library(tidyverse)
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## ✔ dplyr 1.1.2 ✔ readr 2.1.5
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## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
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## ✔ purrr 1.0.2
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library(ltm)
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## Loading required package: MASS
##
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##
## Loading required package: msm
## Warning: package 'msm' was built under R version 4.3.3
## Loading required package: polycor
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library(dplyr)
library(stats)
library(fastDummies)
## Warning: package 'fastDummies' was built under R version 4.3.3
## Thank you for using fastDummies!
## To acknowledge our work, please cite the package:
## Kaplan, J. & Schlegel, B. (2023). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. Version 1.7.1. URL: https://github.com/jacobkap/fastDummies, https://jacobkap.github.io/fastDummies/.
library(knitr)
## Warning: package 'knitr' was built under R version 4.3.1
library(data.table)
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##
## Attaching package: 'data.table'
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library(formattable)
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## Attaching package: 'formattable'
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library(DT)
## Warning: package 'DT' was built under R version 4.3.3
df <- read.csv2("basak_sayisal_veriler_c.csv")
df <- df[-c(43),]
#glimpse(df)
extract_factors <- function (df,what,howmany,reduce=0,rotat="promax"){
cat("________________ START --> ", what, "_____________________")
cat("\n")
center <- function(x) { return (x - mean(x))}
df_sub <- df %>% dplyr::select(starts_with(what)) %>% mutate(across(everything(), center))
CA <- round(cronbach.alpha(df_sub) $ alpha,2)
cat("\n")
cat("cronbach_alpa =", CA)
cat("\n")
if (reduce != 0) df_sub=df_sub[,-reduce]
FA<- df_sub%>%factanal(.,howmany, scores ="regression",rotation=rotat)
print(FA $ loadings)
explained <- 1-FA $ uniquenesses
barplot(explained,cex.names=0.7, col=1:length(explained),
main="faktor analizining acikladigi oranlar", cex.main=0.8)
cat("\n")
cat("faktor analizining acikladigi oranlar:");cat("\n")
explained_props <- as.data.frame(1-FA $ uniquenesses)
colnames(explained_props) ="explained_variances"
print(explained_props);cat("\n")
cat("likelihood ratio test | p-value:", FA $ PVAL); cat("\n")
if(FA $ PVAL<0.05) print("factors are not sufficient")
else cat("\n", "factors are sufficient")
cat("\n")
cat("________________ END _____________________")
outcome <-list(FA,df_sub)
return(outcome)
}
dummy_func <- function (df,this_X,this_key) {
dummy <- list()
for (a in 1:length(this_X)) {
dummy[[a]] <- df %>%
dplyr::select(starts_with(this_key)) %>%
dplyr::select(c(this_X[a])) %>%
dummy_cols %>%
dplyr::select(where(is.numeric)) %>%
dplyr::select(c(-1)) }
dummies_as_df <- do.call(data.frame,dummy)
return(dummies_as_df)
}
show_model_details <- function(model_now){
cat("\n")
model_now %>%
cooks.distance %>%
plot(.,type="h",col="black",
main=paste(model_now $call[2],"cooks distances (verilerin modele etkileri)"),
cex.main = 0.6)
abline(h=1,lty=2,col="red")
text(1:dim(model.matrix(model_now))[1],cooks.distance(model_now),
rownames(model.matrix(model_now)),cex=0.7)
cat(rep("##",3),sep="")
paste("Y =", model_now $call[2]) %>% print
cat(rep("##",3),sep="")
cat("\n")
model_now %>% summary %>% print
}
make_model <- function(df,Y_df,dummies,this_key){
df_pilot <- cbind(Y_df,dummies)
model_sosyal_cevresel_boyut <- lm(df_pilot $sosyal_cevresel_donusum~.,data = df_pilot[,-c(2)])
model_sosyal_cevresel_boyut%>%show_model_details
model_verimlilik_boyutu <- lm(df_pilot $ verimlilik_boyutu~.,data = df_pilot [,-c(1)])
model_verimlilik_boyutu%>%show_model_details}
df_or <- df
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>% names
## [1] "isletme_kisi_egitim" "isletme_sektor" "isletme_kume_1"
## [4] "isletme_kume_2" "isletme_isim" "isletme_yabanciOrtak"
## [7] "isletme_yas" "isletme_olcek" "isletme_cal_say"
## [10] "kisi_cinsiyet" "kisi_bolum_poz" "kisi_kac_yildir"
make_table <- function(df,D){
cat(rep("\n",3))
T <- df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(all_of(D)) %>% table
print(T)
}
olcutler <- list(1,2,6,7,9)
for (a in 1:length(olcutler)) {make_table(df,olcutler[[a]])}
##
##
##
## isletme_kisi_egitim
## Doktora Lisans Lisansustu Lise Yuksekokul
## 2 78 37 1 3
##
##
##
## isletme_sektor
## Cevre-Geri Donusum Gıda Kamu Tuzel Makine-Metal
## 8 10 1 41
## Mobilya Plastik-Kimya Tekstil-Ambalaj Ulasim-Lojistik
## 3 15 3 23
## uretim Xx Yapi Malzemeleri
## 1 1 15
##
##
##
## isletme_yabanciOrtak
## Evet Hayir
## 51 70
##
##
##
## isletme_yas
## 1 - 10 yil 11 - 30 yil 30 yildan fazla
## 15 51 55
##
##
##
## isletme_cal_say
## 10 - 49 kisi 250 kisi ve uzeri 50 - 249 kisi
## 19 45 57
df_sektor <-df %>% dplyr::filter(isletme_sektor != "Xx" &
isletme_sektor != "Mobilya" &
isletme_sektor != "Kamu Tuzel" &
isletme_sektor != "Tekstil-Ambalaj" &
isletme_sektor != "uretim")
df_sektor_egitim <- df_sektor %>% dplyr::filter(isletme_kisi_egitim != "Doktora" &
isletme_kisi_egitim != "Yuksekokul" &
isletme_kisi_egitim != "Lise")
df_all <- df
df <- df_sektor_egitim
see_sur <- extract_factors(df,"far_sur",2)
## ________________ START --> far_sur _____________________
##
## cronbach_alpa = 0.93
##
## Loadings:
## Factor1 Factor2
## far_sur_kaynak 0.959
## far_sur_gelecek 0.455 0.455
## far_sur_adil_is 0.660 0.234
## far_sur_toplum 0.945 -0.125
## far_sur_cevre_koruma 0.727 0.191
## far_sur_paydas 0.356 0.488
## far_sur_eko_performans 0.675
## far_sur_calisan_hak 0.554 0.351
## far_sur_tarim 0.672
##
## Factor1 Factor2
## SS loadings 2.949 2.057
## Proportion Var 0.328 0.229
## Cumulative Var 0.328 0.556
##
## faktor analizining acikladigi oranlar:
## explained_variances
## far_sur_kaynak 0.9000740
## far_sur_gelecek 0.7414797
## far_sur_adil_is 0.7345475
## far_sur_toplum 0.7218938
## far_sur_cevre_koruma 0.7842138
## far_sur_paydas 0.6402567
## far_sur_eko_performans 0.4641977
## far_sur_calisan_hak 0.7372742
## far_sur_tarim 0.3838830
##
## likelihood ratio test | p-value: 0.1328375
##
## factors are sufficient
## ________________ END _____________________
see_sur_scores <- see_sur[[1]] $ scores
cat("\n")
see_sur_scores %>% dim %>% print
## [1] 107 2
colnames(see_sur_scores) <- c("sosyal_cevresel_donusum","verimlilik_boyutu")
see_sur_scores %>% boxplot(.,horizontal=TRUE,cex.axis=0.7,
col=1:dim(see_sur_scores)[2],
main = "Faktor analizinin turettigi bagimli degiskenler", cex.main=0.7)
du_df <- dummy_func(df,c(1),"isletme")
cat("\n")
df_sektor %>% dplyr:: select(isletme_sektor) %>% table %>% print
## isletme_sektor
## Cevre-Geri Donusum Gıda Makine-Metal Plastik-Kimya
## 8 10 41 15
## Ulasim-Lojistik Yapi Malzemeleri
## 23 15
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3953 -0.7103 0.4911 0.9228 2.5819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07841 0.15593 -0.503 0.616
## isletme_kisi_egitim_Lisansustu 0.25425 0.28077 0.906 0.367
##
## Residual standard error: 1.341 on 105 degrees of freedom
## Multiple R-squared: 0.007749, Adjusted R-squared: -0.001701
## F-statistic: 0.82 on 1 and 105 DF, p-value: 0.3673
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5170 -0.3140 -0.0551 0.7896 3.2659
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.007079 0.157756 0.045 0.964
## isletme_kisi_egitim_Lisansustu -0.022952 0.284067 -0.081 0.936
##
## Residual standard error: 1.357 on 105 degrees of freedom
## Multiple R-squared: 6.217e-05, Adjusted R-squared: -0.009461
## F-statistic: 0.006528 on 1 and 105 DF, p-value: 0.9358
du_df <- dummy_func(df,c(2),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1846 -0.7949 0.4506 0.9138 2.7925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24519 0.47588 0.515 0.608
## isletme_sektor_Gıda... -0.06818 0.67300 -0.101 0.920
## isletme_sektor_Makine.Metal -0.53429 0.52242 -1.023 0.309
## isletme_sektor_Plastik.Kimya -0.41752 0.58927 -0.709 0.480
## isletme_sektor_Ulasim.Lojistik 0.03522 0.55571 0.063 0.950
## isletme_sektor_Yapi.Malzemeleri 0.04236 0.58927 0.072 0.943
##
## Residual standard error: 1.346 on 101 degrees of freedom
## Multiple R-squared: 0.0389, Adjusted R-squared: -0.008676
## F-statistic: 0.8177 on 5 and 101 DF, p-value: 0.5399
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4951 -0.4760 0.0104 0.6643 2.8679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.033726 0.484390 0.070 0.945
## isletme_sektor_Gıda... -0.007752 0.685031 -0.011 0.991
## isletme_sektor_Makine.Metal -0.048467 0.531755 -0.091 0.928
## isletme_sektor_Plastik.Kimya 0.371334 0.599810 0.619 0.537
## isletme_sektor_Ulasim.Lojistik -0.115081 0.565646 -0.203 0.839
## isletme_sektor_Yapi.Malzemeleri -0.312980 0.599810 -0.522 0.603
##
## Residual standard error: 1.37 on 101 degrees of freedom
## Multiple R-squared: 0.01965, Adjusted R-squared: -0.02889
## F-statistic: 0.4048 on 5 and 101 DF, p-value: 0.8445
du_df <- dummy_func(df,c(6),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6835 -0.7003 0.5213 0.7989 2.2936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2782 0.1953 -1.425 0.1571
## isletme_yabanciOrtak_Hayir 0.4881 0.2586 1.887 0.0619 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.324 on 105 degrees of freedom
## Multiple R-squared: 0.03281, Adjusted R-squared: 0.0236
## F-statistic: 3.562 on 1 and 105 DF, p-value: 0.06188
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6901 -0.3508 0.0649 0.7310 3.4089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1802 0.1987 0.907 0.367
## isletme_yabanciOrtak_Hayir -0.3161 0.2632 -1.201 0.232
##
## Residual standard error: 1.348 on 105 degrees of freedom
## Multiple R-squared: 0.01355, Adjusted R-squared: 0.004155
## F-statistic: 1.442 on 1 and 105 DF, p-value: 0.2325
du_df <- dummy_func(df,c(7),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1695 -0.8802 0.4165 0.7963 2.8077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5081 0.3412 1.489 0.1394
## isletme_yas_11...30.yil -0.8124 0.3939 -2.062 0.0417 *
## isletme_yas_30.yildan.fazla -0.3790 0.3918 -0.967 0.3357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 104 degrees of freedom
## Multiple R-squared: 0.04634, Adjusted R-squared: 0.028
## F-statistic: 2.527 on 2 and 104 DF, p-value: 0.08481
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6155 -0.4027 -0.1403 0.8234 3.0485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8944 0.3386 -2.641 0.00954 **
## isletme_yas_11...30.yil 1.1189 0.3910 2.861 0.00510 **
## isletme_yas_30.yildan.fazla 0.9648 0.3889 2.481 0.01472 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.312 on 104 degrees of freedom
## Multiple R-squared: 0.07498, Adjusted R-squared: 0.05719
## F-statistic: 4.215 on 2 and 104 DF, p-value: 0.01737
du_df <- dummy_func(df,c(6,7),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3897 -0.8348 0.4283 0.9371 2.5874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2564 0.3639 0.705 0.4826
## isletme_yabanciOrtak_Hayir 0.4720 0.2560 1.844 0.0681 .
## isletme_yas_11...30.yil -0.8124 0.3895 -2.086 0.0395 *
## isletme_yas_30.yildan.fazla -0.4185 0.3880 -1.079 0.2833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 103 degrees of freedom
## Multiple R-squared: 0.07681, Adjusted R-squared: 0.04992
## F-statistic: 2.857 on 3 and 103 DF, p-value: 0.04072
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7910 -0.4710 -0.0155 0.7801 3.2020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7189 0.3642 -1.974 0.05106 .
## isletme_yabanciOrtak_Hayir -0.3290 0.2562 -1.284 0.20202
## isletme_yas_11...30.yil 1.1189 0.3898 2.870 0.00498 **
## isletme_yas_30.yildan.fazla 0.9923 0.3883 2.556 0.01206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.307 on 103 degrees of freedom
## Multiple R-squared: 0.08955, Adjusted R-squared: 0.06304
## F-statistic: 3.377 on 3 and 103 DF, p-value: 0.0212
du_df <- dummy_func(df,c(9),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4017 -0.8061 0.5114 0.9627 2.5755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3014 0.3265 0.923 0.358
## isletme_cal_say_50...249.kisi -0.3735 0.3780 -0.988 0.325
## isletme_cal_say_250.kisi.ve.uzeri -0.3395 0.3898 -0.871 0.386
##
## Residual standard error: 1.346 on 104 degrees of freedom
## Multiple R-squared: 0.009781, Adjusted R-squared: -0.009262
## F-statistic: 0.5136 on 2 and 104 DF, p-value: 0.5998
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4455 -0.2585 -0.0066 0.6896 3.1314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.14384 0.32956 -0.436 0.663
## isletme_cal_say_50...249.kisi 0.07945 0.38149 0.208 0.835
## isletme_cal_say_250.kisi.ve.uzeri 0.28547 0.39341 0.726 0.470
##
## Residual standard error: 1.359 on 104 degrees of freedom
## Multiple R-squared: 0.00704, Adjusted R-squared: -0.01206
## F-statistic: 0.3687 on 2 and 104 DF, p-value: 0.6926
du_df <- dummy_func(df,c(1,2,6,7),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0895 -0.7551 0.4423 0.8615 2.8876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35924 0.57052 0.630 0.5304
## isletme_kisi_egitim_Lisansustu 0.17192 0.29025 0.592 0.5550
## isletme_sektor_Gıda... 0.08849 0.70048 0.126 0.8997
## isletme_sektor_Makine.Metal -0.30713 0.53151 -0.578 0.5647
## isletme_sektor_Plastik.Kimya -0.25452 0.58938 -0.432 0.6668
## isletme_sektor_Ulasim.Lojistik 0.19072 0.56026 0.340 0.7343
## isletme_sektor_Yapi.Malzemeleri 0.19926 0.59633 0.334 0.7390
## isletme_yabanciOrtak_Hayir 0.40357 0.26459 1.525 0.1305
## isletme_yas_11...30.yil -0.83988 0.39979 -2.101 0.0383 *
## isletme_yas_30.yildan.fazla -0.49003 0.40748 -1.203 0.2321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.322 on 97 degrees of freedom
## Multiple R-squared: 0.1101, Adjusted R-squared: 0.02758
## F-statistic: 1.334 on 9 and 97 DF, p-value: 0.2296
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6973 -0.5862 -0.0201 0.8337 2.7980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.41961 0.57285 -0.732 0.46564
## isletme_kisi_egitim_Lisansustu -0.04769 0.29143 -0.164 0.87037
## isletme_sektor_Gıda... -0.35846 0.70335 -0.510 0.61146
## isletme_sektor_Makine.Metal -0.39300 0.53369 -0.736 0.46327
## isletme_sektor_Plastik.Kimya 0.05720 0.59179 0.097 0.92320
## isletme_sektor_Ulasim.Lojistik -0.46008 0.56255 -0.818 0.41545
## isletme_sektor_Yapi.Malzemeleri -0.67603 0.59876 -1.129 0.26166
## isletme_yabanciOrtak_Hayir -0.34153 0.26567 -1.286 0.20167
## isletme_yas_11...30.yil 1.17891 0.40142 2.937 0.00414 **
## isletme_yas_30.yildan.fazla 1.10325 0.40915 2.696 0.00826 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.327 on 97 degrees of freedom
## Multiple R-squared: 0.1167, Adjusted R-squared: 0.03479
## F-statistic: 1.424 on 9 and 97 DF, p-value: 0.1882
du_df <- dummy_func(df,c(1,2,6,7,9),"isletme")
make_model(df,Y_df=see_sur_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$sosyal_cevresel_donusum ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1405 -0.7985 0.4882 0.8288 2.8367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31249 0.58203 0.537 0.5926
## isletme_kisi_egitim_Lisansustu 0.19582 0.29630 0.661 0.5103
## isletme_sektor_Gıda... 0.04879 0.75903 0.064 0.9489
## isletme_sektor_Makine.Metal -0.33183 0.59326 -0.559 0.5773
## isletme_sektor_Plastik.Kimya -0.27640 0.61824 -0.447 0.6558
## isletme_sektor_Ulasim.Lojistik 0.19438 0.61257 0.317 0.7517
## isletme_sektor_Yapi.Malzemeleri 0.18143 0.65275 0.278 0.7817
## isletme_yabanciOrtak_Hayir 0.41840 0.27122 1.543 0.1262
## isletme_yas_11...30.yil -0.85382 0.45486 -1.877 0.0636 .
## isletme_yas_30.yildan.fazla -0.46399 0.47706 -0.973 0.3332
## isletme_cal_say_50...249.kisi 0.12152 0.47852 0.254 0.8001
## isletme_cal_say_250.kisi.ve.uzeri -0.03920 0.51748 -0.076 0.9398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.333 on 95 degrees of freedom
## Multiple R-squared: 0.1129, Adjusted R-squared: 0.01018
## F-statistic: 1.099 on 11 and 95 DF, p-value: 0.3706
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5088 -0.5522 -0.0496 0.8396 2.7316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33945 0.58166 -0.584 0.5609
## isletme_kisi_egitim_Lisansustu -0.09348 0.29611 -0.316 0.7529
## isletme_sektor_Gıda... -0.12470 0.75855 -0.164 0.8698
## isletme_sektor_Makine.Metal -0.19492 0.59289 -0.329 0.7431
## isletme_sektor_Plastik.Kimya 0.19684 0.61785 0.319 0.7507
## isletme_sektor_Ulasim.Lojistik -0.31728 0.61218 -0.518 0.6055
## isletme_sektor_Yapi.Malzemeleri -0.48836 0.65233 -0.749 0.4559
## isletme_yabanciOrtak_Hayir -0.39077 0.27105 -1.442 0.1527
## isletme_yas_11...30.yil 1.33346 0.45457 2.933 0.0042 **
## isletme_yas_30.yildan.fazla 1.21000 0.47676 2.538 0.0128 *
## isletme_cal_say_50...249.kisi -0.46668 0.47822 -0.976 0.3316
## isletme_cal_say_250.kisi.ve.uzeri -0.25866 0.51715 -0.500 0.6181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.333 on 95 degrees of freedom
## Multiple R-squared: 0.1277, Adjusted R-squared: 0.02673
## F-statistic: 1.265 on 11 and 95 DF, p-value: 0.2569