library(wooldridge)
library(rmarkdown)
library(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, unionlibrary() fonksiyonunda parantez içine yazmış olduğum paketin R’a yüklemiş olmamız lazım.
data("ezunem")data() komutunu kullanarak wooldridge paketinin içinde yer alan “ezunem” veri setine ulaşabiliriz.
head(ezunem)## year uclms ez d81 d82 d83 d84 d85 d86 d87 d88 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10
## 1 1980 166746 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 2 1981 83561 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 3 1982 158146 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 4 1983 83572 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 5 1984 45949 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 6 1985 48848 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0
## c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 c21 c22 luclms guclms cez city
## 1 0 0 0 0 0 0 0 0 0 0 0 0 12.02423 NA NA 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 11.33333 -0.69089508 0 1
## 3 0 0 0 0 0 0 0 0 0 0 0 0 11.97127 0.63794231 0 1
## 4 0 0 0 0 0 0 0 0 0 0 0 0 11.33346 -0.63781071 0 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0 10.73529 -0.59817600 1 1
## 6 0 0 0 0 0 0 0 0 0 0 0 0 10.79647 0.06118107 0 1head() komutuyla ezunem datasında yer alan ilk 6 gözleme ulaşabiliriz.
tail(ezunem)## year uclms ez d81 d82 d83 d84 d85 d86 d87 d88 c1 c2 c3 c4 c5 c6 c7 c8 c9
## 193 1983 139328 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 194 1984 80605 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 195 1985 82758 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 196 1986 67815 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 197 1987 67762 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 198 1988 56837 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## c10 c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 c21 c22 luclms guclms
## 193 0 0 0 0 0 0 0 0 0 0 0 0 1 11.84459 -0.0504941940
## 194 0 0 0 0 0 0 0 0 0 0 0 0 1 11.29732 -0.5472707748
## 195 0 0 0 0 0 0 0 0 0 0 0 0 1 11.32368 0.0263605118
## 196 0 0 0 0 0 0 0 0 0 0 0 0 1 11.12454 -0.1991376877
## 197 0 0 0 0 0 0 0 0 0 0 0 0 1 11.12376 -0.0007820129
## 198 0 0 0 0 0 0 0 0 0 0 0 0 1 10.94794 -0.1758136749
## cez city
## 193 0 22
## 194 0 22
## 195 0 22
## 196 0 22
## 197 0 22
## 198 0 22tail() komutuyla ezunem datasında yer alan son 6 gözleme ulaşabiliriz.
paged_table(ezunem)paged_table() komutuyla ezunem datasında yer alan tüm gözlemlere tablo yardımıyla ulaşabiliriz.
summary(ezunem)## year uclms ez d81
## Min. :1980 Min. : 12360 Min. :0.0000 Min. :0.0000
## 1st Qu.:1982 1st Qu.: 43922 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1984 Median : 69171 Median :0.0000 Median :0.0000
## Mean :1984 Mean : 95383 Mean :0.2323 Mean :0.1111
## 3rd Qu.:1986 3rd Qu.:114443 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1988 Max. :667208 Max. :1.0000 Max. :1.0000
##
## d82 d83 d84 d85
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1111 Mean :0.1111 Mean :0.1111 Mean :0.1111
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## d86 d87 d88 c1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.1111 Mean :0.1111 Mean :0.1111 Mean :0.04545
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
##
## c2 c3 c4 c5
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.04545 Mean :0.04545 Mean :0.04545 Mean :0.04545
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## c6 c7 c8 c9
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.04545 Mean :0.04545 Mean :0.04545 Mean :0.04545
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## c10 c11 c12 c13
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.04545 Mean :0.04545 Mean :0.04545 Mean :0.04545
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## c14 c15 c16 c17
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.04545 Mean :0.04545 Mean :0.04545 Mean :0.04545
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## c18 c19 c20 c21
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.04545 Mean :0.04545 Mean :0.04545 Mean :0.04545
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## c22 luclms guclms cez
## Min. :0.00000 Min. : 9.422 Min. :-0.84730 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:10.690 1st Qu.:-0.38671 1st Qu.:0.00000
## Median :0.00000 Median :11.144 Median :-0.21562 Median :0.00000
## Mean :0.04545 Mean :11.191 Mean :-0.15939 Mean :0.05682
## 3rd Qu.:0.00000 3rd Qu.:11.648 3rd Qu.: 0.02655 3rd Qu.:0.00000
## Max. :1.00000 Max. :13.411 Max. : 0.79429 Max. :1.00000
## NA's :22 NA's :22
## city
## Min. : 1.0
## 1st Qu.: 6.0
## Median :11.5
## Mean :11.5
## 3rd Qu.:17.0
## Max. :22.0
## summary() komutu ise bize ezunem datasının özetini vermektedir.
gruplama <- ezunem %>%
group_by(year) %>%
summarise("yaşlara göre ortalama işsizlik başvuruları(uclms)" = mean(uclms))
paged_table(gruplama)library ve dplyr paketinin bize sunmuş olduğu hayp(%>%) sayesinde kodumuzu rahatca yazabildik artı olarak beklenen diye isimlendirdiğimiz yeni bir data oluşturduk. Bu datanın içinde yaşlar ve yaşlara göre işsizlik başvuruları ortalamasına ulaşabilmekteyiz. bu ortalamayı ezunem verisinden mean() komutuyla elde edebiliriz.
yıl: 1980 - 1988
uclms: işsizlik başvuruları
ez: =1 eğer kurumsal bölge varsa
d81: =1 ise yıl == 1981
d82: =1 eğer yıl == 1982
d83: =1 eğer yıl == 1983
verilerinden yararlandım.
37 değişken üzerinde 198 gözlem içeren bir data.frame:
summary(lm(year ~ uclms + ez + d81 + d82 + d83 , data = ezunem ))##
## Call:
## lm(formula = year ~ uclms + ez + d81 + d82 + d83, data = ezunem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6228 -0.5311 -0.0837 1.2155 4.3756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.985e+03 2.566e-01 7735.396 < 2e-16 ***
## uclms -6.217e-06 1.658e-06 -3.750 0.000234 ***
## ez 1.617e+00 3.594e-01 4.499 1.18e-05 ***
## d81 -3.283e+00 4.662e-01 -7.040 3.32e-11 ***
## d82 -1.901e+00 4.823e-01 -3.942 0.000113 ***
## d83 -1.179e+00 4.685e-01 -2.517 0.012640 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 192 degrees of freedom
## Multiple R-squared: 0.4475, Adjusted R-squared: 0.4331
## F-statistic: 31.1 on 5 and 192 DF, p-value: < 2.2e-16lm fonksiyonunda yıl (year) değişkenini kullanmayı tercih ettim.
summary(lm(year ~ uclms + ez + d81 + d82 + d83-1 , data = ezunem ))##
## Call:
## lm(formula = year ~ uclms + ez + d81 + d82 + d83 - 1, data = ezunem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3694.8 55.4 379.7 1480.0 1897.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## uclms 7.351e-03 7.559e-04 9.725 < 2e-16 ***
## ez 1.552e+03 1.661e+02 9.341 < 2e-16 ***
## d81 1.222e+03 2.442e+02 5.006 1.25e-06 ***
## d82 7.722e+02 2.627e+02 2.940 0.00369 **
## d83 1.102e+03 2.485e+02 4.436 1.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1085 on 193 degrees of freedom
## Multiple R-squared: 0.7084, Adjusted R-squared: 0.7008
## F-statistic: 93.77 on 5 and 193 DF, p-value: < 2.2e-16fonksiyonumuzdan intercept’i çıkartmak için fonksiyona -1 ekledik ve fonsiyona başka değişkenlerde eklemiş olduk
##MUTATE FORMÜLÜ İLE KENDİ DATAMIZI OLUŞTURMA ##KUKLA DEĞİŞKEN OLUŞTURMA
revizedata <- ezunem %>%
mutate(esabit = 1) %>%
mutate(revize = ifelse(c3 == 1, 2, 3))revizdata ismini verdiğimiz bir data oluşturduk, bu datada ezunem datasındaki verilerin yanında esabit ismini verdiğimiz bütün gözlemleri 1 olan bir değişken ekledik. Hayp ın bize sağladığı kolaylığı kullanarak mutate formulune birde ifelse ekledik, ifelse bize inseason 1 olduğunda ona 2 değerini 2 olduğunda ise 3 değerini vermesini bekliyoruz.
##SELECT VE FİLTER KULLANMA
verimbenim <- ezunem %>% select(ez, year, c3)
verimbenim## ez year c3
## 1 0 1980 0
## 2 0 1981 0
## 3 0 1982 0
## 4 0 1983 0
## 5 1 1984 0
## 6 1 1985 0
## 7 1 1986 0
## 8 1 1987 0
## 9 1 1988 0
## 10 0 1980 0
## 11 0 1981 0
## 12 0 1982 0
## 13 0 1983 0
## 14 0 1984 0
## 15 0 1985 0
## 16 0 1986 0
## 17 0 1987 0
## 18 0 1988 0
## 19 0 1980 1
## 20 0 1981 1
## 21 0 1982 1
## 22 0 1983 1
## 23 0 1984 1
## 24 0 1985 1
## 25 0 1986 1
## 26 0 1987 1
## 27 0 1988 1
## 28 0 1980 0
## 29 0 1981 0
## 30 0 1982 0
## 31 0 1983 0
## 32 0 1984 0
## 33 0 1985 0
## 34 0 1986 0
## 35 0 1987 0
## 36 0 1988 0
## 37 0 1980 0
## 38 0 1981 0
## 39 0 1982 0
## 40 0 1983 0
## 41 0 1984 0
## 42 1 1985 0
## 43 1 1986 0
## 44 1 1987 0
## 45 1 1988 0
## 46 0 1980 0
## 47 0 1981 0
## 48 0 1982 0
## 49 0 1983 0
## 50 1 1984 0
## 51 1 1985 0
## 52 1 1986 0
## 53 1 1987 0
## 54 1 1988 0
## 55 0 1980 0
## 56 0 1981 0
## 57 0 1982 0
## 58 0 1983 0
## 59 1 1984 0
## 60 1 1985 0
## 61 1 1986 0
## 62 1 1987 0
## 63 1 1988 0
## 64 0 1980 0
## 65 0 1981 0
## 66 0 1982 0
## 67 0 1983 0
## 68 0 1984 0
## 69 1 1985 0
## 70 1 1986 0
## 71 1 1987 0
## 72 1 1988 0
## 73 0 1980 0
## 74 0 1981 0
## 75 0 1982 0
## 76 0 1983 0
## 77 0 1984 0
## 78 1 1985 0
## 79 1 1986 0
## 80 1 1987 0
## 81 1 1988 0
## 82 0 1980 0
## 83 0 1981 0
## 84 0 1982 0
## 85 0 1983 0
## 86 0 1984 0
## 87 0 1985 0
## 88 0 1986 0
## 89 0 1987 0
## 90 0 1988 0
## 91 0 1980 0
## 92 0 1981 0
## 93 0 1982 0
## 94 0 1983 0
## 95 0 1984 0
## 96 0 1985 0
## 97 0 1986 0
## 98 0 1987 0
## 99 0 1988 0
## 100 0 1980 0
## 101 0 1981 0
## 102 0 1982 0
## 103 0 1983 0
## 104 0 1984 0
## 105 0 1985 0
## 106 0 1986 0
## 107 0 1987 0
## 108 0 1988 0
## 109 0 1980 0
## 110 0 1981 0
## 111 0 1982 0
## 112 0 1983 0
## 113 0 1984 0
## 114 0 1985 0
## 115 0 1986 0
## 116 0 1987 0
## 117 0 1988 0
## 118 0 1980 0
## 119 0 1981 0
## 120 0 1982 0
## 121 0 1983 0
## 122 0 1984 0
## 123 1 1985 0
## 124 1 1986 0
## 125 1 1987 0
## 126 1 1988 0
## 127 0 1980 0
## 128 0 1981 0
## 129 0 1982 0
## 130 0 1983 0
## 131 0 1984 0
## 132 0 1985 0
## 133 0 1986 0
## 134 0 1987 0
## 135 0 1988 0
## 136 0 1980 0
## 137 0 1981 0
## 138 0 1982 0
## 139 0 1983 0
## 140 0 1984 0
## 141 0 1985 0
## 142 0 1986 0
## 143 0 1987 0
## 144 0 1988 0
## 145 0 1980 0
## 146 0 1981 0
## 147 0 1982 0
## 148 0 1983 0
## 149 1 1984 0
## 150 1 1985 0
## 151 1 1986 0
## 152 1 1987 0
## 153 1 1988 0
## 154 0 1980 0
## 155 0 1981 0
## 156 0 1982 0
## 157 0 1983 0
## 158 0 1984 0
## 159 0 1985 0
## 160 0 1986 0
## 161 0 1987 0
## 162 0 1988 0
## 163 0 1980 0
## 164 0 1981 0
## 165 0 1982 0
## 166 0 1983 0
## 167 0 1984 0
## 168 0 1985 0
## 169 0 1986 0
## 170 0 1987 0
## 171 0 1988 0
## 172 0 1980 0
## 173 0 1981 0
## 174 0 1982 0
## 175 0 1983 0
## 176 1 1984 0
## 177 1 1985 0
## 178 1 1986 0
## 179 1 1987 0
## 180 1 1988 0
## 181 0 1980 0
## 182 0 1981 0
## 183 0 1982 0
## 184 0 1983 0
## 185 1 1984 0
## 186 1 1985 0
## 187 1 1986 0
## 188 1 1987 0
## 189 1 1988 0
## 190 0 1980 0
## 191 0 1981 0
## 192 0 1982 0
## 193 0 1983 0
## 194 0 1984 0
## 195 0 1985 0
## 196 0 1986 0
## 197 0 1987 0
## 198 0 1988 0verimbenim ismini verdiğimiz ve dplyr paketinin içinde yer alan select komutuyla yani sadece sütunları filitreleye biliriz.
verimbenim1 <- ezunem %>% filter(c3== 1)
verimbenim1## year uclms ez d81 d82 d83 d84 d85 d86 d87 d88 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10
## 1 1980 61046 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 2 1981 60959 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 3 1982 90064 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 1983 61609 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 5 1984 41869 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 6 1985 49353 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0
## 7 1986 38632 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
## 8 1987 37350 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
## 9 1988 31182 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
## c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 c21 c22 luclms guclms cez
## 1 0 0 0 0 0 0 0 0 0 0 0 0 11.01938 NA NA
## 2 0 0 0 0 0 0 0 0 0 0 0 0 11.01796 -0.001426697 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 11.40828 0.390318871 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 11.02856 -0.379712105 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 10.64230 -0.386262894 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 10.80675 0.164453506 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 10.56184 -0.244917870 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 10.52809 -0.033747673 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 10.34760 -0.180492401 0
## city
## 1 3
## 2 3
## 3 3
## 4 3
## 5 3
## 6 3
## 7 3
## 8 3
## 9 3verimbenim1 ismli tablomuzda ise yine dplyr paketinden filter komutunu kullanarak sadece inseason’ un 1 olduğu satırları filitreleyebiliriz.
verimbenim2 veri sitenin basit regresyonunu yapmaya çalışalım.
mod2 <- lm(verimbenim1$year ~ verimbenim1$ez)
summary(mod2)##
## Call:
## lm(formula = verimbenim1$year ~ verimbenim1$ez)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4 -2 0 2 4
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1984.0000 0.9129 2173 <2e-16 ***
## verimbenim1$ez NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.739 on 8 degrees of freedomDenklem sonucu 1984.0000year + NAez kesim parametresi dir.
Aynı regresyonu year 0 a göre yapalım.
verimbenim2 <- ezunem %>% filter(c3 == 0)
verimbenim2## year uclms ez d81 d82 d83 d84 d85 d86 d87 d88 c1 c2 c3 c4 c5 c6 c7 c8 c9
## 1 1980 166746 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 2 1981 83561 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 3 1982 158146 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 4 1983 83572 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 5 1984 45949 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
## 6 1985 48848 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 7 1986 46570 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
## 8 1987 47205 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
## 9 1988 37953 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## 10 1980 115279 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 11 1981 78278 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 12 1982 126389 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 13 1983 79666 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 14 1984 41376 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0
## 15 1985 53905 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
## 16 1986 42037 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
## 17 1987 44151 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
## 18 1988 27088 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
## 19 1980 91152 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 20 1981 47337 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 21 1982 102228 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 22 1983 59709 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 23 1984 30062 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0
## 24 1985 41624 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0
## 25 1986 33688 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
## 26 1987 23357 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
## 27 1988 17190 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
## 28 1980 183095 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 29 1981 100892 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 30 1982 121593 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 31 1983 61597 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 32 1984 63997 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0
## 33 1985 82141 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0
## 34 1986 54092 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0
## 35 1987 37084 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
## 36 1988 28978 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 37 1980 117090 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 38 1981 131495 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 39 1982 190727 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 40 1983 172074 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 41 1984 113250 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
## 42 1985 112460 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0
## 43 1986 95065 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
## 44 1987 112507 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
## 45 1988 73704 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
## 46 1980 172782 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 47 1981 161154 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 48 1982 356613 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 49 1983 255142 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 50 1984 125848 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
## 51 1985 124783 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## 52 1986 106736 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
## 53 1987 95935 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
## 54 1988 72338 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
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## 169 0 0 0 0 0 0 0 0 0 0 1 0 0 10.680332 0.0307016373
## 170 0 0 0 0 0 0 0 0 0 0 1 0 0 10.276980 -0.4033517838
## 171 0 0 0 0 0 0 0 0 0 0 1 0 0 10.137176 -0.1398048401
## 172 0 0 0 0 0 0 0 0 0 0 0 1 0 12.264304 NA
## 173 0 0 0 0 0 0 0 0 0 0 0 1 0 11.850447 -0.4138574600
## 174 0 0 0 0 0 0 0 0 0 0 0 1 0 12.125107 0.2746601105
## 175 0 0 0 0 0 0 0 0 0 0 0 1 0 11.798127 -0.3269796371
## 176 0 0 0 0 0 0 0 0 0 0 0 1 0 11.442889 -0.3552379608
## 177 0 0 0 0 0 0 0 0 0 0 0 1 0 11.493263 0.0503740311
## 178 0 0 0 0 0 0 0 0 0 0 0 1 0 11.346918 -0.1463451385
## 179 0 0 0 0 0 0 0 0 0 0 0 1 0 11.061358 -0.2855596542
## 180 0 0 0 0 0 0 0 0 0 0 0 1 0 10.986817 -0.0745410919
## 181 0 0 0 0 0 0 0 0 0 0 0 0 1 11.629196 NA
## 182 0 0 0 0 0 0 0 0 0 0 0 0 1 11.552848 -0.0763483047
## 183 0 0 0 0 0 0 0 0 0 0 0 0 1 11.895081 0.3422327042
## 184 0 0 0 0 0 0 0 0 0 0 0 0 1 11.844586 -0.0504941940
## 185 0 0 0 0 0 0 0 0 0 0 0 0 1 11.297316 -0.5472707748
## 186 0 0 0 0 0 0 0 0 0 0 0 0 1 11.323676 0.0263605118
## 187 0 0 0 0 0 0 0 0 0 0 0 0 1 11.124538 -0.1991376877
## 188 0 0 0 0 0 0 0 0 0 0 0 0 1 11.123756 -0.0007820129
## 189 0 0 0 0 0 0 0 0 0 0 0 0 1 10.947943 -0.1758136749
## cez city
## 1 NA 1
## 2 0 1
## 3 0 1
## 4 0 1
## 5 1 1
## 6 0 1
## 7 0 1
## 8 0 1
## 9 0 1
## 10 NA 2
## 11 0 2
## 12 0 2
## 13 0 2
## 14 0 2
## 15 0 2
## 16 0 2
## 17 0 2
## 18 0 2
## 19 NA 4
## 20 0 4
## 21 0 4
## 22 0 4
## 23 0 4
## 24 0 4
## 25 0 4
## 26 0 4
## 27 0 4
## 28 NA 5
## 29 0 5
## 30 0 5
## 31 0 5
## 32 0 5
## 33 1 5
## 34 0 5
## 35 0 5
## 36 0 5
## 37 NA 6
## 38 0 6
## 39 0 6
## 40 0 6
## 41 1 6
## 42 0 6
## 43 0 6
## 44 0 6
## 45 0 6
## 46 NA 7
## 47 0 7
## 48 0 7
## 49 0 7
## 50 1 7
## 51 0 7
## 52 0 7
## 53 0 7
## 54 0 7
## 55 NA 8
## 56 0 8
## 57 0 8
## 58 0 8
## 59 0 8
## 60 1 8
## 61 0 8
## 62 0 8
## 63 0 8
## 64 NA 9
## 65 0 9
## 66 0 9
## 67 0 9
## 68 0 9
## 69 1 9
## 70 0 9
## 71 0 9
## 72 0 9
## 73 NA 10
## 74 0 10
## 75 0 10
## 76 0 10
## 77 0 10
## 78 0 10
## 79 0 10
## 80 0 10
## 81 0 10
## 82 NA 11
## 83 0 11
## 84 0 11
## 85 0 11
## 86 0 11
## 87 0 11
## 88 0 11
## 89 0 11
## 90 0 11
## 91 NA 12
## 92 0 12
## 93 0 12
## 94 0 12
## 95 0 12
## 96 0 12
## 97 0 12
## 98 0 12
## 99 0 12
## 100 NA 13
## 101 0 13
## 102 0 13
## 103 0 13
## 104 0 13
## 105 0 13
## 106 0 13
## 107 0 13
## 108 0 13
## 109 NA 14
## 110 0 14
## 111 0 14
## 112 0 14
## 113 0 14
## 114 1 14
## 115 0 14
## 116 0 14
## 117 0 14
## 118 NA 15
## 119 0 15
## 120 0 15
## 121 0 15
## 122 0 15
## 123 0 15
## 124 0 15
## 125 0 15
## 126 0 15
## 127 NA 16
## 128 0 16
## 129 0 16
## 130 0 16
## 131 0 16
## 132 0 16
## 133 0 16
## 134 0 16
## 135 0 16
## 136 NA 17
## 137 0 17
## 138 0 17
## 139 0 17
## 140 1 17
## 141 0 17
## 142 0 17
## 143 0 17
## 144 0 17
## 145 NA 18
## 146 0 18
## 147 0 18
## 148 0 18
## 149 0 18
## 150 0 18
## 151 0 18
## 152 0 18
## 153 0 18
## 154 NA 19
## 155 0 19
## 156 0 19
## 157 0 19
## 158 0 19
## 159 0 19
## 160 0 19
## 161 0 19
## 162 0 19
## 163 NA 20
## 164 0 20
## 165 0 20
## 166 0 20
## 167 1 20
## 168 0 20
## 169 0 20
## 170 0 20
## 171 0 20
## 172 NA 21
## 173 0 21
## 174 0 21
## 175 0 21
## 176 1 21
## 177 0 21
## 178 0 21
## 179 0 21
## 180 0 21
## 181 NA 22
## 182 0 22
## 183 0 22
## 184 0 22
## 185 0 22
## 186 0 22
## 187 0 22
## 188 0 22
## 189 0 22mod3 <- lm(verimbenim2$year ~ verimbenim2$ez)
summary(mod3)##
## Call:
## lm(formula = verimbenim2$year ~ verimbenim2$ez)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3007 -2.1739 -0.3007 1.6993 4.6993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1983.3007 0.1907 10399.027 < 2e-16 ***
## verimbenim2$ez 2.8732 0.3866 7.432 3.71e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.281 on 187 degrees of freedom
## Multiple R-squared: 0.228, Adjusted R-squared: 0.2239
## F-statistic: 55.24 on 1 and 187 DF, p-value: 3.711e-12Denklem sonucu 1983.3007year + 2.8732ez denklemi tek tabloda gösterelim.
##TABLO BİRLEŞİMİ
require(stargazer)## Zorunlu paket yükleniyor: stargazer##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazerrequire() komutu library() komutuyla benzerlik gösterir. ama bir başka görevi de vardır. library() komutu inidirilmiş paketleri kullanmak için yazılırken require() komutu indirilmemiş paketi önce indirir daha sonra ise library yapmaktadır.
iyi bir proje, öğrencilerin kendi eyaletlerindeki girişim, yetkilendirme veya rönesans bölgesi politikalarını analiz etmelerini sağlamaktır. Artık birçok eyalette bu tür programlar var. Şehir veya posta kodu düzeyinde bölge belirleme dönemlerini kapsayan birkaç yıllık panel veri güzel bir çalışma yapabilir.
stargazer( mod2, mod3, type = 'text')##
## ==========================================================
## Dependent variable:
## --------------------------------------
## year year
## (1) (2)
## ----------------------------------------------------------
## ez
##
##
## ez 2.873***
## (0.387)
##
## Constant 1,984.000*** 1,983.301***
## (0.913) (0.191)
##
## ----------------------------------------------------------
## Observations 9 189
## R2 0.000 0.228
## Adjusted R2 0.000 0.224
## Residual Std. Error 2.739 (df = 8) 2.281 (df = 187)
## F Statistic 55.238*** (df = 1; 187)
## ==========================================================
## Note: *p<0.1; **p<0.05; ***p<0.01