rmarkdown tablo olusturmak icin kullaniyoruz datamiz beauty
library(wooldridge)
library(rmarkdown)
data("beauty")
paged_table(beauty)
head ilk 6 verimizi gosteriyor
head(beauty)
## wage lwage belavg abvavg exper looks union goodhlth black female married
## 1 5.73 1.745715 0 1 30 4 0 1 0 1 1
## 2 4.28 1.453953 0 0 28 3 0 1 0 1 1
## 3 7.96 2.074429 0 1 35 4 0 1 0 1 0
## 4 11.57 2.448416 0 0 38 3 0 1 0 0 1
## 5 11.42 2.435366 0 0 27 3 0 1 0 0 1
## 6 3.91 1.363537 0 0 20 3 0 0 0 1 1
## south bigcity smllcity service expersq educ
## 1 0 0 1 1 900 14
## 2 1 0 1 0 784 12
## 3 0 0 1 0 1225 10
## 4 0 1 0 1 1444 16
## 5 0 0 1 0 729 16
## 6 0 1 0 0 400 12
tail son 6 verimizi gosteriyor
tail(beauty)
## wage lwage belavg abvavg exper looks union goodhlth black female
## 1255 1.79 0.5822156 0 0 20 3 0 1 0 1
## 1256 1.61 0.4762342 0 0 25 3 0 1 1 1
## 1257 1.68 0.5187938 1 0 4 2 0 1 0 1
## 1258 3.29 1.1908876 0 0 35 3 0 1 1 1
## 1259 2.31 0.8372475 0 0 15 3 0 1 1 1
## 1260 1.92 0.6523252 0 0 24 3 0 0 0 1
## married south bigcity smllcity service expersq educ
## 1255 1 0 0 0 1 400 8
## 1256 0 0 0 0 1 625 12
## 1257 1 0 0 1 1 16 12
## 1258 0 0 0 1 1 1225 12
## 1259 1 0 1 0 1 225 10
## 1260 0 1 0 1 1 576 16
max komutu en yuksek degeri gosteriyor
max(beauty$wage)
## [1] 77.72
min komutu en kucuk degeri gosteriyor
min(beauty$looks)
## [1] 1
summary dogrusal regresyonun ozetini gosteriyor
summary(lm(wage ~ exper + looks + union + goodhlth + black + female + married + south, data = beauty ))
##
## Call:
## lm(formula = wage ~ exper + looks + union + goodhlth + black +
## female + married + south, data = beauty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.793 -2.255 -0.713 1.045 72.937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.96934 0.84818 3.501 0.00048 ***
## exper 0.06604 0.01098 6.015 2.35e-09 ***
## looks 0.62336 0.18071 3.449 0.00058 ***
## union 0.56387 0.27686 2.037 0.04189 *
## goodhlth 0.28987 0.49519 0.585 0.55840
## black -0.42852 0.47400 -0.904 0.36615
## female -2.36950 0.27788 -8.527 < 2e-16 ***
## married 0.66968 0.28640 2.338 0.01953 *
## south 0.65126 0.32266 2.018 0.04376 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.329 on 1251 degrees of freedom
## Multiple R-squared: 0.1426, Adjusted R-squared: 0.1372
## F-statistic: 26.02 on 8 and 1251 DF, p-value: < 2.2e-16
summary(lm(wage ~ exper + looks + union + goodhlth + black + female + married + south - 1, data = beauty ))
##
## Call:
## lm(formula = wage ~ exper + looks + union + goodhlth + black +
## female + married + south - 1, data = beauty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.255 -2.151 -0.603 1.130 72.314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## exper 0.08039 0.01023 7.859 8.32e-15 ***
## looks 1.06896 0.12885 8.296 2.75e-16 ***
## union 0.66546 0.27657 2.406 0.01627 *
## goodhlth 1.26410 0.41144 3.072 0.00217 **
## black -0.34774 0.47557 -0.731 0.46478
## female -2.09384 0.26768 -7.822 1.10e-14 ***
## married 0.91835 0.27870 3.295 0.00101 **
## south 0.74798 0.32292 2.316 0.02070 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.349 on 1252 degrees of freedom
## Multiple R-squared: 0.6944, Adjusted R-squared: 0.6924
## F-statistic: 355.5 on 8 and 1252 DF, p-value: < 2.2e-16
library(car)
## Zorunlu paket yükleniyor: carData
## Warning: package 'carData' was built under R version 4.2.2
model1 <- lm(wage ~ exper + looks + union + goodhlth + black + female + married + south, data = beauty )
anova dogrusal regresyonun icerisinde hangi degiskenin aciklayici
oldugunu gosteriyor
anova(model1)
## Analysis of Variance Table
##
## Response: wage
## Df Sum Sq Mean Sq F value Pr(>F)
## exper 1 1505.5 1505.54 80.3280 < 2.2e-16 ***
## looks 1 234.1 234.06 12.4884 0.0004244 ***
## union 1 160.4 160.36 8.5557 0.0035064 **
## goodhlth 1 33.7 33.73 1.7998 0.1799837
## black 1 109.9 109.93 5.8651 0.0155858 *
## female 1 1688.4 1688.44 90.0867 < 2.2e-16 ***
## married 1 92.3 92.29 4.9240 0.0266655 *
## south 1 76.4 76.36 4.0740 0.0437613 *
## Residuals 1251 23446.7 18.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tidyverse dogrusal regresyonu grafik icerisinde gostermek icin
kullaniyoruz
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.2.2
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## Warning: package 'forcats' was built under R version 4.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::recode() masks car::recode()
## ✖ purrr::some() masks car::some()
qplot(beauty$wage,beauty$exper) + geom_smooth(method = "lm", se = F )
## `geom_smooth()` using formula 'y ~ x'
summary(lm(scale(wage) ~ scale(exper) + scale(looks) + union + goodhlth +black +female + married + south, data = beauty ))
##
## Call:
## lm(formula = scale(wage) ~ scale(exper) + scale(looks) + union +
## goodhlth + black + female + married + south, data = beauty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4574 -0.4838 -0.1529 0.2242 15.6497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03200 0.11912 -0.269 0.78825
## scale(exper) 0.16952 0.02818 6.015 2.35e-09 ***
## scale(looks) 0.09160 0.02656 3.449 0.00058 ***
## union 0.12099 0.05940 2.037 0.04189 *
## goodhlth 0.06220 0.10625 0.585 0.55840
## black -0.09194 0.10170 -0.904 0.36615
## female -0.50841 0.05962 -8.527 < 2e-16 ***
## married 0.14369 0.06145 2.338 0.01953 *
## south 0.13974 0.06923 2.018 0.04376 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9289 on 1251 degrees of freedom
## Multiple R-squared: 0.1426, Adjusted R-squared: 0.1372
## F-statistic: 26.02 on 8 and 1251 DF, p-value: < 2.2e-16
dplyr table içerdinde ki select mutate sutun ve satır oluşturmak ve
data içersinde ki değişkenler seçmek içn kullanyr
library(dplyr)
beauty %>%
select(wage,exper, union, goodhlth, black, belavg ) %>%
filter(goodhlth == 1 )
## wage exper union goodhlth black belavg
## 1 5.73 30 0 1 0 0
## 2 4.28 28 0 1 0 0
## 3 7.96 35 0 1 0 0
## 4 11.57 38 0 1 0 0
## 5 11.42 27 0 1 0 0
## 6 8.76 12 0 1 0 0
## 7 7.69 5 1 1 0 0
## 8 5.00 5 0 1 0 0
## 9 3.89 12 0 1 0 0
## 10 3.45 3 0 1 0 0
## 11 4.03 6 0 1 0 0
## 12 5.14 19 0 1 0 1
## 13 3.00 8 0 1 0 0
## 14 7.99 12 0 1 0 0
## 15 6.01 17 0 1 0 0
## 16 5.16 7 0 1 0 0
## 17 11.54 12 0 1 0 0
## 18 10.44 10 0 1 0 0
## 19 7.69 7 0 1 0 0
## 20 7.69 7 1 1 0 0
## 21 6.79 19 0 1 0 0
## 22 6.87 33 0 1 0 0
## 23 17.03 32 0 1 0 0
## 24 10.05 12 0 1 0 0
## 25 15.81 24 0 1 0 0
## 26 14.84 29 0 1 0 0
## 27 19.08 17 0 1 0 0
## 28 5.96 10 0 1 0 0
## 29 6.73 41 0 1 0 0
## 30 8.17 18 0 1 0 1
## 31 12.39 28 0 1 0 0
## 32 9.62 37 0 1 0 1
## 33 4.83 20 0 1 1 0
## 34 7.69 10 1 1 0 1
## 35 12.50 31 0 1 0 0
## 36 2.82 18 0 1 0 0
## 37 12.31 9 1 1 0 0
## 38 13.22 42 0 1 0 0
## 39 16.83 14 0 1 0 0
## 40 11.54 27 0 1 0 0
## 41 4.95 20 0 1 1 0
## 42 7.21 14 0 1 0 0
## 43 11.58 4 0 1 0 0
## 44 15.38 13 0 1 0 0
## 45 6.10 5 0 1 0 0
## 46 7.93 32 0 1 0 0
## 47 9.16 19 1 1 0 0
## 48 6.91 14 0 1 0 0
## 49 20.18 28 0 1 0 0
## 50 9.93 31 1 1 0 0
## 51 7.79 18 0 1 0 0
## 52 5.13 23 0 1 0 0
## 53 10.99 14 0 1 0 0
## 54 6.56 17 0 1 0 1
## 55 10.10 16 0 1 0 0
## 56 10.74 19 0 1 0 0
## 57 16.35 26 0 1 0 0
## 58 4.62 20 0 1 0 0
## 59 7.49 18 0 1 0 0
## 60 19.23 35 0 1 0 0
## 61 4.77 9 0 1 0 0
## 62 7.14 20 0 1 0 1
## 63 14.42 7 0 1 0 0
## 64 9.28 40 0 1 0 0
## 65 29.98 12 0 1 0 0
## 66 32.79 33 0 1 0 0
## 67 9.62 16 0 1 0 0
## 68 8.36 15 0 1 0 0
## 69 21.63 11 0 1 0 0
## 70 15.90 24 0 1 0 0
## 71 5.77 6 0 1 0 1
## 72 20.99 40 0 1 0 1
## 73 23.32 15 0 1 0 0
## 74 4.97 9 0 1 0 0
## 75 6.89 16 0 1 0 1
## 76 5.73 12 0 1 0 1
## 77 5.19 8 0 1 0 0
## 78 7.79 25 1 1 0 0
## 79 8.79 24 1 1 0 0
## 80 9.32 25 1 1 0 0
## 81 8.33 7 0 1 0 0
## 82 10.12 40 0 1 1 0
## 83 8.24 13 0 1 0 0
## 84 5.05 5 0 1 0 0
## 85 6.55 10 0 1 0 0
## 86 4.47 25 1 1 0 0
## 87 7.96 10 0 1 0 0
## 88 6.25 20 0 1 0 1
## 89 4.13 7 0 1 0 0
## 90 6.73 25 0 1 0 1
## 91 7.69 8 0 1 0 0
## 92 4.35 8 0 1 0 1
## 93 7.69 28 0 1 0 0
## 94 4.81 37 0 1 0 1
## 95 5.24 31 0 1 0 0
## 96 2.14 24 0 1 0 0
## 97 4.49 33 0 1 0 0
## 98 3.61 8 0 1 0 0
## 99 5.45 33 0 1 0 0
## 100 5.00 30 0 1 0 0
## 101 6.73 23 0 1 0 0
## 102 10.05 24 0 1 0 0
## 103 6.54 4 0 1 0 0
## 104 9.20 40 0 1 0 0
## 105 4.62 1 0 1 0 0
## 106 3.37 36 0 1 1 0
## 107 5.54 7 0 1 0 0
## 108 6.11 7 0 1 0 0
## 109 4.99 14 0 1 0 0
## 110 5.88 7 0 1 0 0
## 111 5.66 11 1 1 0 0
## 112 4.00 6 0 1 0 0
## 113 6.87 23 0 1 0 0
## 114 4.37 19 0 1 0 1
## 115 5.77 30 0 1 0 0
## 116 5.77 21 0 1 0 0
## 117 12.31 9 0 1 0 0
## 118 19.08 29 1 1 0 0
## 119 7.55 9 0 1 0 0
## 120 5.90 18 0 1 0 0
## 121 6.37 26 0 1 0 0
## 122 6.15 21 1 1 0 0
## 123 1.92 12 1 1 0 1
## 124 3.27 11 0 1 0 0
## 125 2.18 18 0 1 0 0
## 126 5.62 22 0 1 0 0
## 127 9.29 38 0 1 0 0
## 128 6.41 10 1 1 0 0
## 129 7.46 22 1 1 0 0
## 130 6.59 16 1 1 0 1
## 131 6.73 20 1 1 0 1
## 132 5.61 14 1 1 0 0
## 133 3.65 14 0 1 0 0
## 134 13.01 15 1 1 0 0
## 135 6.97 18 1 1 0 0
## 136 7.69 21 1 1 0 0
## 137 6.73 15 0 1 0 0
## 138 5.13 10 1 1 0 0
## 139 2.14 6 0 1 0 0
## 140 4.53 8 1 1 0 1
## 141 9.10 18 0 1 0 1
## 142 9.78 15 1 1 0 0
## 143 3.63 11 0 1 0 0
## 144 10.62 18 1 1 0 0
## 145 4.62 10 1 1 0 0
## 146 6.54 26 1 1 0 0
## 147 7.21 30 1 1 0 0
## 148 3.85 13 1 1 0 0
## 149 2.01 16 0 1 0 0
## 150 3.90 5 1 1 0 0
## 151 6.35 11 1 1 0 0
## 152 2.00 6 0 1 0 0
## 153 7.69 22 1 1 0 0
## 154 2.63 4 0 1 0 0
## 155 5.00 15 1 1 0 0
## 156 6.54 7 0 1 0 0
## 157 5.19 10 0 1 0 0
## 158 9.75 8 1 1 0 0
## 159 2.81 14 0 1 1 0
## 160 4.62 11 1 1 0 0
## 161 7.33 34 1 1 0 0
## 162 7.69 12 1 1 0 0
## 163 3.26 10 1 1 0 0
## 164 5.77 11 0 1 0 1
## 165 5.59 8 1 1 0 0
## 166 5.13 28 0 1 1 0
## 167 14.74 24 1 1 0 1
## 168 3.67 8 1 1 0 0
## 169 9.73 13 0 1 0 0
## 170 3.46 4 1 1 0 0
## 171 6.37 43 1 1 0 0
## 172 6.58 30 0 1 0 0
## 173 10.15 16 1 1 1 0
## 174 9.23 19 1 1 0 0
## 175 10.22 26 0 1 0 0
## 176 4.76 8 1 1 0 0
## 177 1.76 24 0 1 0 0
## 178 12.73 15 1 1 0 0
## 179 5.85 26 1 1 0 0
## 180 5.99 19 0 1 0 1
## 181 4.62 4 0 1 0 0
## 182 1.80 23 0 1 0 0
## 183 5.95 25 0 1 0 0
## 184 7.46 18 0 1 0 0
## 185 8.70 27 0 1 0 1
## 186 3.75 16 0 1 0 0
## 187 7.69 20 0 1 0 0
## 188 10.10 35 0 1 0 0
## 189 3.75 36 0 1 0 1
## 190 5.79 35 0 1 0 0
## 191 5.06 5 0 1 0 0
## 192 7.96 15 1 1 0 0
## 193 12.82 44 1 1 0 0
## 194 8.46 28 0 1 0 0
## 195 3.76 2 1 1 0 0
## 196 2.86 43 0 1 0 0
## 197 11.81 30 1 1 0 0
## 198 7.69 26 0 1 0 0
## 199 2.88 7 0 1 1 0
## 200 5.73 10 1 1 0 0
## 201 4.49 37 0 1 0 1
## 202 9.57 27 1 1 0 0
## 203 3.61 5 0 1 0 0
## 204 7.77 14 0 1 0 1
## 205 9.23 16 0 1 0 1
## 206 5.58 4 0 1 0 0
## 207 5.34 39 0 1 0 0
## 208 5.17 32 1 1 0 0
## 209 6.41 16 0 1 0 0
## 210 7.40 10 0 1 0 0
## 211 6.75 15 0 1 0 0
## 212 8.46 10 0 1 0 0
## 213 4.61 10 0 1 0 0
## 214 5.24 4 0 1 0 0
## 215 15.38 12 0 1 0 0
## 216 2.37 15 0 1 0 0
## 217 6.72 37 0 1 0 0
## 218 9.40 23 0 1 0 0
## 219 7.84 15 0 1 0 0
## 220 15.00 10 0 1 0 0
## 221 4.70 14 0 1 0 0
## 222 11.28 17 0 1 0 0
## 223 11.97 24 0 1 0 0
## 224 13.99 13 0 1 0 0
## 225 3.00 5 0 1 0 0
## 226 3.41 20 0 1 0 0
## 227 9.62 29 0 1 0 0
## 228 6.73 10 0 1 0 0
## 229 9.62 36 0 1 0 0
## 230 5.19 15 0 1 0 0
## 231 4.62 26 0 1 0 0
## 232 5.03 13 0 1 0 0
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## 234 5.29 9 1 1 0 0
## 235 10.99 40 0 1 0 1
## 236 8.17 12 0 1 0 1
## 237 5.88 25 0 1 0 0
## 238 7.92 18 0 1 0 0
## 239 4.44 9 0 1 0 0
## 240 6.64 9 1 1 0 0
## 241 9.23 40 0 1 0 0
## 242 12.82 36 0 1 0 0
## 243 14.18 30 0 1 0 0
## 244 11.54 27 1 1 0 0
## 245 9.40 22 1 1 0 0
## 246 10.39 40 1 1 0 0
## 247 5.54 40 0 1 1 0
## 248 7.86 21 0 1 0 0
## 249 4.05 16 0 1 0 0
## 250 2.56 18 0 1 0 0
## 251 12.98 11 0 1 0 0
## 252 4.81 12 0 1 0 0
## 253 1.32 2 1 1 0 0
## 254 3.71 4 0 1 0 0
## 255 4.81 21 1 1 0 0
## 256 4.68 4 0 1 0 0
## 257 8.21 32 1 1 0 1
## 258 4.03 7 0 1 0 0
## 259 15.63 23 0 1 0 0
## 260 7.69 23 1 1 0 0
## 261 7.17 14 0 1 0 0
## 262 8.08 13 0 1 0 0
## 263 9.03 28 0 1 0 0
## 264 6.75 35 0 1 0 0
## 265 8.44 16 0 1 0 0
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## 303 11.40 28 0 1 0 0
## 304 2.20 18 0 1 0 0
## 305 1.65 24 0 1 0 1
## 306 6.59 27 0 1 0 0
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## 1089 5.31 15 0 1 0 0
## 1090 2.63 19 0 1 0 0
## 1091 2.25 12 0 1 1 1
## 1092 2.75 11 0 1 0 0
## 1093 3.29 6 1 1 0 0
## 1094 5.77 13 0 1 0 0
## 1095 4.74 8 0 1 0 0
## 1096 4.12 7 0 1 0 0
## 1097 2.89 12 0 1 1 0
## 1098 6.96 11 1 1 0 0
## 1099 6.41 41 0 1 0 1
## 1100 4.28 7 1 1 0 0
## 1101 1.28 9 0 1 0 0
## 1102 2.56 40 1 1 0 0
## 1103 4.62 7 0 1 1 0
## 1104 1.98 9 0 1 0 0
## 1105 1.92 13 0 1 0 0
## 1106 1.98 17 0 1 0 0
## 1107 2.00 3 0 1 1 1
## 1108 2.11 8 0 1 0 0
## 1109 2.11 3 0 1 0 0
## 1110 2.77 9 0 1 0 0
## 1111 3.46 30 0 1 0 0
## 1112 6.00 5 0 1 0 0
## 1113 4.81 13 0 1 1 0
## 1114 2.33 5 0 1 0 1
## 1115 3.57 9 0 1 0 0
## 1116 1.98 8 0 1 0 0
## 1117 5.13 20 0 1 1 1
## 1118 4.47 3 0 1 0 0
## 1119 2.07 5 1 1 0 0
## 1120 6.97 20 1 1 0 0
## 1121 1.98 2 0 1 0 0
## 1122 4.62 7 0 1 0 0
## 1123 3.21 5 0 1 0 0
## 1124 1.80 46 0 1 0 0
## 1125 4.62 10 1 1 0 0
## 1126 6.11 27 0 1 0 0
## 1127 5.73 39 0 1 1 0
## 1128 3.57 13 0 1 0 0
## 1129 1.58 27 0 1 0 0
## 1130 4.21 7 0 1 0 0
## 1131 1.75 18 0 1 0 0
## 1132 2.15 6 0 1 0 1
## 1133 1.02 11 0 1 0 0
## 1134 2.40 10 0 1 1 1
## 1135 2.83 28 0 1 1 0
## 1136 3.29 2 0 1 0 0
## 1137 1.98 45 0 1 0 0
## 1138 23.16 9 0 1 0 0
## 1139 5.49 13 0 1 0 0
## 1140 3.00 40 0 1 0 0
## 1141 2.88 15 0 1 0 0
## 1142 2.63 12 0 1 0 0
## 1143 5.82 20 0 1 0 1
## 1144 1.09 8 0 1 0 1
## 1145 1.28 13 0 1 0 0
## 1146 6.25 3 0 1 0 0
## 1147 2.88 7 0 1 0 0
## 1148 7.69 18 1 1 0 0
## 1149 7.14 13 0 1 0 0
## 1150 6.93 36 1 1 0 0
## 1151 1.56 4 0 1 0 0
## 1152 1.98 8 0 1 0 0
## 1153 8.75 24 1 1 0 0
## 1154 4.81 31 0 1 0 1
## 1155 4.71 16 0 1 0 0
## 1156 4.87 10 0 1 0 0
## 1157 4.62 10 0 1 0 0
## 1158 2.40 12 0 1 0 0
## 1159 5.34 20 0 1 0 1
## 1160 4.21 13 1 1 0 0
## 1161 4.72 9 1 1 0 0
## 1162 12.82 15 1 1 0 0
## 1163 6.73 34 0 1 0 0
## 1164 5.39 8 1 1 0 0
## 1165 5.31 3 0 1 0 0
## 1166 10.58 19 0 1 0 0
## 1167 8.65 10 1 1 0 0
## 1168 4.17 13 0 1 0 0
## 1169 6.81 7 1 1 0 0
## 1170 9.62 21 1 1 0 0
## 1171 1.22 10 0 1 0 0
## 1172 1.79 20 0 1 0 0
## 1173 1.61 25 0 1 1 0
## 1174 1.68 4 0 1 0 1
## 1175 3.29 35 0 1 1 0
## 1176 2.31 15 0 1 1 0
beauty %>%
select(wage,exper, union, goodhlth, black, belavg ) %>%
group_by(goodhlth) %>%
summarise(mean(exper))
## # A tibble: 2 × 2
## goodhlth `mean(exper)`
## <int> <dbl>
## 1 0 24.1
## 2 1 17.8