 2 74 3.21 1.50
## 3 76 2.80 1.66
## 4 78 2.80 1.58
## 5 80 2.82 1.58
## 6 82 2.40 1.70
## 7 84 2.24 1.51
summary(lm(kids ~ educ + age + I(age^2) + black + east + northcen + west + farm + othrural + town + smcity + y74 + y76 + y78 + y80 + y82 + y84, data = fertil1))
##
## Call:
## lm(formula = kids ~ educ + age + I(age^2) + black + east + northcen +
## west + farm + othrural + town + smcity + y74 + y76 + y78 +
## y80 + y82 + y84, data = fertil1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9878 -1.0086 -0.0767 0.9331 4.6548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.742457 3.051767 -2.537 0.011315 *
## educ -0.128427 0.018349 -6.999 4.44e-12 ***
## age 0.532135 0.138386 3.845 0.000127 ***
## I(age^2) -0.005804 0.001564 -3.710 0.000217 ***
## black 1.075658 0.173536 6.198 8.02e-10 ***
## east 0.217324 0.132788 1.637 0.101992
## northcen 0.363114 0.120897 3.004 0.002729 **
## west 0.197603 0.166913 1.184 0.236719
## farm -0.052557 0.147190 -0.357 0.721105
## othrural -0.162854 0.175442 -0.928 0.353481
## town 0.084353 0.124531 0.677 0.498314
## smcity 0.211879 0.160296 1.322 0.186507
## y74 0.268183 0.172716 1.553 0.120771
## y76 -0.097379 0.179046 -0.544 0.586633
## y78 -0.068666 0.181684 -0.378 0.705544
## y80 -0.071305 0.182771 -0.390 0.696511
## y82 -0.522484 0.172436 -3.030 0.002502 **
## y84 -0.545166 0.174516 -3.124 0.001831 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.555 on 1111 degrees of freedom
## Multiple R-squared: 0.1295, Adjusted R-squared: 0.1162
## F-statistic: 9.723 on 17 and 1111 DF, p-value: < 2.2e-16
data("cps78_85")
paged_table(cps78_85)
cps78_85 %>%
group_by(year) %>%
summarise(n = n())
## # A tibble: 2 × 2
## year n
## <int> <int>
## 1 78 550
## 2 85 534
summary(lm(lwage ~ y85*(educ + female) + exper + I(exper^2) + union, data = cps78_85))
##
## Call:
## lm(formula = lwage ~ y85 * (educ + female) + exper + I(exper^2) +
## union, data = cps78_85)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.56098 -0.25828 0.00864 0.26571 2.11669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.589e-01 9.345e-02 4.911 1.05e-06 ***
## y85 1.178e-01 1.238e-01 0.952 0.3415
## educ 7.472e-02 6.676e-03 11.192 < 2e-16 ***
## female -3.167e-01 3.662e-02 -8.648 < 2e-16 ***
## exper 2.958e-02 3.567e-03 8.293 3.27e-16 ***
## I(exper^2) -3.994e-04 7.754e-05 -5.151 3.08e-07 ***
## union 2.021e-01 3.029e-02 6.672 4.03e-11 ***
## y85:educ 1.846e-02 9.354e-03 1.974 0.0487 *
## y85:female 8.505e-02 5.131e-02 1.658 0.0977 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4127 on 1075 degrees of freedom
## Multiple R-squared: 0.4262, Adjusted R-squared: 0.4219
## F-statistic: 99.8 on 8 and 1075 DF, p-value: < 2.2e-16
data("kielmc")
paged_table(kielmc)
kielmc81 <- kielmc %>% filter(year==1981)
model_81 <- lm(kielmc81$rprice ~ kielmc81$nearinc)
summary(model_81)
##
## Call:
## lm(formula = kielmc81$rprice ~ kielmc81$nearinc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60678 -19832 -2997 21139 136754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101308 3093 32.754 < 2e-16 ***
## kielmc81$nearinc -30688 5828 -5.266 5.14e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31240 on 140 degrees of freedom
## Multiple R-squared: 0.1653, Adjusted R-squared: 0.1594
## F-statistic: 27.73 on 1 and 140 DF, p-value: 5.139e-07
kielmc78 <- kielmc %>% filter(year==1978)
model_78 <- lm(kielmc78$rprice ~ kielmc78$nearinc)
summary(model_78)
##
## Call:
## lm(formula = kielmc78$rprice ~ kielmc78$nearinc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56517 -16605 -3193 8683 236307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82517 2654 31.094 < 2e-16 ***
## kielmc78$nearinc -18824 4745 -3.968 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29430 on 177 degrees of freedom
## Multiple R-squared: 0.08167, Adjusted R-squared: 0.07648
## F-statistic: 15.74 on 1 and 177 DF, p-value: 0.0001054
model_78_81 <- lm(kielmc$rprice ~ kielmc$nearinc*kielmc$y81)
summary(model_78_81)
##
## Call:
## lm(formula = kielmc$rprice ~ kielmc$nearinc * kielmc$y81)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60678 -17693 -3031 12483 236307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82517 2727 30.260 < 2e-16 ***
## kielmc$nearinc -18824 4875 -3.861 0.000137 ***
## kielmc$y81 18790 4050 4.640 5.12e-06 ***
## kielmc$nearinc:kielmc$y81 -11864 7457 -1.591 0.112595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30240 on 317 degrees of freedom
## Multiple R-squared: 0.1739, Adjusted R-squared: 0.1661
## F-statistic: 22.25 on 3 and 317 DF, p-value: 4.224e-13
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
stargazer(list(model_78,model_81,model_78_81), type = "text")
##
## ===========================================================================================
## Dependent variable:
## -----------------------------------------------------------------------
## rprice rprice rprice
## (1) (2) (3)
## -------------------------------------------------------------------------------------------
## nearinc -18,824.370***
## (4,744.594)
##
## nearinc -30,688.270***
## (5,827.709)
##
## nearinc -18,824.370***
## (4,875.322)
##
## y81 18,790.290***
## (4,050.065)
##
## y81 -11,863.900
## (7,456.646)
##
## Constant 82,517.230*** 101,307.500*** 82,517.230***
## (2,653.790) (3,093.027) (2,726.910)
##
## -------------------------------------------------------------------------------------------
## Observations 179 142 321
## R2 0.082 0.165 0.174
## Adjusted R2 0.076 0.159 0.166
## Residual Std. Error 29,431.960 (df = 177) 31,238.040 (df = 140) 30,242.900 (df = 317)
## F Statistic 15.741*** (df = 1; 177) 27.730*** (df = 1; 140) 22.251*** (df = 3; 317)
## ===========================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
model_78_81_sutun_2 <- lm(rprice ~ nearinc*y81 + age + I(age^2), data = kielmc )
summary(model_78_81_sutun_2)
##
## Call:
## lm(formula = rprice ~ nearinc * y81 + age + I(age^2), data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79349 -14431 -1711 10069 201486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.912e+04 2.406e+03 37.039 < 2e-16 ***
## nearinc 9.398e+03 4.812e+03 1.953 0.051713 .
## y81 2.132e+04 3.444e+03 6.191 1.86e-09 ***
## age -1.494e+03 1.319e+02 -11.333 < 2e-16 ***
## I(age^2) 8.691e+00 8.481e-01 10.248 < 2e-16 ***
## nearinc:y81 -2.192e+04 6.360e+03 -3.447 0.000644 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25540 on 315 degrees of freedom
## Multiple R-squared: 0.4144, Adjusted R-squared: 0.4052
## F-statistic: 44.59 on 5 and 315 DF, p-value: < 2.2e-16