sınav odevi

Sınav öncesi ödevi

library(wooldridge)

Ana veri setimiz wooldridge paketimizi install.packages yaptıktan sonra library yapıyoruz

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, union

yardımcı paketlerimizi kütüphanemize yükledik ve library yaparak kullanıma hazır hale getirdik.

data(fish)

wooldridge paketimizin içinde yer alan “fish” data veri setini kullnmak için yüklüyoruz

head(fish)

##        prca      prcw qtya qtyw mon tues wed thurs speed2 wave2 speed3 wave3
## 1 0.6222222 0.7666667 1875 2205   1    0   0     0     15   7.5     20   9.0
## 2 0.9722222 1.1750000 2900  566   0    0   1     0     10   5.0     20   7.5
## 3 1.2333330 1.4750000  770 1525   0    0   0     1     10   6.0     20   4.0
## 4 1.9285710 1.6250000  927  943   0    0   0     0     15   6.0     20   5.0
## 5 0.8031250 0.8642857 4220 2665   1    0   0     0     10   3.5     20   3.5
## 6 0.8818182 0.9142857 4230 1375   0    1   0     0     15   4.5     15   3.5
##      avgprc totqty      lavgprc  ltotqty t      lavgp_1     gavgprc    gavgp_1
## 1 0.7002860   4080 -0.356266499 8.313852 1           NA          NA         NA
## 2 1.0053359   3466  0.005321742 8.150757 2 -0.356266499  0.36158824         NA
## 3 1.3939178   2295  0.332118332 7.738488 3  0.005321742  0.32679659  0.3615882
## 4 1.7754868   1870  0.574074626 7.533694 4  0.332118332  0.24195629  0.3267966
## 5 0.8267987   6885 -0.190194055 8.837100 5  0.574074626 -0.76426870  0.2419563
## 6 0.8897830   5605 -0.116777636 8.631414 6 -0.190194055  0.07341642 -0.7642687

ilk 6 gözlemi tablo halinde göstermek için head komutunu kullanırız.

tail(fish)

##         prca      prcw qtya qtyw mon tues wed thurs speed2 wave2 speed3 wave3
## 92 0.3088235 0.3375000 5700  900   0    0   0     0     10   3.5     15   3.0
## 93 0.3208333 0.6437500 2290 1160   1    0   0     0     15   4.5     15   3.5
## 94 0.8750000 0.9333333  540  690   0    1   0     0     12   4.5     25   4.0
## 95 1.2357140 1.2500000 1000  250   0    0   1     0     12   5.0     25   4.5
## 96 1.2500000 1.2500000 2500 1480   0    0   0     1     15   6.0     18   4.5
## 97 1.7300000 1.7083330 3040  740   0    0   0     0     15   9.0     20   5.0
##       avgprc totqty     lavgprc  ltotqty  t     lavgp_1     gavgprc     gavgp_1
## 92 0.3127339   6600 -1.16240251 8.794825 92 -0.89633626 -0.26606625 -0.10170186
## 93 0.4294082   3450 -0.84534729 8.146130 93 -1.16240251  0.31705523 -0.26606625
## 94 0.9077235   1230 -0.09681541 7.114769 94 -0.84534729  0.74853188  0.31705523
## 95 1.2385712   1250  0.21395843 7.130899 95 -0.09681541  0.31077385  0.74853188
## 96 1.2500000   3980  0.22314355 8.289037 96  0.21395843  0.00918512  0.31077385
## 97 1.7257583   3780  0.54566658 8.237479 97  0.22314355  0.32252303  0.00918512

son 6 gözlem için tail komutunu kullanırız.

paged_table(fish)

Tüm veri setimizin gözlemlerini göstermek istiyorsak paged_table komutunu kullanırız.

summary(fish)

##       prca             prcw            qtya           qtyw     
##  Min.   :0.2667   Min.   :0.250   Min.   : 110   Min.   :  60  
##  1st Qu.:0.5833   1st Qu.:0.625   1st Qu.:1145   1st Qu.: 690  
##  Median :0.7475   Median :0.795   Median :1885   Median :1260  
##  Mean   :0.8198   Mean   :0.892   Mean   :2590   Mean   :1537  
##  3rd Qu.:1.0417   3rd Qu.:1.175   3rd Qu.:3620   3rd Qu.:1935  
##  Max.   :1.9286   Max.   :1.708   Max.   :9120   Max.   :6800  
##                                                                
##       mon              tues             wed             thurs       
##  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   :0.1856   Mean   :0.1959   Mean   :0.2062   Mean   :0.2062  
##  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  
##                                                                     
##      speed2          wave2            speed3          wave3       
##  Min.   : 5.00   Min.   : 2.500   Min.   :10.00   Min.   : 3.000  
##  1st Qu.:10.00   1st Qu.: 4.000   1st Qu.:15.00   1st Qu.: 3.500  
##  Median :10.00   Median : 4.500   Median :20.00   Median : 4.500  
##  Mean   :11.98   Mean   : 5.093   Mean   :20.93   Mean   : 5.021  
##  3rd Qu.:15.00   3rd Qu.: 6.000   3rd Qu.:25.00   3rd Qu.: 6.000  
##  Max.   :25.00   Max.   :12.500   Max.   :45.00   Max.   :12.500  
##                                                                   
##      avgprc           totqty         lavgprc           ltotqty     
##  Min.   :0.2903   Min.   :  170   Min.   :-1.2370   Min.   :5.136  
##  1st Qu.:0.5969   1st Qu.: 1995   1st Qu.:-0.5160   1st Qu.:7.598  
##  Median :0.7581   Median : 3460   Median :-0.2769   Median :8.149  
##  Mean   :0.8474   Mean   : 4127   Mean   :-0.2457   Mean   :8.086  
##  3rd Qu.:1.1173   3rd Qu.: 5975   3rd Qu.: 0.1109   3rd Qu.:8.695  
##  Max.   :1.7755   Max.   :10940   Max.   : 0.5741   Max.   :9.300  
##                                                                    
##        t         lavgp_1            gavgprc             gavgp_1         
##  Min.   : 1   Min.   :-1.23695   Min.   :-0.764269   Min.   :-0.764269  
##  1st Qu.:25   1st Qu.:-0.51703   1st Qu.:-0.156143   1st Qu.:-0.158231  
##  Median :49   Median :-0.28282   Median :-0.036514   Median :-0.037729  
##  Mean   :49   Mean   :-0.25393   Mean   : 0.009395   Mean   : 0.006099  
##  3rd Qu.:73   3rd Qu.: 0.09114   3rd Qu.: 0.160748   3rd Qu.: 0.155324  
##  Max.   :97   Max.   : 0.57407   Max.   : 0.756772   Max.   : 0.756772  
##               NA's   :1          NA's   :1           NA's   :2

veri setimizin özeti için summary komutunu kullandık.

fish %>% 
  group_by(mon) %>% 
  summarise("pazartesine göre ortalama rüzgar hızı(speed2)" = mean(speed2))

## # A tibble: 2 × 2
##     mon `pazartesine göre ortalama rüzgar hızı(speed2)`
##   <int>                                           <dbl>
## 1     0                                            11.5
## 2     1                                            14.2

group_by komutu ve mean(ortalama alma) komutu sayesinde pazartesi gün kü yaşanan rüzgar hızlarının ortalamasını gösterebiliriz.

lm formülü ile regresyon oluşturma

summary(lm( t ~ prca + prcw + qtya + qtyw + mon+ tues + wed + thurs + speed2 + wave2 , data = fish  ))

## 
## Call:
## lm(formula = t ~ prca + prcw + qtya + qtyw + mon + tues + wed + 
##     thurs + speed2 + wave2, data = fish)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.204 -19.902   1.271  18.319  72.154 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.871e+01  1.624e+01   5.464 4.46e-07 ***
## prca        -9.638e-01  2.504e+01  -0.038    0.969    
## prcw        -1.866e+01  2.700e+01  -0.691    0.491    
## qtya        -9.331e-04  1.779e-03  -0.524    0.601    
## qtyw        -6.673e-04  3.044e-03  -0.219    0.827    
## mon         -4.905e+00  1.021e+01  -0.480    0.632    
## tues        -3.156e+00  9.389e+00  -0.336    0.738    
## wed         -3.777e+00  9.411e+00  -0.401    0.689    
## thurs       -1.425e+00  9.526e+00  -0.150    0.881    
## speed2      -2.749e-01  9.305e-01  -0.295    0.768    
## wave2       -2.540e+00  2.264e+00  -1.122    0.265    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27.83 on 86 degrees of freedom
## Multiple R-squared:  0.1241, Adjusted R-squared:  0.02228 
## F-statistic: 1.219 on 10 and 86 DF,  p-value: 0.2907

-İntercept li olarak yaptığımız regresyon

summary(lm( t ~ prca + prcw + qtya + qtyw + mon+ tues + wed + thurs + speed2 + speed3 + wave2 + wave3 -1 , data = fish  ))

## 
## Call:
## lm(formula = t ~ prca + prcw + qtya + qtyw + mon + tues + wed + 
##     thurs + speed2 + speed3 + wave2 + wave3 - 1, data = fish)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -57.32 -21.90   1.74  28.61  60.77 
## 
## Coefficients:
##          Estimate Std. Error t value Pr(>|t|)  
## prca   -21.240332  29.362797  -0.723   0.4714  
## prcw    26.473943  31.873983   0.831   0.4085  
## qtya     0.003126   0.001850   1.690   0.0946 .
## qtyw     0.004876   0.003388   1.439   0.1538  
## mon      1.803907  11.656902   0.155   0.8774  
## tues    16.158150  10.448624   1.546   0.1257  
## wed     15.229454  10.254469   1.485   0.1412  
## thurs   10.862609  10.661546   1.019   0.3112  
## speed2   1.772317   0.987298   1.795   0.0762 .
## speed3   0.942909   0.681353   1.384   0.1700  
## wave2   -1.440232   2.626880  -0.548   0.5849  
## wave3   -3.567798   2.313492  -1.542   0.1267  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31.98 on 85 degrees of freedom
## Multiple R-squared:  0.7186, Adjusted R-squared:  0.6789 
## F-statistic: 18.09 on 12 and 85 DF,  p-value: < 2.2e-16

interceptsiz regresyonumuz.

kukla değişken ekleme

sabitdatam <- fish %>% 
mutate(sabit = 1) %>%
  mutate(degisken = ifelse(mon == 1, 0, 15))
paged_table(sabitdatam)

### filter ve select komutu kullanma

 satır <- sabitdatam %>% filter(degisken == 15)
satır

##         prca      prcw qtya qtyw mon tues wed thurs speed2 wave2 speed3 wave3
## 1  0.9722222 1.1750000 2900  566   0    0   1     0     10   5.0     20   7.5
## 2  1.2333330 1.4750000  770 1525   0    0   0     1     10   6.0     20   4.0
## 3  1.9285710 1.6250000  927  943   0    0   0     0     15   6.0     20   5.0
## 4  0.8818182 0.9142857 4230 1375   0    1   0     0     15   4.5     15   3.5
## 5  0.7687500 0.8300000 3079 1880   0    0   1     0     10   4.5     20   3.5
## 6  0.8300000 1.0000000  945 1240   0    0   0     1     10   4.0     20   4.5
## 7  0.6892857 0.7615384 7925 1395   0    0   0     0     10   4.0     15   4.5
## 8  1.6166670 1.5000000 1275  650   0    1   0     0     15   9.0     45  12.5
## 9  1.4500000 1.5111110 1834  636   0    0   1     0     15   7.5     30  12.5
## 10 1.2791671 1.3531250 4295 4522   0    0   0     1     10   4.5     25   9.0
## 11 1.2166671 1.2687500 3120 1545   0    0   0     0     10   4.5     20   7.5
## 12 0.8300000 0.9300000 3250  540   0    1   0     0     10   4.0     25   6.0
## 13 1.2785710 1.2500000 1730  300   0    0   1     0     15   8.0     15   6.0
## 14 1.0833330 1.2333330 1245 1640   0    0   0     1     15   8.0     20   4.0
## 15 1.0625000 1.1666670  540  660   0    0   0     0     15   7.5     35   8.0
## 16 1.2500000 1.2500000  450  650   0    1   0     0     15   5.5     30   4.5
## 17 0.9250000 0.9750000 1670  810   0    0   1     0     10   3.0     25   5.5
## 18 0.5958334 0.7428572 5010 1105   0    0   0     1     10   3.5     20   5.5
## 19 0.5000000 0.4300000 3785 3000   0    0   0     0     10   5.0     20   3.0
## 20 0.5000000 0.2500000 1680  100   0    1   0     0     15   4.0     20   6.0
## 21 0.5583333 0.6000000 1145  965   0    0   1     0     15   3.0     25   5.5
## 22 0.5142857 0.5590909 2120 2585   0    0   0     1     10   2.5     15   4.0
## 23 0.5833334 0.5888889 1690  960   0    0   0     0     10   2.5     15   3.0
## 24 0.3666667 0.6000000 1010   60   0    1   0     0     15   8.0     35   8.0
## 25 0.5125000 1.2500000  585  200   0    0   1     0     12   6.5     35   8.0
## 26 1.4875000 1.4500000 2300 1145   0    0   0     1     12   6.5     25   8.0
## 27 1.4321430 1.4200000 1885 4020   0    0   0     0     10   6.5     18   6.5
## 28 1.5500000 1.6333330  330  760   0    1   0     0     10   6.5     25   5.0
## 29 1.1961540 1.3500000 3040 1420   0    0   1     0     10   4.5     30   6.5
## 30 0.9500000 1.0416670 4190 1870   0    0   0     1     15   4.5     15   6.5
## 31 0.5156250 0.7333333 7420 2235   0    0   0     0     10   5.0     20   4.5
## 32 0.4083333 0.5200000 6435 1445   0    1   0     0     10   4.5     20   3.0
## 33 0.2666667 0.3500000 1430  565   0    0   1     0     10   5.0     25   4.5
## 34 0.3571429 0.4750000 1580 1500   0    0   0     1     12   5.0     15   4.5
## 35 0.4214286 0.5437500 4195 1330   0    0   0     0     12   4.0     18   5.0
## 36 0.4333333 0.5000000 1220  300   0    1   0     0     10   5.5     10   3.5
## 37 0.7928572 0.8333333 1570  305   0    0   1     0     15   6.0     15   4.0
## 38 1.1833330 1.2277780  550  875   0    0   0     1     15   8.0     25   5.5
## 39 1.3312500 1.2166671 1140 1340   0    0   0     0     15   8.0     25   6.0
## 40 1.0416670 1.0642860  410 1300   0    1   0     0     10   4.5     30   6.0
## 41 0.7730770 0.7681818 4320 2820   0    0   1     0     10   4.5     15   6.0
## 42 0.7833334 0.7818182 3440 3420   0    0   0     1      5   4.0     20   4.5
## 43 0.5857143 0.6428571 3620 1153   0    0   0     0      5   3.5     20   4.5
## 44 0.6000000 0.7071428 5940 1300   0    1   0     0     10   3.5     30   5.5
## 45 0.7722222 0.8700000 2320  540   0    0   1     0     10   5.0     15   5.5
## 46 0.7166667 0.7480000 3075 2900   0    0   0     1     20   6.5     15   3.5
## 47 0.6833333 0.6944444 1120  950   0    0   0     0     25   6.5     25   5.0
## 48 0.5083333 0.6214285 3380 2690   0    1   0     0     15   4.0     20   4.0
## 49 0.9166667 1.0000000  630  620   0    0   1     0     15   5.0     20   3.5
## 50 1.0833330 1.1562500 1500 1305   0    0   0     1     10   5.0     15   4.0
## 51 0.6000000 0.5954546 1460 3050   0    0   0     0     10   8.0     25   5.0
## 52 0.7400000 0.7750000 1420  410   0    1   0     0      5   7.0     20   3.5
## 53 0.6200000 0.6750000 1530  950   0    0   1     0     15   7.0     10   3.0
## 54 0.6117647 0.6153846 4440 4225   0    0   0     1     10   4.0     25   7.0
## 55 0.8071429 0.8416667 1530  340   0    0   0     0     10   5.0     20   7.0
## 56 0.9000000 1.3333330  360  690   0    1   0     0     10   6.5     35   8.0
## 57 0.8000000 0.8416667 3440  830   0    0   1     0     10   4.0     35   8.0
## 58 0.6312500 0.6437500 3270 5320   0    0   0     1     10   4.0     15   6.5
## 59 0.5266666 0.5911765 6190 3270   0    0   0     0     10   4.0     15   4.0
## 60 0.7500000 0.6166667  200  675   0    1   0     0     10   4.0     15   4.0
## 61 0.5066667 0.5866667 6660 4280   0    0   1     0     10   3.0     20   4.0
## 62 0.6562500 0.7230000 1400 1725   0    0   0     1     10   3.0     15   4.0
## 63 0.6450000 0.7571428 3480  990   0    0   0     0     10   3.0     15   3.0
## 64 0.6500000 0.7357143  930  680   0    1   0     0     15   4.0     25   3.0
## 65 0.6000000 0.9250000  190  180   0    0   1     0      5   4.0     25   3.5
## 66 0.7187500 0.7500000 4820 2350   0    0   0     1      5   3.0     25   4.0
## 67 0.7807692 0.8187500 4750  890   0    0   0     0     10   4.0     10   4.0
## 68 0.7475000 0.7555556 4600 1260   0    1   0     0     10   4.0     15   4.0
## 69 0.8550000 0.9363636 1620 1330   0    0   1     0     10   3.0     15   4.0
## 70 0.8533334 0.8657143 3330 2520   0    0   0     1     10   5.0     15   4.0
## 71 0.5807692 0.6692308 9120 1660   0    0   0     0     10   5.0     15   3.0
## 72 0.5333334 0.6055555 1260 1260   0    1   0     0     10   3.0     15   3.5
## 73 0.4333333 0.5166667 3420  970   0    0   1     0     10   3.0     15   3.5
## 74 0.3800000 0.4750000 4890 2050   0    0   0     1     10   3.0     15   3.0
## 75 0.3088235 0.3375000 5700  900   0    0   0     0     10   3.5     15   3.0
## 76 0.8750000 0.9333333  540  690   0    1   0     0     12   4.5     25   4.0
## 77 1.2357140 1.2500000 1000  250   0    0   1     0     12   5.0     25   4.5
## 78 1.2500000 1.2500000 2500 1480   0    0   0     1     15   6.0     18   4.5
## 79 1.7300000 1.7083330 3040  740   0    0   0     0     15   9.0     20   5.0
##       avgprc totqty      lavgprc  ltotqty  t      lavgp_1      gavgprc
## 1  1.0053359   3466  0.005321742 8.150757  2 -0.356266499  0.361588240
## 2  1.3939178   2295  0.332118332 7.738488  3  0.005321742  0.326796591
## 3  1.7754868   1870  0.574074626 7.533694  4  0.332118332  0.241956294
## 4  0.8897830   5605 -0.116777636 8.631414  6 -0.190194055  0.073416419
## 5  0.7919704   4959 -0.233231217 8.508960  7 -0.116777636 -0.116453581
## 6  0.9264759   2185 -0.076367199 7.689371  8 -0.233231217  0.156864017
## 7  0.7001004   9320 -0.356531590 9.139918  9 -0.076367199 -0.280164391
## 8  1.5772730   1925  0.455697417 7.562681 11  0.400240600  0.055456817
## 9  1.4657356   2470  0.382357210 7.811974 12  0.455697417 -0.073340207
## 10 1.3170980   8817  0.275430858 9.084437 13  0.382357210 -0.106926352
## 11 1.2339164   4665  0.210193172 8.447844 14  0.275430858 -0.065237686
## 12 0.8442480   3790 -0.169308990 8.240121 16 -0.071573481 -0.097735509
## 13 1.2743487   2030  0.242435247 7.615791 17 -0.169308990  0.411744237
## 14 1.1686016   2885  0.155807853 7.967280 18  0.242435247 -0.086627394
## 15 1.1197919   1200  0.113142833 7.090077 19  0.155807853 -0.042665020
## 16 1.2500000   1100  0.223143548 7.003066 21  0.084557354  0.138586193
## 17 0.9413307   2480 -0.060460798 7.816014 22  0.223143548 -0.283604354
## 18 0.6224011   6115 -0.474170506 8.718500 23 -0.060460798 -0.413709700
## 19 0.4690494   6785 -0.757047236 8.822470 24 -0.474170506 -0.282876730
## 20 0.4859551   1780 -0.721639156 7.484369 26 -0.738400400  0.016761243
## 21 0.5773894   2110 -0.549238324 7.654443 27 -0.721639156  0.172400832
## 22 0.5389023   4705 -0.618220925 8.456381 28 -0.549238324 -0.068982601
## 23 0.5853459   2650 -0.535552263 7.882315 29 -0.618220925  0.082668662
## 24 0.3797508   1070 -0.968239963 6.975414 31 -0.432429135 -0.535810828
## 25 0.7003981    785 -0.356106400 6.665684 32 -0.968239963  0.612133563
## 26 1.4750363   3445  0.388682574 8.144679 33 -0.356106400  0.744789004
## 27 1.4238763   5905  0.353382945 8.683555 34  0.388682574 -0.035299629
## 28 1.6081038   1090  0.475055695 6.993933 36  0.327854246  0.147201449
## 29 1.2451364   4460  0.219245061 8.402905 37  0.475055695 -0.255810618
## 30 0.9782867   6060 -0.021952519 8.709465 38  0.219245061 -0.241197586
## 31 0.5660215   9655 -0.569123209 9.175231 39 -0.021952519 -0.547170699
## 32 0.4288102   7880 -0.846740782 8.972083 40 -0.569123209 -0.277617574
## 33 0.2902674   1995 -1.236952782 7.598399 41 -0.846740782 -0.390211999
## 34 0.4145408   3080 -0.880583823 8.032685 42 -1.236952782  0.356368959
## 35 0.4508743   5525 -0.796566665 8.617039 43 -0.880583823  0.084017158
## 36 0.4464912   1520 -0.806335568 7.326466 45 -0.643928111 -0.162407458
## 37 0.7994413   1875 -0.223842129 7.536364 46 -0.806335568  0.582493424
## 38 1.2106237   1425  0.191135719 7.261927 47 -0.223842129  0.414977849
## 39 1.2693383   2480  0.238495708 7.816014 48  0.191135719  0.047359988
## 40 1.0588627   1710  0.057195395 7.444249 50  0.113134742 -0.055939347
## 41 0.7711436   7140 -0.259880662 8.873468 51  0.057195395 -0.317076057
## 42 0.7825781   6860 -0.245161623 8.833463 52 -0.259880662  0.014719039
## 43 0.5995181   4773 -0.511629105 8.470731 53 -0.245161623 -0.266467482
## 44 0.6192384   7240 -0.479264975 8.887377 55 -0.825806022  0.346541047
## 45 0.7906837   2860 -0.234857202 7.958577 56 -0.479264975  0.244407773
## 46 0.7318745   5975 -0.312146187 8.695339 57 -0.234857202 -0.077288985
## 47 0.6884326   2070 -0.373337895 7.635304 58 -0.312146187 -0.061191708
## 48 0.5584529   6070 -0.582584977 8.711114 60 -0.469162077 -0.113422900
## 49 0.9580000   1250 -0.042907495 7.130899 61 -0.582584977  0.539677501
## 50 1.1172570   2805  0.110876575 7.939159 62 -0.042907495  0.153784066
## 51 0.5969260   4510 -0.515962064 8.414052 63  0.110876575 -0.626838624
## 52 0.7478415   1830 -0.290564179 7.512071 65 -0.081437975 -0.209126204
## 53 0.6410686   2480 -0.444618851 7.816014 66 -0.290564179 -0.154054672
## 54 0.6135297   8665 -0.488526523 9.067047 67 -0.444618851 -0.043907672
## 55 0.8134199   1870 -0.206507772 7.533694 68 -0.488526523  0.282018751
## 56 1.1847616   1050  0.169541612 6.956545 70  0.163877770  0.005663842
## 57 0.8080992   4270 -0.213070512 8.359369 71  0.169541612 -0.382612109
## 58 0.6389916   8590 -0.447863966 9.058354 72 -0.213070512 -0.234793454
## 59 0.5489655   9460 -0.599719763 9.154828 73 -0.447863966 -0.151855797
## 60 0.6471429    875 -0.435188174 6.774224 75 -0.391042024 -0.044146150
## 61 0.5379647  10940 -0.619962335 9.300181 76 -0.435188174 -0.184774160
## 62 0.6930960   3125 -0.366586775 8.047190 77 -0.619962335  0.253375560
## 63 0.6698370   4470 -0.400720894 8.405144 78 -0.366586775 -0.034134120
## 64 0.6862023   1610 -0.376582801 7.383989 80 -0.327797621 -0.048785180
## 65 0.7581081    370 -0.276929229 5.913503 81 -0.376582801  0.099653572
## 66 0.7289923   7170 -0.316092044 8.877661 82 -0.276929229 -0.039162815
## 67 0.7867627   5640 -0.239828661 8.637639 83 -0.316092044  0.076263383
## 68 0.7492321   5860 -0.288706452 8.675905 84 -0.239828661 -0.048877791
## 69 0.8916826   2950 -0.114645079 7.989561 85 -0.288706452  0.174061373
## 70 0.8586667   5850 -0.152374417 8.674197 86 -0.114645079 -0.037729338
## 71 0.5943913  10780 -0.520217419 9.285448 87 -0.152374417 -0.367843002
## 72 0.5694444   2520 -0.563094079 7.832014 89 -0.459587485 -0.103506595
## 73 0.4517464   4390 -0.794634402 8.387085 90 -0.563094079 -0.231540322
## 74 0.4080620   6940 -0.896336257 8.845057 91 -0.794634402 -0.101701856
## 75 0.3127339   6600 -1.162402511 8.794825 92 -0.896336257 -0.266066253
## 76 0.9077235   1230 -0.096815415 7.114769 94 -0.845347285  0.748531878
## 77 1.2385712   1250  0.213958427 7.130899 95 -0.096815415  0.310773849
## 78 1.2500000   3980  0.223143548 8.289037 96  0.213958427  0.009185120
## 79 1.7257583   3780  0.545666575 8.237479 97  0.223143548  0.322523028
##         gavgp_1 sabit degisken
## 1            NA     1       15
## 2   0.361588240     1       15
## 3   0.326796591     1       15
## 4  -0.764268696     1       15
## 5   0.073416419     1       15
## 6  -0.116453581     1       15
## 7   0.156864017     1       15
## 8   0.756772161     1       15
## 9   0.055456817     1       15
## 10 -0.073340207     1       15
## 11 -0.106926352     1       15
## 12 -0.281766653     1       15
## 13 -0.097735509     1       15
## 14  0.411744237     1       15
## 15 -0.086627394     1       15
## 16 -0.028585479     1       15
## 17  0.138586193     1       15
## 18 -0.283604354     1       15
## 19 -0.413709700     1       15
## 20  0.018646836     1       15
## 21  0.016761243     1       15
## 22  0.172400832     1       15
## 23 -0.068982601     1       15
## 24  0.103123128     1       15
## 25 -0.535810828     1       15
## 26  0.612133563     1       15
## 27  0.744789004     1       15
## 28 -0.025528699     1       15
## 29  0.147201449     1       15
## 30 -0.255810618     1       15
## 31 -0.241197586     1       15
## 32 -0.547170699     1       15
## 33 -0.277617574     1       15
## 34 -0.390211999     1       15
## 35  0.356368959     1       15
## 36  0.152638555     1       15
## 37 -0.162407458     1       15
## 38  0.582493424     1       15
## 39  0.414977849     1       15
## 40 -0.125360966     1       15
## 41 -0.055939347     1       15
## 42 -0.317076057     1       15
## 43  0.014719039     1       15
## 44 -0.314176917     1       15
## 45  0.346541047     1       15
## 46  0.244407773     1       15
## 47 -0.077288985     1       15
## 48 -0.095824182     1       15
## 49 -0.113422900     1       15
## 50  0.539677501     1       15
## 51  0.153784066     1       15
## 52  0.434524089     1       15
## 53 -0.209126204     1       15
## 54 -0.154054672     1       15
## 55 -0.043907672     1       15
## 56  0.370385528     1       15
## 57  0.005663842     1       15
## 58 -0.382612109     1       15
## 59 -0.234793454     1       15
## 60  0.208677739     1       15
## 61 -0.044146150     1       15
## 62 -0.184774160     1       15
## 63  0.253375560     1       15
## 64  0.072923273     1       15
## 65 -0.048785180     1       15
## 66  0.099653572     1       15
## 67 -0.039162815     1       15
## 68  0.076263383     1       15
## 69 -0.048877791     1       15
## 70  0.174061373     1       15
## 71 -0.037729338     1       15
## 72  0.060629934     1       15
## 73 -0.103506595     1       15
## 74 -0.231540322     1       15
## 75 -0.101701856     1       15
## 76  0.317055225     1       15
## 77  0.748531878     1       15
## 78  0.310773849     1       15
## 79  0.009185120     1       15

filter komutu seçtiğimiz değikenin satırlarını göstermek için kullandığımız komuttur.

sutun <- sabitdatam %>% select(sabit, totqty, wed)
sutun

##    sabit totqty wed
## 1      1   4080   0
## 2      1   3466   1
## 3      1   2295   0
## 4      1   1870   0
## 5      1   6885   0
## 6      1   5605   0
## 7      1   4959   1
## 8      1   2185   0
## 9      1   9320   0
## 10     1   6793   0
## 11     1   1925   0
## 12     1   2470   1
## 13     1   8817   0
## 14     1   4665   0
## 15     1   7240   0
## 16     1   3790   0
## 17     1   2030   1
## 18     1   2885   0
## 19     1   1200   0
## 20     1    170   0
## 21     1   1100   0
## 22     1   2480   1
## 23     1   6115   0
## 24     1   6785   0
## 25     1   2780   0
## 26     1   1780   0
## 27     1   2110   1
## 28     1   4705   0
## 29     1   2650   0
## 30     1   6845   0
## 31     1   1070   0
## 32     1    785   1
## 33     1   3445   0
## 34     1   5905   0
## 35     1   3460   0
## 36     1   1090   0
## 37     1   4460   1
## 38     1   6060   0
## 39     1   9655   0
## 40     1   7880   0
## 41     1   1995   1
## 42     1   3080   0
## 43     1   5525   0
## 44     1   2220   0
## 45     1   1520   0
## 46     1   1875   1
## 47     1   1425   0
## 48     1   2480   0
## 49     1   2975   0
## 50     1   1710   0
## 51     1   7140   1
## 52     1   6860   0
## 53     1   4773   0
## 54     1   9600   0
## 55     1   7240   0
## 56     1   2860   1
## 57     1   5975   0
## 58     1   2070   0
## 59     1   4540   0
## 60     1   6070   0
## 61     1   1250   1
## 62     1   2805   0
## 63     1   4510   0
## 64     1   1285   0
## 65     1   1830   0
## 66     1   2480   1
## 67     1   8665   0
## 68     1   1870   0
## 69     1   1140   0
## 70     1   1050   0
## 71     1   4270   1
## 72     1   8590   0
## 73     1   9460   0
## 74     1   4032   0
## 75     1    875   0
## 76     1  10940   1
## 77     1   3125   0
## 78     1   4470   0
## 79     1   4520   0
## 80     1   1610   0
## 81     1    370   1
## 82     1   7170   0
## 83     1   5640   0
## 84     1   5860   0
## 85     1   2950   1
## 86     1   5850   0
## 87     1  10780   0
## 88     1   3060   0
## 89     1   2520   0
## 90     1   4390   1
## 91     1   6940   0
## 92     1   6600   0
## 93     1   3450   0
## 94     1   1230   0
## 95     1   1250   1
## 96     1   3980   0
## 97     1   3780   0

select komutu veri setimizde yer alan sadece sectiğimiz sütunları göstememizi sağlayan komuttur.

sınav odevi

zamira berdiyeva

2022-11-19

Sınav öncesi ödevi

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