library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.3.2
## Warning: package 'ggplot2' was built under R version 4.3.1
## Warning: package 'tibble' was built under R version 4.3.1
## Warning: package 'tidyr' was built under R version 4.3.1
## Warning: package 'readr' was built under R version 4.3.2
## Warning: package 'purrr' was built under R version 4.3.1
## Warning: package 'dplyr' was built under R version 4.3.1
## Warning: package 'stringr' was built under R version 4.3.1
## Warning: package 'forcats' was built under R version 4.3.2
## Warning: package 'lubridate' was built under R version 4.3.2
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ltm)
## Warning: package 'ltm' was built under R version 4.3.3
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: msm
## Warning: package 'msm' was built under R version 4.3.3
## Loading required package: polycor
## Warning: package 'polycor' was built under R version 4.3.3
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)
## Warning: package 'data.table' was built under R version 4.3.1
##
## Attaching package: 'data.table'
##
## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year
##
## The following objects are masked from 'package:dplyr':
##
## between, first, last
##
## The following object is masked from 'package:purrr':
##
## transpose
library(formattable)
## Warning: package 'formattable' was built under R version 4.3.3
##
## Attaching package: 'formattable'
##
## The following object is masked from 'package:MASS':
##
## area
library(DT)
## Warning: package 'DT' was built under R version 4.3.3
df_or <- read.csv2("basak_sayisal_veriler.csv")
df <- df_or[-43,]
glimpse(df)
## Rows: 121
## Columns: 146
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, …
## $ KOD <int> 75, 31, 54, 3, 70, 104, 87, 7, 37, 82, 81, …
## $ zaman <chr> "2-16-2024 23:24:14", "2-5-2024 14:40", "2-…
## $ kisi_cinsiyet <chr> "Kadin", "Kadin", "Kadin", "Kadin", "Erkek"…
## $ kisi_egitim <chr> "Lisansustu", "Lisans", "Lisans", "Lisans",…
## $ kisi_bolum_poz <chr> "ust yonetim", "Kalite Kontrol-Planlama", "…
## $ kisi_kac_yildir <chr> "10 yildan fazla", "1 yildan 2", "10 yildan…
## $ isletme_sektor <chr> "uretim", "Talasli imalat", "Gida", "Makina…
## $ isletme_isim <chr> "-", "3 Eksen Makina", "Ajinomoto istanbul …
## $ isletme_yabanciOrtak <chr> "Hayir", "Hayir", "Evet", "Evet", "Hayir", …
## $ isletme_yas <chr> "30 yildan fazla", "11 - 30 yil", "30 yilda…
## $ isletme_olcek <chr> "Buyuk", "Orta", "Buyuk", "Orta", "Orta", "…
## $ isletme_cal_say <chr> "250 kisi ve uzeri", "50 - 249 kisi", "50 -…
## $ far_sur_kaynak <int> 5, 4, 5, 5, 4, 4, 4, 5, 4, 3, 5, 5, 5, 5, 3…
## $ far_sur_gelecek <int> 5, 2, 5, 4, 4, 3, 3, 4, 4, 4, 5, 5, 5, 5, 3…
## $ far_sur_adil_is <int> 5, 3, 5, 5, 5, 4, 5, 4, 5, 4, 5, 5, 5, 5, 5…
## $ far_sur_toplum <int> 4, 2, 5, 5, 4, 4, 5, 4, 5, 3, 5, 5, 4, 5, 4…
## $ far_sur_cevre_koruma <int> 4, 3, 5, 4, 4, 3, 5, 4, 5, 4, 5, 5, 5, 5, 5…
## $ far_sur_paydas <int> 4, 3, 5, 5, 5, 3, 4, 5, 4, 3, 5, 5, 5, 5, 4…
## $ far_sur_eko_performans <int> 4, 5, 5, 5, 4, 4, 5, 5, 4, 3, 5, 5, 5, 5, 4…
## $ far_sur_calisan_hak <int> 5, 2, 5, 4, 5, 4, 3, 5, 3, 2, 5, 5, 5, 5, 5…
## $ far_sur_tarim <int> 1, 1, 5, 3, 3, 1, 4, 3, 5, 4, 4, 5, 5, 3, 4…
## $ far_cev_karbon <int> 5, 1, 3, 3, 4, 3, 4, 5, 3, 4, 5, 3, 4, 4, 3…
## $ far_cev_atik <int> 5, 3, 5, 3, 5, 3, 5, 5, 3, 4, 5, 4, 3, 5, 3…
## $ far_cev_enerji <int> 4, 3, 5, 5, 4, 3, 5, 4, 4, 4, 5, 3, 3, 4, 4…
## $ far_cev_su <int> 3, 2, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 4, 3…
## $ far_cev_iklim <int> 4, 1, 4, 2, 3, 3, 3, 4, 2, 3, 5, 3, 4, 4, 3…
## $ far_sos_egitim <int> 3, 2, 5, 2, 4, 2, 5, 4, 4, 2, 4, 4, 4, 5, 3…
## $ far_sos_cinsiyet <int> 3, 3, 5, 2, 3, 2, 3, 5, 3, 2, 4, 4, 4, 4, 5…
## $ far_sos_is_sagligi <int> 5, 3, 5, 4, 4, 3, 5, 5, 4, 4, 4, 5, 5, 5, 5…
## $ far_sos_tedarikci <int> 3, 1, 5, 2, 4, 1, 5, 4, 4, 3, 3, 5, 5, 3, 3…
## $ far_sos_sorumluluk <int> 4, 1, 5, 2, 3, 2, 5, 4, 3, 3, 5, 5, 5, 4, 3…
## $ far_sos_calisan <int> 4, 1, 5, 4, 4, 3, 5, 4, 3, 2, 5, 5, 5, 5, 5…
## $ far_sos_musteri <int> 5, 5, 5, 4, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5, 5…
## $ far_sos_sosyal_hak <int> 3, 4, 5, 4, 4, 3, 3, 4, 4, 4, 3, 5, 4, 4, 5…
## $ far_sos_yetenek <int> 4, 4, 5, 4, 4, 4, 5, 4, 4, 2, 4, 5, 4, 4, 4…
## $ far_sos_istihdam <int> 4, 2, 5, 4, 5, 4, 5, 5, 5, 3, 5, 4, 4, 4, 3…
## $ far_sos_urun_guvenlik <int> 3, 2, 5, 4, 5, 5, 5, 5, 4, 3, 5, 5, 5, 4, 4…
## $ far_sos_inovasyon <int> 2, 2, 5, 4, 4, 5, 5, 5, 4, 3, 5, 5, 5, 3, 3…
## $ far_sos_motivasyon <int> 4, 3, 5, 4, 3, 3, 4, 4, 4, 2, 5, 5, 5, 4, 5…
## $ far_sos_kirilgan <int> 3, 2, 5, 3, 3, 2, 4, 3, 2, 2, 5, 5, 4, 3, 4…
## $ far_yon_belge <int> 5, 3, 5, 4, 5, 5, 4, 5, 4, 4, 4, 4, 5, 3, 4…
## $ far_yon_kultur <int> 3, 2, 5, 4, 4, 4, 5, 4, 4, 3, 4, 4, 4, 4, 4…
## $ far_yon_mevzuat <int> 5, 4, 5, 5, 5, 4, 5, 5, 4, 4, 4, 5, 5, 4, 5…
## $ far_yon_cevre_politika <int> 5, 3, 5, 3, 4, 3, 5, 5, 4, 4, 4, 5, 5, 5, 4…
## $ far_yon_sur_hedef <int> 3, 1, 5, 4, 4, 4, 5, 5, 5, 3, 5, 5, 5, 4, 4…
## $ far_eko_verimlilik <int> 5, 4, 5, 4, 4, 3, 4, 5, 3, 3, 4, 5, 5, 4, 4…
## $ far_eko_satin_alma <int> 3, 2, 5, 3, 3, 4, 3, 4, 3, 3, 4, 5, 5, 3, 3…
## $ far_eko_teknoloji <int> 4, 1, 5, 4, 4, 4, 4, 5, 3, 3, 4, 5, 5, 3, 3…
## $ far_birim_sur <int> 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0…
## $ far_birim_cevre <int> 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1…
## $ far_birim_isg <int> 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1…
## $ far_birim_kalite <int> 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_birim_idari <int> 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1…
## $ far_birim_satin_alma <int> 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1…
## $ far_birim_finans <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1…
## $ far_birim_pazarlama <int> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1…
## $ far_birim_muhasebe <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1…
## $ far_birim_ik <int> 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1…
## $ far_birim_iletisim <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0…
## $ far_birim_lojistik <int> 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1…
## $ far_birim_depolama <int> 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1…
## $ far_itici_yonetim <int> 4, 1, 4, 4, 4, 4, 4, 4, 3, 3, 5, 5, 4, 5, 3…
## $ far_itici_mutakabat <int> 5, 1, 4, 3, 5, 2, 4, 4, 3, 3, 5, 4, 5, 1, 2…
## $ far_itici_oneri <int> 2, 3, 4, 3, 4, 2, 3, 4, 4, 3, 3, 5, 5, 4, 4…
## $ far_itici_duzeltici <int> 2, 4, 5, 3, 4, 3, 5, 4, 3, 3, 3, 5, 5, 4, 4…
## $ far_itici_sertifikasyon <int> 3, 4, 5, 4, 5, 4, 5, 5, 3, 4, 3, 4, 2, 5, 4…
## $ far_itici_ortaklik <int> 4, 3, 4, 4, 5, 2, 4, 5, 2, 4, 5, 1, 3, 1, 3…
## $ far_itici_sur_hedef <int> 3, 2, 5, 3, 4, 3, 4, 5, 3, 3, 4, 4, 5, 3, 2…
## $ far_itici_musteri <int> 5, 5, 4, 4, 4, 4, 2, 5, 2, 4, 5, 5, 4, 5, 4…
## $ far_itici_stk <int> 4, 1, 4, 3, 4, 3, 2, 3, 3, 3, 5, 5, 3, 3, 3…
## $ far_itici_verimlilik <int> 5, 1, 5, 3, 4, 3, 5, 5, 3, 3, 5, 5, 5, 1, 3…
## $ far_itici_mevzuat <int> 4, 3, 4, 4, 5, 4, 5, 4, 2, 4, 5, 5, 4, 5, 3…
## $ far_itici_merak <int> 3, 1, 5, 3, 5, 4, 4, 4, 3, 3, 2, 5, 2, 3, 3…
## $ far_engel_veri <int> 2, 4, 3, 5, 3, 2, 2, 2, 3, 4, 2, 4, 4, 4, 3…
## $ far_engel_maliyet <int> 4, 5, 4, 3, 3, 4, 5, 3, 4, 5, 2, 4, 5, 4, 5…
## $ far_engel_2_personel <int> 5, 5, 3, 5, 5, 5, 3, 3, 3, 3, 2, 4, 4, 5, 4…
## $ far_engel_direnc <int> 5, 5, 2, 3, 3, 4, 5, 2, 3, 5, 3, 5, 3, 4, 4…
## $ far_engel_kar <int> 4, 5, 2, 3, 3, 3, 5, 2, 4, 4, 1, 4, 5, 4, 5…
## $ far_engel_iletisim <int> 4, 5, 1, 4, 2, 3, 2, 1, 3, 5, 1, 5, 3, 3, 4…
## $ far_arac_iso14001 <int> 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_arac_iso45001 <int> 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1…
## $ far_arac_iso9001 <int> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_arac_iso50001 <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1…
## $ far_arac_iso27001 <int> 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0…
## $ far_arac_ohsas18001 <int> 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0…
## $ far_arac_brc <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ far_arac_iatf16949 <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…
## $ far_arac_global_compact <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ far_arac_diger <int> 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0…
## $ far_cev_fay_enerji <int> 3, 3, 4, 5, 5, 2, 3, 5, 4, 3, 5, 5, 5, 4, 5…
## $ far_cev_fay_atik <int> 3, 4, 5, 3, 4, 3, 5, 5, 3, 3, 5, 5, 5, 4, 5…
## $ far_cev_fay_emisyon <int> 3, 4, 4, 3, 3, 2, 2, 4, 3, 3, 4, 5, 5, 4, 3…
## $ far_cev_fay_su <int> 3, 2, 4, 3, 4, 2, 3, 4, 4, 3, 5, 5, 5, 4, 5…
## $ far_sos_fay_nitelikli <int> 4, 2, 5, 4, 3, 5, 5, 4, 4, 3, 4, 5, 2, 4, 5…
## $ far_sos_fay_guvenli <int> 4, 2, 5, 4, 4, 3, 5, 5, 4, 3, 5, 5, 5, 4, 5…
## $ far_sos_fay_toplum_refah <int> 4, 1, 5, 4, 5, 2, 5, 4, 5, 3, 5, 5, 4, 4, 5…
## $ far_sos_fay_esit_calisma <int> 4, 1, 5, 4, 4, 2, 4, 4, 5, 3, 5, 5, 4, 4, 5…
## $ far_sos_fay_rekabet <int> 2, 3, 5, 4, 3, 4, 3, 5, 4, 3, 4, 5, 4, 4, 5…
## $ far_sos_fay_calisan_refah <int> 4, 1, 5, 4, 4, 3, 3, 4, 3, 3, 5, 5, 4, 4, 5…
## $ far_yon_yesil <int> 2, 1, 4, 3, 3, 3, 3, 4, 4, 2, 5, 4, 5, 3, 3…
## $ far_yon_marka <int> 2, 2, 5, 4, 3, 4, 5, 4, 5, 2, 5, 5, 5, 4, 5…
## $ far_eko_maliyet <int> 5, 4, 4, 4, 2, 4, 3, 4, 4, 3, 4, 5, 5, 3, 5…
## $ far_eko_karlilik <int> 5, 5, 4, 4, 2, 5, 3, 4, 4, 3, 4, 5, 4, 3, 5…
## $ far_pay_tedarikci <int> 3, 4, 4, 3, 3, 2, 2, 3, 4, 2, 3, 5, 4, 3, 5…
## $ far_pay_bilinc <int> 3, 4, 5, 3, 4, 3, 3, 4, 4, 2, 5, 5, 5, 4, 5…
## $ far_pay_top_fayda <int> 3, 1, 5, 4, 5, 2, 4, 4, 5, 2, 5, 5, 5, 3, 5…
## $ far_pay_musteri <int> 3, 5, 4, 4, 3, 3, 5, 4, 5, 2, 3, 5, 5, 4, 5…
## $ far_pay_ulke_ekonomi <int> 3, 2, 4, 4, 5, 3, 5, 4, 5, 2, 3, 5, 5, 4, 5…
## $ far_pay_cev_sagligi <int> 4, 1, 4, 3, 4, 2, 5, 4, 4, 2, 4, 5, 5, 5, 5…
## $ far_pay_dongu_ekonomi <int> 3, 2, 4, 4, 4, 4, 4, 4, 4, 2, 4, 5, 5, 4, 5…
## $ far_pay_yatirimci <int> 1, 4, 4, 4, 3, 2, 2, 4, 4, 2, 3, 5, 4, 5, 5…
## $ far_pay_etik <int> 3, 2, 5, 3, 5, 3, 5, 4, 5, 2, 3, 5, 5, 5, 5…
## $ far_pay_engelli <int> 3, 1, 5, 3, 3, 2, 2, 3, 4, 2, 5, 5, 4, 4, 5…
## $ far_pay_insan_haklari <int> 5, 2, 5, 3, 4, 3, 4, 4, 5, 2, 5, 5, 4, 5, 5…
## $ far_pay_diger_isletme <int> 3, 2, 5, 4, 4, 4, 5, 4, 4, 2, 5, 5, 5, 4, 5…
## $ Yon_plan_ust_yonetim <int> 3, 4, 5, 4, 4, 4, 3, 5, 4, 2, 5, 5, 4, 5, 5…
## $ Yon_plan_amac <int> 3, 2, 5, 4, 4, 4, 3, 5, 4, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_stratejik_plan <int> 3, 2, 4, 4, 5, 4, 3, 5, 5, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_risk_analiz <int> 4, 1, 4, 4, 4, 3, 3, 5, 4, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_eylem_plan <int> 3, 2, 4, 4, 4, 3, 3, 5, 4, 2, 5, 5, 4, 5, 5…
## $ Yon_plan_kaynak <int> 3, 3, 4, 4, 5, 4, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_plan_oncelik <int> 2, 1, 4, 3, 4, 3, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_plan_kalkinma_amac <int> 4, 1, 4, 3, 4, 3, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_uyg_veri <int> 3, 1, 4, 3, 3, 3, 4, 5, 4, 3, 4, 5, 4, 4, 4…
## $ Yon_uyg_birim <int> 3, 2, 4, 3, 3, 3, 4, 5, 4, 3, 4, 5, 5, 4, 4…
## $ Yon_uyg_teknolojik <int> 3, 1, 4, 3, 4, 4, 5, 5, 4, 3, 4, 5, 5, 4, 4…
## $ Yon_uyg_egitim <int> 4, 2, 4, 3, 4, 4, 4, 4, 4, 3, 4, 5, 5, 4, 5…
## $ Yon_uyg_yetkinlik <int> 2, 1, 5, 3, 5, 3, 4, 5, 5, 3, 4, 5, 4, 4, 5…
## $ Yon_uyg_motivasyon <int> 2, 1, 5, 3, 5, 3, 3, 4, 5, 3, 5, 5, 3, 3, 5…
## $ Yon_uyg_calisan_baglilik <int> 4, 1, 5, 4, 4, 2, 2, 4, 5, 3, 4, 5, 3, 3, 5…
## $ Yon_uyg_adil_is <int> 4, 1, 5, 3, 5, 3, 2, 4, 4, 3, 4, 5, 4, 3, 5…
## $ Yon_uyg_firsat_esitlik <int> 5, 1, 5, 3, 5, 3, 3, 4, 4, 3, 4, 5, 5, 3, 5…
## $ Yon_ilet_internet <int> 1, 1, 5, 1, 2, 2, 3, 4, 4, 2, 4, 5, 4, 1, 5…
## $ Yon_ilet_pano <int> 4, 1, 5, 2, 5, 2, 3, 4, 4, 2, 4, 5, 4, 2, 5…
## $ Yon_ilet_kulup <int> 1, 1, 4, 1, 3, 2, 2, 5, 5, 1, 5, 5, 4, 2, 5…
## $ Yon_ilet_birim <int> 1, 1, 4, 3, 4, 3, 5, 5, 4, 1, 5, 5, 5, 3, 5…
## $ Yon_ilet_rapor <int> 1, 1, 4, 4, 4, 1, 3, 5, 5, 1, 5, 5, 4, 4, 5…
## $ Yon_kont_tedarikci <int> 3, 2, 4, 3, 3, 3, 4, 4, 3, 2, 4, 5, 3, 3, 5…
## $ Yon_kont_gozden_gecirme <int> 4, 2, 5, 4, 4, 3, 5, 4, 3, 2, 4, 5, 4, 3, 5…
## $ Yon_kont_sertifika <int> 3, 3, 5, 4, 4, 4, 4, 5, 3, 2, 4, 5, 4, 4, 5…
## $ Yon_kont_surec_hedef <int> 3, 2, 5, 4, 5, 3, 4, 4, 2, 2, 4, 5, 4, 4, 5…
## $ Yon_kont_bilim_temelli <int> 4, 2, 4, 4, 4, 3, 5, 4, 2, 2, 4, 5, 4, 4, 5…
## $ Yon_iyiles_hafiza <int> 3, 1, 5, 4, 5, 4, 4, 4, 5, 2, 5, 5, 5, 3, 5…
## $ Yon_iyiles_calisan_gorus <int> 4, 4, 5, 3, 5, 3, 3, 4, 4, 2, 5, 5, 5, 4, 5…
## $ Yon_iyiles_duzeltici_rapor <int> 4, 3, 5, 4, 4, 3, 3, 4, 5, 2, 5, 5, 4, 4, 5…
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")
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 %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>% names
## [1] "isletme_sektor" "isletme_isim" "isletme_yabanciOrtak"
## [4] "isletme_yas" "isletme_olcek" "isletme_cal_say"
## [7] "kisi_cinsiyet" "kisi_egitim" "kisi_bolum_poz"
## [10] "kisi_kac_yildir"
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(3)) %>%
table
## isletme_yabanciOrtak
## Evet Hayir
## 51 70
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(5,6)) %>% table
## isletme_cal_say
## isletme_olcek 10 - 49 kisi 250 kisi ve uzeri 50 - 249 kisi
## Buyuk 1 36 11
## Kucuk 9 0 6
## Orta 9 9 40
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(4)) %>% table
## isletme_yas
## 1 - 10 yil 11 - 30 yil 30 yildan fazla
## 15 51 55
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(7)) %>% table
## kisi_cinsiyet
## Belirtmek istemiyorum Erkek Kadin
## 2 74 45
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(8)) %>% table
## kisi_egitim
## Doktora Lisans Lisansustu Lise Yuksekokul
## 2 78 37 1 3
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(9)) %>% datatable
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(c(10)) %>% table
## kisi_kac_yildir
## 1 - 5 yil
## 35
## 1 yildan 2
## 6
## 10 yildan fazla
## 44
## 20 yildir ayni sektordeyim. Bunun 12 yili fabrikada Ar-ge bolumunde, 8 senesi ise satis-p2arlama bolumunde gecti
## 1
## 6 - 10 yil
## 35
see_sur <- extract_factors(df,"far_sur",2)
## ________________ START --> far_sur _____________________
##
## cronbach_alpa = 0.92
##
## Loadings:
## Factor1 Factor2
## far_sur_kaynak 1.031
## far_sur_gelecek 0.439 0.459
## far_sur_adil_is 0.772
## far_sur_toplum 0.943 -0.143
## far_sur_cevre_koruma 0.786 0.101
## far_sur_paydas 0.444 0.378
## far_sur_eko_performans 0.139 0.533
## far_sur_calisan_hak 0.715 0.170
## far_sur_tarim 0.660 -0.105
##
## Factor1 Factor2
## SS loadings 3.467 1.779
## Proportion Var 0.385 0.198
## Cumulative Var 0.385 0.583
##
## faktor analizining acikladigi oranlar:
## explained_variances
## far_sur_kaynak 0.9435916
## far_sur_gelecek 0.7231282
## far_sur_adil_is 0.7080690
## far_sur_toplum 0.6964625
## far_sur_cevre_koruma 0.7537155
## far_sur_paydas 0.6064055
## far_sur_eko_performans 0.4214697
## far_sur_calisan_hak 0.7336435
## far_sur_tarim 0.3367582
##
## likelihood ratio test | p-value: 0.06571096
##
## factors are sufficient
## ________________ END _____________________
see_sur_scores <- see_sur[[1]] $ scores
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(3),"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
## -4.8050 -0.9329 0.4233 0.7697 3.9615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3573 0.1965 -1.819 0.0715 .
## isletme_yabanciOrtak_Hayir 0.6176 0.2583 2.391 0.0184 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 119 degrees of freedom
## Multiple R-squared: 0.04584, Adjusted R-squared: 0.03782
## F-statistic: 5.717 on 1 and 119 DF, p-value: 0.01837
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1971 -0.4102 0.0624 0.7704 3.4458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2734 0.2022 1.352 0.179
## isletme_yabanciOrtak_Hayir -0.4726 0.2659 -1.778 0.078 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.444 on 119 degrees of freedom
## Multiple R-squared: 0.02587, Adjusted R-squared: 0.01768
## F-statistic: 3.16 on 1 and 119 DF, p-value: 0.07803
du_df <- dummy_func(df,c(4),"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
## -4.2879 -1.0077 0.5104 0.8430 4.4785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3873 0.3674 1.054 0.294
## isletme_yas_11...30.yil -0.6441 0.4180 -1.541 0.126
## isletme_yas_30.yildan.fazla -0.2549 0.4145 -0.615 0.540
##
## Residual standard error: 1.423 on 118 degrees of freedom
## Multiple R-squared: 0.02679, Adjusted R-squared: 0.01029
## F-statistic: 1.624 on 2 and 118 DF, p-value: 0.2015
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3766 -0.3086 -0.0749 0.8103 3.0748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8352 0.3702 -2.256 0.0259 *
## isletme_yas_11...30.yil 1.0070 0.4211 2.391 0.0184 *
## isletme_yas_30.yildan.fazla 0.9036 0.4176 2.164 0.0325 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.434 on 118 degrees of freedom
## Multiple R-squared: 0.04799, Adjusted R-squared: 0.03185
## F-statistic: 2.974 on 2 and 118 DF, p-value: 0.05494
du_df <- dummy_func(df,c(3,4),"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
## -4.5616 -0.9141 0.3816 0.9118 4.2048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06362 0.38585 0.165 0.8693
## isletme_yabanciOrtak_Hayir 0.60692 0.25777 2.354 0.0202 *
## isletme_yas_11...30.yil -0.65359 0.41016 -1.594 0.1137
## isletme_yas_30.yildan.fazla -0.30637 0.40732 -0.752 0.4535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 117 degrees of freedom
## Multiple R-squared: 0.07082, Adjusted R-squared: 0.04699
## F-statistic: 2.972 on 3 and 117 DF, p-value: 0.03464
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1553 -0.5086 -0.0157 0.9199 3.2961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5734 0.3920 -1.463 0.1462
## isletme_yabanciOrtak_Hayir -0.4908 0.2619 -1.874 0.0634 .
## isletme_yas_11...30.yil 1.0147 0.4167 2.435 0.0164 *
## isletme_yas_30.yildan.fazla 0.9452 0.4138 2.284 0.0242 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 117 degrees of freedom
## Multiple R-squared: 0.07573, Adjusted R-squared: 0.05203
## F-statistic: 3.195 on 3 and 117 DF, p-value: 0.02611
du_df <- dummy_func(df,c(5),"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
## -4.4814 -0.9939 0.4672 1.0090 4.2851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03352 0.20728 -0.162 0.872
## isletme_olcek_Kucuk 0.38550 0.42480 0.907 0.366
## isletme_olcek_Orta -0.02977 0.28022 -0.106 0.916
##
## Residual standard error: 1.436 on 118 degrees of freedom
## Multiple R-squared: 0.008736, Adjusted R-squared: -0.008066
## F-statistic: 0.5199 on 2 and 118 DF, p-value: 0.5959
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0459 -0.4098 0.0221 0.7193 2.9736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2730 0.2095 1.303 0.195
## isletme_olcek_Kucuk -0.5322 0.4294 -1.239 0.218
## isletme_olcek_Orta -0.4319 0.2833 -1.525 0.130
##
## Residual standard error: 1.452 on 118 degrees of freedom
## Multiple R-squared: 0.02375, Adjusted R-squared: 0.007199
## F-statistic: 1.435 on 2 and 118 DF, p-value: 0.2422
du_df <- dummy_func(df,c(3,4,5),"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
## -4.4703 -0.9104 0.3774 0.8766 4.2961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02953 0.46699 0.063 0.9497
## isletme_yabanciOrtak_Hayir 0.58661 0.26344 2.227 0.0279 *
## isletme_yas_11...30.yil -0.60312 0.43025 -1.402 0.1637
## isletme_yas_30.yildan.fazla -0.22259 0.45106 -0.493 0.6226
## isletme_olcek_Kucuk 0.22877 0.46312 0.494 0.6223
## isletme_olcek_Orta -0.08735 0.28056 -0.311 0.7561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 115 degrees of freedom
## Multiple R-squared: 0.07533, Adjusted R-squared: 0.03513
## F-statistic: 1.874 on 5 and 115 DF, p-value: 0.1043
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0208 -0.5532 0.0046 0.8369 3.1319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3793 0.4732 -0.801 0.4245
## isletme_yabanciOrtak_Hayir -0.4756 0.2670 -1.782 0.0775 .
## isletme_yas_11...30.yil 0.9696 0.4360 2.224 0.0281 *
## isletme_yas_30.yildan.fazla 0.8849 0.4571 1.936 0.0553 .
## isletme_olcek_Kucuk -0.1072 0.4693 -0.228 0.8197
## isletme_olcek_Orta -0.2987 0.2843 -1.051 0.2956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 115 degrees of freedom
## Multiple R-squared: 0.0849, Adjusted R-squared: 0.04512
## F-statistic: 2.134 on 5 and 115 DF, p-value: 0.06629
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
## -4.5403 -0.9434 0.4964 0.9799 4.2262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2123 0.3301 0.643 0.521
## isletme_cal_say_50...249.kisi -0.2167 0.3812 -0.568 0.571
## isletme_cal_say_250.kisi.ve.uzeri -0.2964 0.3937 -0.753 0.453
##
## Residual standard error: 1.439 on 118 degrees of freedom
## Multiple R-squared: 0.004788, Adjusted R-squared: -0.01208
## F-statistic: 0.2839 on 2 and 118 DF, p-value: 0.7534
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0807 -0.3374 -0.0095 0.9556 3.0460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10296 0.33517 -0.307 0.759
## isletme_cal_say_50...249.kisi -0.02114 0.38703 -0.055 0.957
## isletme_cal_say_250.kisi.ve.uzeri 0.30362 0.39972 0.760 0.449
##
## Residual standard error: 1.461 on 118 degrees of freedom
## Multiple R-squared: 0.01135, Adjusted R-squared: -0.005408
## F-statistic: 0.6772 on 2 and 118 DF, p-value: 0.51
du_df <- dummy_func(df,c(3,4,6),"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
## -4.6472 -0.9776 0.3402 0.8819 4.1193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006269 0.433704 0.014 0.9885
## isletme_yabanciOrtak_Hayir 0.629035 0.264715 2.376 0.0191 *
## isletme_yas_11...30.yil -0.646683 0.458042 -1.412 0.1607
## isletme_yas_30.yildan.fazla -0.252965 0.475465 -0.532 0.5957
## isletme_cal_say_50...249.kisi 0.113901 0.414194 0.275 0.7838
## isletme_cal_say_250.kisi.ve.uzeri -0.097544 0.447334 -0.218 0.8278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 115 degrees of freedom
## Multiple R-squared: 0.07506, Adjusted R-squared: 0.03485
## F-statistic: 1.866 on 5 and 115 DF, p-value: 0.1056
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9589 -0.5409 0.0363 0.7472 3.1860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2940 0.4369 -0.673 0.5023
## isletme_yabanciOrtak_Hayir -0.5725 0.2667 -2.147 0.0339 *
## isletme_yas_11...30.yil 1.2102 0.4614 2.623 0.0099 **
## isletme_yas_30.yildan.fazla 1.0959 0.4790 2.288 0.0240 *
## isletme_cal_say_50...249.kisi -0.5896 0.4172 -1.413 0.1604
## isletme_cal_say_250.kisi.ve.uzeri -0.2831 0.4506 -0.628 0.5311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 115 degrees of freedom
## Multiple R-squared: 0.09549, Adjusted R-squared: 0.05616
## F-statistic: 2.428 on 5 and 115 DF, p-value: 0.0393
du_df <- dummy_func(df,c(3,4,5,6),"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
## -4.5486 -0.9288 0.3253 0.8933 4.2179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07659 0.56799 0.135 0.8930
## isletme_yabanciOrtak_Hayir 0.64193 0.27050 2.373 0.0193 *
## isletme_yas_11...30.yil -0.64445 0.46191 -1.395 0.1657
## isletme_yas_30.yildan.fazla -0.20787 0.48504 -0.429 0.6691
## isletme_olcek_Kucuk 0.08161 0.54468 0.150 0.8812
## isletme_olcek_Orta -0.27165 0.34936 -0.778 0.4384
## isletme_cal_say_50...249.kisi 0.20150 0.43911 0.459 0.6472
## isletme_cal_say_250.kisi.ve.uzeri -0.15237 0.53114 -0.287 0.7747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 113 degrees of freedom
## Multiple R-squared: 0.08381, Adjusted R-squared: 0.02705
## F-statistic: 1.477 on 7 and 113 DF, p-value: 0.1826
## ######[1] "Y = df_pilot$verimlilik_boyutu ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9207 -0.5526 0.0561 0.7960 3.1201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04657 0.57335 -0.081 0.9354
## isletme_yabanciOrtak_Hayir -0.53715 0.27305 -1.967 0.0516 .
## isletme_yas_11...30.yil 1.18579 0.46627 2.543 0.0123 *
## isletme_yas_30.yildan.fazla 1.07474 0.48962 2.195 0.0302 *
## isletme_olcek_Kucuk -0.27785 0.54982 -0.505 0.6143
## isletme_olcek_Orta -0.27383 0.35265 -0.776 0.4391
## isletme_cal_say_50...249.kisi -0.61232 0.44326 -1.381 0.1699
## isletme_cal_say_250.kisi.ve.uzeri -0.47554 0.53615 -0.887 0.3770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 113 degrees of freedom
## Multiple R-squared: 0.1004, Adjusted R-squared: 0.04463
## F-statistic: 1.801 on 7 and 113 DF, p-value: 0.09382