2.ÖDEV

library(wooldridge)

data("discrim")

head(discrim)

##   psoda pfries pentree wagest nmgrs nregs hrsopen  emp psoda2 pfries2 pentree2
## 1  1.12   1.06    1.02   4.25     3     5    16.0 27.5   1.11    1.11     1.05
## 2  1.06   0.91    0.95   4.75     3     3    16.5 21.5   1.05    0.89     0.95
## 3  1.06   0.91    0.98   4.25     3     5    18.0 30.0   1.05    0.94     0.98
## 4  1.12   1.02    1.06   5.00     4     5    16.0 27.5   1.15    1.05     1.05
## 5  1.12     NA    0.49   5.00     3     3    16.0  5.0   1.04    1.01     0.58
## 6  1.06   0.95    1.01   4.25     4     4    15.0 17.5   1.05    0.94     1.00
##   wagest2 nmgrs2 nregs2 hrsopen2 emp2 compown chain density    crmrte state
## 1    5.05      5      5     15.0 27.0       1     3    4030 0.0528866     1
## 2    5.05      4      3     17.5 24.5       0     1    4030 0.0528866     1
## 3    5.05      4      5     17.5 25.0       0     1   11400 0.0360003     1
## 4    5.05      4      5     16.0   NA       0     3    8345 0.0484232     1
## 5    5.05      3      3     16.0 12.0       0     1     720 0.0615890     1
## 6    5.05      3      4     15.0 28.0       0     1    4424 0.0334823     1
##     prpblck    prppov   prpncar hseval nstores income county     lpsoda
## 1 0.1711542 0.0365789 0.0788428 148300       3  44534     18 0.11332869
## 2 0.1711542 0.0365789 0.0788428 148300       3  44534     18 0.05826885
## 3 0.0473602 0.0879072 0.2694298 169200       3  41164     12 0.05826885
## 4 0.0528394 0.0591227 0.1366903 171600       3  50366     10 0.11332869
## 5 0.0344800 0.0254145 0.0738020 249100       1  72287     10 0.11332869
## 6 0.0591327 0.0835001 0.1151341 148000       2  44515     18 0.05826885
##       lpfries  lhseval  lincome ldensity NJ BK KFC RR
## 1  0.05826885 11.90699 10.70401 8.301521  1  0   0  1
## 2 -0.09431065 11.90699 10.70401 8.301521  1  1   0  0
## 3 -0.09431065 12.03884 10.62532 9.341369  1  1   0  0
## 4  0.01980261 12.05292 10.82707 9.029418  1  0   0  1
## 5          NA 12.42561 11.18840 6.579251  1  1   0  0
## 6 -0.05129331 11.90497 10.70358 8.394799  1  1   0  0

psoda: price of medium soda, 1st wave, orta sodanın fiyatı. -prpblck: proportion black, zipcode, restoranın bulunduğu bölgede siyahi oranı -income: median family income, zipcode, restoranın bulunduğu bölgenin medyan (ortanca) aile geliri. -prppov: proportion in poverty, zipcode, restoranın bulunduğu bölgede yoksulluk oranı

help(discrim)

## httpd yardım sunucusu başlatılıyor ... tamamlandı

mean(discrim$prpblck)

## [1] NA

sd(discrim$prpblck)

## [1] NA

mean(discrim$income)

## [1] NA

sd(discrim$income)

## [1] NA

sum(is.na(discrim$prpblck))

## [1] 1

sum(is.na(discrim$income))

## [1] 1

mean(discrim$prpblck,na.rm = TRUE)

## [1] 0.1134864

sd(discrim$prpblck,na.rm = TRUE)

## [1] 0.1824165

mean(discrim$income, na.rm = TRUE)

## [1] 47053.78

sd(discrim$income, na.rm = TRUE)

## [1] 13179.29

library(vtable)

## Zorunlu paket yükleniyor: kableExtra

sumtable(discrim, summ=c('notNA(x)', 'countNA(x)', 'mean(x)','sd(x)'),out='return')

##    Variable NotNA CountNA   Mean    Sd
## 1     psoda   402       8      1 0.089
## 2    pfries   393      17   0.92  0.11
## 3   pentree   398      12    1.3  0.64
## 4    wagest   390      20    4.6  0.35
## 5     nmgrs   404       6    3.4     1
## 6     nregs   388      22    3.6   1.2
## 7   hrsopen   410       0     14   2.8
## 8       emp   404       6     18   9.4
## 9    psoda2   388      22      1 0.094
## 10  pfries2   382      28   0.94  0.11
## 11 pentree2   386      24    1.4  0.65
## 12  wagest2   389      21      5  0.25
## 13   nmgrs2   404       6    3.5   1.1
## 14   nregs2   388      22    3.6   1.2
## 15 hrsopen2   399      11     14   2.8
## 16     emp2   397      13     18   8.6
## 17  compown   410       0   0.34  0.48
## 18    chain   410       0    2.1   1.1
## 19  density   409       1   4562  5132
## 20   crmrte   409       1  0.053 0.047
## 21    state   410       0    1.2  0.39
## 22  prpblck   409       1   0.11  0.18
## 23   prppov   409       1  0.071 0.067
## 24  prpncar   409       1   0.11  0.12
## 25   hseval   409       1 147399 56070
## 26  nstores   410       0    3.1   1.8
## 27   income   409       1  47054 13179
## 28   county   410       0     14     8
## 29   lpsoda   402       8   0.04 0.085
## 30  lpfries   393      17 -0.088  0.12
## 31  lhseval   409       1     12  0.39
## 32  lincome   409       1     11  0.28
## 33 ldensity   409       1      8     1
## 34       NJ   410       0   0.81  0.39
## 35       BK   410       0   0.42  0.49
## 36      KFC   410       0    0.2   0.4
## 37       RR   410       0   0.24  0.43

discrimreg <- lm(psoda~prpblck+income, data = discrim)
summary(discrimreg)

## 
## Call:
## lm(formula = psoda ~ prpblck + income, data = discrim)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.29401 -0.05242  0.00333  0.04231  0.44322 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.563e-01  1.899e-02  50.354  < 2e-16 ***
## prpblck     1.150e-01  2.600e-02   4.423 1.26e-05 ***
## income      1.603e-06  3.618e-07   4.430 1.22e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08611 on 398 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.06422,    Adjusted R-squared:  0.05952 
## F-statistic: 13.66 on 2 and 398 DF,  p-value: 1.835e-06

basitdiscrimreg <- lm(psoda~prpblck, data = discrim)
summary(basitdiscrimreg)

## 
## Call:
## lm(formula = psoda ~ prpblck, data = discrim)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30884 -0.05963  0.01135  0.03206  0.44840 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.03740    0.00519  199.87  < 2e-16 ***
## prpblck      0.06493    0.02396    2.71  0.00702 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0881 on 399 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.01808,    Adjusted R-squared:  0.01561 
## F-statistic: 7.345 on 1 and 399 DF,  p-value: 0.007015

logdiscrimreg <- lm(log(psoda)~prpblck+log(income), data = discrim)
summary(logdiscrimreg)

## 
## Call:
## lm(formula = log(psoda) ~ prpblck + log(income), data = discrim)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33563 -0.04695  0.00658  0.04334  0.35413 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.79377    0.17943  -4.424 1.25e-05 ***
## prpblck      0.12158    0.02575   4.722 3.24e-06 ***
## log(income)  0.07651    0.01660   4.610 5.43e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0821 on 398 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.06809,    Adjusted R-squared:  0.06341 
## F-statistic: 14.54 on 2 and 398 DF,  p-value: 8.039e-07

paste( (0.2*100)*0.122, "yüzdelik artış")

## [1] "2.44 yüzdelik artış"

logdiscrimregprpov <- lm(log(psoda)~prpblck+log(income)+prppov, data = discrim)
summary(logdiscrimregprpov)

## 
## Call:
## lm(formula = log(psoda) ~ prpblck + log(income) + prppov, data = discrim)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.32218 -0.04648  0.00651  0.04272  0.35622 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.46333    0.29371  -4.982  9.4e-07 ***
## prpblck      0.07281    0.03068   2.373   0.0181 *  
## log(income)  0.13696    0.02676   5.119  4.8e-07 ***
## prppov       0.38036    0.13279   2.864   0.0044 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08137 on 397 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.08696,    Adjusted R-squared:  0.08006 
## F-statistic:  12.6 on 3 and 397 DF,  p-value: 6.917e-08

cor(log(discrim$income), discrim$prppov, use = "complete.obs")

## [1] -0.838467

8. Yüksek düzeyde ilişkili olmalarına rağmen, her ikisinin de dahil edilmesi mükemmel bir doğrusallık ile sonuçlanmaz ve bunun yerine, ayırt edici etkiyi izole etmeye yardımcı olan başka bir kontrol değişkeni ekleyerek modeli tamamlar.