Eksplorasi Data big9salary

Eksplorasi Data

Eksplorasi data big9salary dari wooldridge

library(wooldridge)
data("big9salary")
str(big9salary)

'data.frame':   786 obs. of  30 variables:
 $ id       : int  101 101 101 102 102 102 103 103 103 104 ...
 $ year     : int  92 95 99 92 95 99 92 95 99 92 ...
 $ salary   : int  NA NA 107100 79420 88239 100450 87450 96831 108290 NA ...
 $ pubindx  : num  30.5 31 40.5 33.5 33.9 ...
 $ totpge   : num  92.7 107.2 186.5 127.5 133 ...
 $ assist   : int  0 0 0 0 0 0 0 0 0 1 ...
 $ assoc    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ prof     : int  1 1 1 1 1 1 1 1 1 0 ...
 $ chair    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ top20phd : int  0 0 0 0 0 0 1 1 1 1 ...
 $ yearphd  : int  73 73 73 76 76 76 61 61 61 91 ...
 $ female   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ osu      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ iowa     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ indiana  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ purdue   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ msu      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ minn     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ mich     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ wisc     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ illinois : int  0 0 0 0 0 0 0 0 0 0 ...
 $ y92      : int  1 0 0 1 0 0 1 0 0 1 ...
 $ y95      : int  0 1 0 0 1 0 0 1 0 0 ...
 $ y99      : int  0 0 1 0 0 1 0 0 1 0 ...
 $ lsalary  : num  NA NA 11.6 11.3 11.4 ...
 $ exper    : int  19 22 26 16 19 23 31 34 38 1 ...
 $ expersq  : int  361 484 676 256 361 529 961 1156 1444 1 ...
 $ pubindxsq: num  933 959 1636 1125 1149 ...
 $ pubindx0 : num  0 0 0 0 0 0 0 0 0 0 ...
 $ lpubindx : num  3.42 3.43 3.7 3.51 3.52 ...
 - attr(*, "time.stamp")= chr "22 Jan 2013 14:09"

perintah str berfungsi untuk menampilkan nama variabel,tipe data tiap variabel dan nilainya, dimana terdapat 30 variabel dengan 786 data

head(big9salary)

   id year salary pubindx  totpge assist assoc prof chair top20phd yearphd
1 101   92     NA   30.54  92.667      0     0    1     0        0      73
2 101   95     NA   30.96 107.170      0     0    1     0        0      73
3 101   99 107100   40.45 186.500      0     0    1     0        0      73
4 102   92  79420   33.54 127.500      0     0    1     0        0      76
5 102   95  88239   33.89 133.000      0     0    1     0        0      76
6 102   99 100450   36.23 191.500      0     0    1     0        0      76
  female osu iowa indiana purdue msu minn mich wisc illinois y92 y95 y99
1      0   0    0       1      0   0    0    0    0        0   1   0   0
2      0   0    0       1      0   0    0    0    0        0   0   1   0
3      0   0    0       1      0   0    0    0    0        0   0   0   1
4      0   0    0       1      0   0    0    0    0        0   1   0   0
5      0   0    0       1      0   0    0    0    0        0   0   1   0
6      0   0    0       1      0   0    0    0    0        0   0   0   1
   lsalary exper expersq pubindxsq pubindx0 lpubindx
1       NA    19     361  932.6917        0 3.419037
2       NA    22     484  958.5215        0 3.432696
3 11.58152    26     676 1636.2025        0 3.700067
4 11.28251    16     256 1124.9316        0 3.512739
5 11.38780    19     361 1148.5321        0 3.523120
6 11.51742    23     529 1312.6129        0 3.589887

perintah head menampilkan 6 baris pertama dari data secara lengkap

summary(big9salary)

       id             year           salary          pubindx       
 Min.   :101.0   Min.   :92.00   Min.   : 18670   Min.   :  0.000  
 1st Qu.:320.0   1st Qu.:92.00   1st Qu.: 60294   1st Qu.:  5.395  
 Median :518.5   Median :95.00   Median : 78371   Median : 23.975  
 Mean   :542.8   Mean   :95.33   Mean   : 82045   Mean   : 35.352  
 3rd Qu.:804.0   3rd Qu.:99.00   3rd Qu.: 97279   3rd Qu.: 46.722  
 Max.   :933.0   Max.   :99.00   Max.   :223537   Max.   :248.900  
                                 NA's   :107                       
     totpge            assist           assoc             prof       
 Min.   :   0.00   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:  65.83   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median : 127.50   Median :0.0000   Median :0.0000   Median :1.0000  
 Mean   : 160.96   Mean   :0.1058   Mean   :0.1715   Mean   :0.6902  
 3rd Qu.: 209.25   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:1.0000  
 Max.   :1196.00   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
 NA's   :39        NA's   :162      NA's   :162      NA's   :163     
     chair            top20phd        yearphd          female       
 Min.   :0.00000   Min.   :0.000   Min.   :38.00   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:1.000   1st Qu.:70.00   1st Qu.:0.00000  
 Median :0.00000   Median :1.000   Median :77.00   Median :0.00000  
 Mean   :0.05147   Mean   :0.766   Mean   :77.05   Mean   :0.08367  
 3rd Qu.:0.00000   3rd Qu.:1.000   3rd Qu.:85.00   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000   Max.   :96.00   Max.   :1.00000  
 NA's   :106       NA's   :81      NA's   :87      NA's   :33       
      osu              iowa            indiana            purdue       
 Min.   :0.0000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.0000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.1183   Mean   :0.09542   Mean   :0.08015   Mean   :0.07634  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                       
      msu              minn             mich             wisc      
 Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.000  
 Median :0.0000   Median :0.0000   Median :0.0000   Median :0.000  
 Mean   :0.1374   Mean   :0.1031   Mean   :0.1374   Mean   :0.126  
 3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.000  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.000  
                                                                   
    illinois          y92              y95              y99        
 Min.   :0.000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.000   Median :0.0000   Median :0.0000   Median :0.0000  
 Mean   :0.126   Mean   :0.3333   Mean   :0.3333   Mean   :0.3333  
 3rd Qu.:0.000   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
 Max.   :1.000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
                                                                   
    lsalary           exper          expersq         pubindxsq       
 Min.   : 9.835   Min.   : 0.00   Min.   :   0.0   Min.   :    0.00  
 1st Qu.:11.007   1st Qu.:10.00   1st Qu.: 100.0   1st Qu.:   29.12  
 Median :11.269   Median :18.00   Median : 324.0   Median :  574.81  
 Mean   :11.259   Mean   :18.33   Mean   : 451.9   Mean   : 2762.36  
 3rd Qu.:11.485   3rd Qu.:25.00   3rd Qu.: 625.0   3rd Qu.: 2183.02  
 Max.   :12.317   Max.   :61.00   Max.   :3721.0   Max.   :61951.21  
 NA's   :107      NA's   :87      NA's   :87                         
    pubindx0         lpubindx     
 Min.   :0.0000   Min.   :-1.966  
 1st Qu.:0.0000   1st Qu.: 2.389  
 Median :0.0000   Median : 3.315  
 Mean   :0.1043   Mean   : 3.030  
 3rd Qu.:0.0000   3rd Qu.: 3.971  
 Max.   :1.0000   Max.   : 5.517  
                  NA's   :82      

perintah summary menampilkan rangkuman nilai minimal, kuartil pertama, median/kuartil kedua, rata-rata, kuartil ketiga dan nilai max dari tiap variabel

Distribusi_Salary <- table(big9salary$salary)
Distribusi_Salary

 18670  21051  22254  29337  29462  33128  33155  33642  36050  38200  40500 
     1      1      1      1      1      1      1      1      1      1      1 
 41490  41600  41618  42648  43207  44000  44100  44550  44771  44850  45000 
     1      1      1      1      1      2      1      1      1      1      1 
 45358  45755  45950  46000  46200  46300  47000  47250  47300  47333  47500 
     1      1      1      1      1      1      2      1      1      1      1 
 47575  47667  47810  47831  47892  48000  48200  48293  48345  48500  48905 
     1      1      1      1      1      1      1      1      1      1      1 
 48950  49156  49360  49553  50200  50209  50367  50500  50605  50700  51000 
     1      1      1      1      1      1      1      1      1      1      1 
 51083  51127  51138  51232  51250  51400  51408  51500  51516  51575  51640 
     1      1      1      1      1      1      1      1      1      1      1 
 51846  51921  52000  52224  52250  52320  52400  52460  52533  52650  52675 
     1      1      9      1      1      1      1      1      1      1      1 
 52883  52900  53000  53104  53232  53268  53400  53500  53624  53740  53875 
     1      2      1      1      1      1      1      1      1      1      1 
 53958  54000  54100  54225  54283  54336  54400  54585  54750  55000  55050 
     1      2      1      1      1      1      2      2      1      1      1 
 55172  55300  55368  55562  55650  55715  55900  55949  56040  56304  56330 
     1      1      1      1      1      1      1      1      1      1      1 
 56352  56435  56463  56700  56717  56785  56818  57000  57200  57253  57500 
     1      1      1      1      1      1      1      2      1      1      1 
 57510  57511  57553  57570  57642  57800  57867  57882  57933  58000  58244 
     1      1      1      1      1      1      1      1      2      1      1 
 58294  58296  58404  58710  59136  59200  59280  59287  59300  59305  59360 
     1      1      1      1      1      1      1      1      1      1      1 
 59376  59400  59700  59772  59925  59950  60000  60012  60100  60215  60288 
     1      1      1      1      1      1      1      1      1      1      1 
 60300  60400  60401  60450  60467  60600  60683  60690  60850  61000  61200 
     1      1      1      1      1      1      1      1      1      1      2 
 61400  61487  61500  61545  61650  61652  61680  62000  62250  62350  62448 
     1      1      1      1      1      1      1      1      1      1      1 
 62640  62668  62825  63000  63204  63250  63253  63350  63500  63610  63612 
     1      1      1      1      1      1      1      1      1      1      1 
 63819  63900  63961  64000  64152  64292  64400  64900  64957  65000  65009 
     1      1      1      1      1      1      1      1      1      2      1 
 65331  65472  65500  65600  65700  65950  66000  66093  66400  66412  66491 
     1      1      2      1      1      1      1      1      1      1      1 
 66512  66594  66775  66936  67000  67092  67296  67400  67529  67600  67604 
     1      1      1      1      2      1      1      1      1      2      1 
 67800  67915  67950  68000  68180  68230  68440  69105  69204  69300  69420 
     1      1      1      1      1      2      1      1      1      1      1 
 69612  69630  69700  69950  70042  70290  70365  70430  70500  70700  70740 
     1      1      1      1      1      1      1      1      1      1      1 
 70800  71000  71139  71170  71240  71352  71576  71600  71732  71754  71800 
     1      1      1      1      1      1      1      1      1      1      1 
 72192  72240  72400  72570  72724  73000  73182  73200  73262  73300  73500 
     2      1      1      1      1      1      1      1      1      1      1 
 73557  73618  73700  73704  73884  73896  73939  73993  74000  74107  74125 
     1      1      1      1      1      1      1      1      2      1      1 
 74200  74238  74250  74364  74603  74659  74697  75000  75300  75700  75858 
     1      1      1      1      1      1      1      6      2      1      1 
 75883  76000  76019  76346  76350  76400  76641  76800  77000  77150  77170 
     1      2      1      1      1      1      1      1      3      1      1 
 77300  77345  77510  77749  77875  78000  78085  78270  78310  78371  78400 
     1      1      1      1      1      1      1      1      1      1      1 
 78420  78492  78500  78600  78751  78895  78950  79000  79056  79110  79150 
     1      1      1      1      1      1      1      1      1      1      1 
 79420  79800  79891  80000  80086  80100  80508  80575  80628  80799  80850 
     1      3      1      1      1      1      1      1      1      1      1 
 80992  81000  81050  81250  81310  81450  81500  81650  81939  81960  81972 
     1      4      1      1      1      1      1      1      1      1      1 
 82000  82100  82400  82411  82476  82500  82600  82764  83241  83553  83604 
     3      1      1      1      1      1      1      1      1      1      1 
 83736  83845  84000  84700  84707  84750  85000  85004  85067  85225  85300 
     1      1      2      1      1      1      2      1      1      1      1 
 85500  85700  85816  85834  85875  85964  86115  86122  86142  86155  86250 
     1      1      1      1      1      1      1      1      2      1      1 
 86400  86600  86747  86940  86975  87200  87300  87450  87500  87560  87700 
     1      1      1      1      1      1      1      1      1      1      3 
 87733  87800  87900  87948  87995  88000  88063  88133  88239  88470  88572 
     1      1      1      1      1      1      1      1      1      1      1 
 88700  89000  89200  89300  89500  89639  90000  90085  90160  90200  90769 
     1      1      1      1      1      1      3      1      1      1      1 
 90856  90940  91000  91032  91056  91155  91443  91500  91900  91937  92083 
     1      1      3      1      1      1      1      1      1      1      1 
 92160  92376  92417  92500  93000  93040  93100  93200  93204  93708  94000 
     1      1      1      2      1      1      1      2      1      1      1 
 94200  94300  94401  94450  94500  94700  94764  94814  94915  95000  95206 
     1      1      2      1      1      1      1      1      1      1      1 
 95700  95992  96000  96300  96400  96470  96817  96831  96876  97000  97020 
     1      1      3      1      1      1      1      1      1      1      1 
 97037  97150  97200  97258  97300  97563  97600  97892  98063  98064  98124 
     1      1      1      1      1      1      1      1      1      1      1 
 98223  98908  98930  99008  99308  99500  99545  99625  99990 100000 100450 
     1      1      1      1      1      1      1      1      1      4      1 
100500 100900 100916 101044 101075 101386 101985 102000 102030 102072 102149 
     2      1      1      1      1      1      1      1      1      1      1 
102280 102345 102495 102500 102699 103200 103328 103800 104089 104092 104120 
     1      1      1      1      1      1      1      1      1      1      1 
104444 104613 105000 105800 106020 106542 106730 107026 107100 107360 107400 
     1      1      1      1      1      1      1      1      1      1      1 
107568 107890 107900 108000 108060 108200 108290 108500 108600 109000 109200 
     1      1      1      3      1      1      1      1      1      2      1 
109700 110000 110068 110090 110429 110552 110712 110736 111060 111300 111500 
     1      2      1      1      1      1      1      1      1      1      1 
111655 112162 112219 112500 113000 113307 113500 114192 114325 114650 115700 
     1      1      1      1      1      1      1      1      1      1      1 
115775 116000 116016 116725 117060 117314 117629 118134 118183 118310 118500 
     1      1      1      1      1      1      1      1      1      1      1 
118728 118800 118962 119165 119400 119700 120300 120733 121053 121430 121733 
     1      1      1      1      1      1      1      1      1      1      1 
121900 122388 122397 122700 123099 123600 124225 124483 125000 126568 126875 
     1      1      1      1      1      2      1      1      2      1      1 
127211 128400 128500 129295 129372 129550 130000 130140 130379 131166 132229 
     1      1      1      1      1      1      1      1      1      1      1 
132782 133750 134400 134532 134544 135000 135409 136428 139000 139660 140178 
     1      1      1      1      1      1      1      1      1      1      1 
143674 147852 150300 157008 157092 158140 158450 160296 160314 161000 161566 
     1      1      1      1      1      1      1      1      1      1      1 
164265 164320 169021 177504 181104 184752 189631 190412 191000 223537 
     1      1      1      1      1      1      1      1      1      1 

menampilkan besar salary dan frekuensinya

barplot(Distribusi_Salary, 
        main = "Distribusi Frekuensi Salary",
        xlab = "Salary",
        ylab = "Frekuensi",
        col = "green")

distribusi frekuensi salary dengan barplot

dt1 <- table(big9salary$salary, big9salary$year)
dt1

        
         92 95 99
  18670   1  0  0
  21051   0  1  0
  22254   0  1  0
  29337   0  1  0
  29462   1  0  0
  33128   0  0  1
  33155   0  1  0
  33642   0  0  1
  36050   0  1  0
  38200   1  0  0
  40500   0  1  0
  41490   0  0  1
  41600   1  0  0
  41618   0  0  1
  42648   0  1  0
  43207   1  0  0
  44000   2  0  0
  44100   1  0  0
  44550   1  0  0
  44771   1  0  0
  44850   1  0  0
  45000   1  0  0
  45358   1  0  0
  45755   1  0  0
  45950   1  0  0
  46000   1  0  0
  46200   1  0  0
  46300   1  0  0
  47000   1  1  0
  47250   1  0  0
  47300   0  1  0
  47333   0  1  0
  47500   1  0  0
  47575   1  0  0
  47667   0  1  0
  47810   1  0  0
  47831   0  1  0
  47892   1  0  0
  48000   1  0  0
  48200   1  0  0
  48293   0  0  1
  48345   0  1  0
  48500   1  0  0
  48905   1  0  0
  48950   1  0  0
  49156   0  0  1
  49360   0  1  0
  49553   1  0  0
  50200   0  1  0
  50209   0  0  1
  50367   0  1  0
  50500   1  0  0
  50605   1  0  0
  50700   0  1  0
  51000   1  0  0
  51083   0  1  0
  51127   0  0  1
  51138   0  1  0
  51232   0  1  0
  51250   1  0  0
  51400   0  0  1
  51408   1  0  0
  51500   1  0  0
  51516   0  1  0
  51575   1  0  0
  51640   0  0  1
  51846   0  1  0
  51921   0  1  0
  52000   4  5  0
  52224   1  0  0
  52250   1  0  0
  52320   1  0  0
  52400   1  0  0
  52460   1  0  0
  52533   1  0  0
  52650   0  1  0
  52675   0  1  0
  52883   1  0  0
  52900   1  0  1
  53000   0  1  0
  53104   1  0  0
  53232   0  1  0
  53268   0  1  0
  53400   0  1  0
  53500   0  1  0
  53624   0  1  0
  53740   1  0  0
  53875   1  0  0
  53958   0  1  0
  54000   0  2  0
  54100   0  1  0
  54225   0  1  0
  54283   0  0  1
  54336   0  0  1
  54400   2  0  0
  54585   2  0  0
  54750   1  0  0
  55000   1  0  0
  55050   1  0  0
  55172   0  0  1
  55300   0  1  0
  55368   0  1  0
  55562   0  0  1
  55650   0  0  1
  55715   1  0  0
  55900   0  1  0
  55949   0  1  0
  56040   1  0  0
  56304   0  1  0
  56330   1  0  0
  56352   1  0  0
  56435   0  1  0
  56463   1  0  0
  56700   0  1  0
  56717   0  1  0
  56785   1  0  0
  56818   0  0  1
  57000   1  0  1
  57200   1  0  0
  57253   0  0  1
  57500   1  0  0
  57510   0  1  0
  57511   0  1  0
  57553   0  0  1
  57570   0  0  1
  57642   0  1  0
  57800   0  1  0
  57867   0  1  0
  57882   0  1  0
  57933   0  2  0
  58000   1  0  0
  58244   0  0  1
  58294   0  0  1
  58296   0  0  1
  58404   0  0  1
  58710   0  0  1
  59136   0  0  1
  59200   1  0  0
  59280   0  0  1
  59287   1  0  0
  59300   1  0  0
  59305   1  0  0
  59360   0  1  0
  59376   1  0  0
  59400   1  0  0
  59700   1  0  0
  59772   0  0  1
  59925   1  0  0
  59950   0  1  0
  60000   1  0  0
  60012   0  1  0
  60100   0  1  0
  60215   0  0  1
  60288   0  0  1
  60300   0  0  1
  60400   1  0  0
  60401   0  0  1
  60450   0  0  1
  60467   0  1  0
  60600   0  1  0
  60683   0  1  0
  60690   0  0  1
  60850   0  1  0
  61000   1  0  0
  61200   0  1  1
  61400   1  0  0
  61487   0  1  0
  61500   1  0  0
  61545   0  1  0
  61650   0  0  1
  61652   0  1  0
  61680   0  1  0
  62000   1  0  0
  62250   1  0  0
  62350   0  1  0
  62448   0  1  0
  62640   0  0  1
  62668   0  1  0
  62825   0  1  0
  63000   1  0  0
  63204   0  0  1
  63250   0  1  0
  63253   0  1  0
  63350   0  1  0
  63500   1  0  0
  63610   0  0  1
  63612   0  0  1
  63819   0  1  0
  63900   0  0  1
  63961   0  0  1
  64000   1  0  0
  64152   1  0  0
  64292   0  1  0
  64400   1  0  0
  64900   0  0  1
  64957   0  1  0
  65000   1  0  1
  65009   1  0  0
  65331   1  0  0
  65472   0  1  0
  65500   0  1  1
  65600   1  0  0
  65700   0  1  0
  65950   0  0  1
  66000   0  0  1
  66093   0  1  0
  66400   1  0  0
  66412   0  1  0
  66491   0  0  1
  66512   0  0  1
  66594   0  0  1
  66775   0  1  0
  66936   1  0  0
  67000   1  0  1
  67092   1  0  0
  67296   0  1  0
  67400   1  0  0
  67529   0  0  1
  67600   1  0  1
  67604   0  1  0
  67800   0  1  0
  67915   0  1  0
  67950   0  1  0
  68000   0  0  1
  68180   0  0  1
  68230   2  0  0
  68440   0  0  1
  69105   0  1  0
  69204   1  0  0
  69300   0  1  0
  69420   1  0  0
  69612   0  1  0
  69630   0  0  1
  69700   0  1  0
  69950   0  1  0
  70042   1  0  0
  70290   1  0  0
  70365   1  0  0
  70430   0  1  0
  70500   1  0  0
  70700   1  0  0
  70740   0  1  0
  70800   0  1  0
  71000   1  0  0
  71139   0  0  1
  71170   0  0  1
  71240   1  0  0
  71352   0  1  0
  71576   0  0  1
  71600   0  1  0
  71732   0  0  1
  71754   0  0  1
  71800   1  0  0
  72192   2  0  0
  72240   1  0  0
  72400   1  0  0
  72570   0  0  1
  72724   0  1  0
  73000   1  0  0
  73182   0  0  1
  73200   0  1  0
  73262   0  0  1
  73300   0  1  0
  73500   1  0  0
  73557   0  0  1
  73618   0  0  1
  73700   0  1  0
  73704   0  0  1
  73884   0  0  1
  73896   0  1  0
  73939   1  0  0
  73993   0  0  1
  74000   1  0  1
  74107   1  0  0
  74125   0  0  1
  74200   1  0  0
  74238   1  0  0
  74250   1  0  0
  74364   0  0  1
  74603   0  0  1
  74659   0  0  1
  74697   0  1  0
  75000   2  3  1
  75300   2  0  0
  75700   0  0  1
  75858   0  1  0
  75883   1  0  0
  76000   1  0  1
  76019   1  0  0
  76346   0  1  0
  76350   0  1  0
  76400   1  0  0
  76641   0  1  0
  76800   1  0  0
  77000   1  1  1
  77150   1  0  0
  77170   1  0  0
  77300   1  0  0
  77345   0  0  1
  77510   0  0  1
  77749   1  0  0
  77875   0  0  1
  78000   0  1  0
  78085   0  1  0
  78270   1  0  0
  78310   1  0  0
  78371   0  1  0
  78400   0  1  0
  78420   0  1  0
  78492   1  0  0
  78500   1  0  0
  78600   1  0  0
  78751   0  1  0
  78895   1  0  0
  78950   1  0  0
  79000   1  0  0
  79056   0  1  0
  79110   1  0  0
  79150   1  0  0
  79420   1  0  0
  79800   2  1  0
  79891   0  0  1
  80000   1  0  0
  80086   0  1  0
  80100   0  1  0
  80508   0  0  1
  80575   1  0  0
  80628   1  0  0
  80799   0  0  1
  80850   0  0  1
  80992   0  1  0
  81000   3  0  1
  81050   0  1  0
  81250   0  0  1
  81310   0  0  1
  81450   0  1  0
  81500   0  1  0
  81650   1  0  0
  81939   0  1  0
  81960   0  1  0
  81972   0  0  1
  82000   0  2  1
  82100   1  0  0
  82400   0  1  0
  82411   0  1  0
  82476   0  1  0
  82500   1  0  0
  82600   0  0  1
  82764   1  0  0
  83241   1  0  0
  83553   0  1  0
  83604   1  0  0
  83736   0  0  1
  83845   1  0  0
  84000   1  1  0
  84700   1  0  0
  84707   1  0  0
  84750   0  0  1
  85000   1  0  1
  85004   0  1  0
  85067   0  1  0
  85225   0  1  0
  85300   0  1  0
  85500   0  0  1
  85700   0  1  0
  85816   1  0  0
  85834   1  0  0
  85875   0  1  0
  85964   0  1  0
  86115   0  0  1
  86122   1  0  0
  86142   0  2  0
  86155   0  0  1
  86250   1  0  0
  86400   0  0  1
  86600   0  1  0
  86747   0  1  0
  86940   0  1  0
  86975   1  0  0
  87200   0  0  1
  87300   0  1  0
  87450   1  0  0
  87500   0  1  0
  87560   0  1  0
  87700   1  2  0
  87733   0  1  0
  87800   0  0  1
  87900   0  1  0
  87948   0  0  1
  87995   1  0  0
  88000   0  1  0
  88063   0  0  1
  88133   0  1  0
  88239   0  1  0
  88470   0  1  0
  88572   0  1  0
  88700   0  0  1
  89000   0  1  0
  89200   0  1  0
  89300   1  0  0
  89500   0  0  1
  89639   1  0  0
  90000   1  1  1
  90085   0  1  0
  90160   0  1  0
  90200   0  1  0
  90769   0  0  1
  90856   1  0  0
  90940   1  0  0
  91000   1  1  1
  91032   0  1  0
  91056   0  0  1
  91155   0  1  0
  91443   0  0  1
  91500   0  0  1
  91900   0  0  1
  91937   0  1  0
  92083   0  1  0
  92160   0  0  1
  92376   1  0  0
  92417   0  1  0
  92500   1  0  1
  93000   1  0  0
  93040   0  0  1
  93100   0  0  1
  93200   0  1  1
  93204   0  0  1
  93708   0  1  0
  94000   1  0  0
  94200   1  0  0
  94300   0  0  1
  94401   0  0  2
  94450   1  0  0
  94500   0  1  0
  94700   0  1  0
  94764   0  1  0
  94814   0  1  0
  94915   0  0  1
  95000   0  0  1
  95206   0  0  1
  95700   0  0  1
  95992   0  0  1
  96000   0  2  1
  96300   0  1  0
  96400   0  0  1
  96470   0  0  1
  96817   0  0  1
  96831   0  1  0
  96876   0  1  0
  97000   0  1  0
  97020   0  1  0
  97037   0  0  1
  97150   0  1  0
  97200   1  0  0
  97258   0  0  1
  97300   0  0  1
  97563   0  0  1
  97600   0  0  1
  97892   1  0  0
  98063   0  1  0
  98064   0  0  1
  98124   1  0  0
  98223   0  1  0
  98908   0  1  0
  98930   0  0  1
  99008   0  0  1
  99308   0  1  0
  99500   0  0  1
  99545   0  1  0
  99625   1  0  0
  99990   0  0  1
  100000  0  3  1
  100450  0  0  1
  100500  0  1  1
  100900  1  0  0
  100916  0  0  1
  101044  0  0  1
  101075  1  0  0
  101386  0  1  0
  101985  0  1  0
  102000  0  0  1
  102030  0  1  0
  102072  0  1  0
  102149  0  1  0
  102280  0  0  1
  102345  0  0  1
  102495  0  0  1
  102500  0  1  0
  102699  0  1  0
  103200  0  0  1
  103328  1  0  0
  103800  0  0  1
  104089  0  0  1
  104092  0  1  0
  104120  0  0  1
  104444  0  0  1
  104613  0  1  0
  105000  0  0  1
  105800  0  1  0
  106020  0  0  1
  106542  1  0  0
  106730  0  1  0
  107026  0  1  0
  107100  0  0  1
  107360  0  0  1
  107400  0  0  1
  107568  1  0  0
  107890  0  1  0
  107900  1  0  0
  108000  0  2  1
  108060  0  0  1
  108200  0  0  1
  108290  0  0  1
  108500  0  0  1
  108600  0  1  0
  109000  0  0  2
  109200  0  0  1
  109700  1  0  0
  110000  0  1  1
  110068  1  0  0
  110090  0  1  0
  110429  1  0  0
  110552  0  0  1
  110712  0  0  1
  110736  0  0  1
  111060  1  0  0
  111300  0  0  1
  111500  0  0  1
  111655  0  0  1
  112162  1  0  0
  112219  1  0  0
  112500  1  0  0
  113000  0  0  1
  113307  0  0  1
  113500  0  0  1
  114192  0  0  1
  114325  1  0  0
  114650  1  0  0
  115700  0  0  1
  115775  0  1  0
  116000  0  1  0
  116016  0  0  1
  116725  0  0  1
  117060  0  0  1
  117314  0  1  0
  117629  0  0  1
  118134  0  1  0
  118183  0  1  0
  118310  0  1  0
  118500  0  0  1
  118728  0  1  0
  118800  0  0  1
  118962  0  1  0
  119165  0  1  0
  119400  0  0  1
  119700  0  0  1
  120300  0  1  0
  120733  0  0  1
  121053  0  1  0
  121430  0  0  1
  121733  0  1  0
  121900  1  0  0
  122388  0  1  0
  122397  0  0  1
  122700  0  0  1
  123099  0  0  1
  123600  0  1  1
  124225  0  0  1
  124483  0  1  0
  125000  1  0  1
  126568  0  0  1
  126875  0  0  1
  127211  0  1  0
  128400  0  0  1
  128500  0  0  1
  129295  0  0  1
  129372  0  1  0
  129550  0  0  1
  130000  0  0  1
  130140  1  0  0
  130379  0  0  1
  131166  1  0  0
  132229  0  0  1
  132782  0  0  1
  133750  1  0  0
  134400  0  0  1
  134532  0  0  1
  134544  0  0  1
  135000  0  0  1
  135409  0  1  0
  136428  0  1  0
  139000  0  1  0
  139660  0  0  1
  140178  0  0  1
  143674  0  0  1
  147852  0  1  0
  150300  0  0  1
  157008  0  1  0
  157092  1  0  0
  158140  0  0  1
  158450  0  1  0
  160296  0  0  1
  160314  0  0  1
  161000  0  1  0
  161566  1  0  0
  164265  0  0  1
  164320  0  0  1
  169021  0  1  0
  177504  0  0  1
  181104  0  0  1
  184752  0  1  0
  189631  0  0  1
  190412  0  0  1
  191000  0  0  1
  223537  0  1  0

menampilkan nilai besar salary dan frekuensi pada tiap year (92,95,99)

barplot(dt1,
        main = "Distribusi Salary Berdasarkan Year",
        xlab = "Year",
        ylab = "Frekuensi",
        legend = FALSE,
        beside = TRUE,
        col = c("red", "blue", "grey"))

visualisasi distribusi frekuensi salary berdasarkan year dengan barplot

dt2 <- table(big9salary$salary, big9salary$exper)
dt2

        
         0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
  18670  1 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  21051  1 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  22254  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  29337  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  29462  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  33128  0 0 0 0 0 0 1 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  33155  0 0 0 0 0 0 0 0 0 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  33642  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  36050  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  38200  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0
  40500  0 1 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  41490  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  41600  0 0 0 0 0 0 0 0 0 0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  41618  0 0 0 0 0 0 0 1 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  42648  0 0 0 0 0 0 0 0 0 1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  43207  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  44000  0 1 0 0 0 0 0 1 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  44100  0 0 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  44550  0 0 0 0 0 1 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  44771  1 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  44850  0 0 0 0 0 0 0 0 0 0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  45000  1 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  45358  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  45755  0 1 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  45950  0 0 0 0 1 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  46000  1 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  46200  0 0 0 0 0 0 0 0 1 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  46300  0 0 0 0 0 0 0 1 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47000  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  1
  47250  0 0 0 0 0 0 1 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47300  0 0 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47333  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
  47500  0 0 0 1 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47575  0 0 0 0 0 0 1 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47667  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
  47810  0 0 0 0 1 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47831  0 0 0 1 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  47892  0 0 0 0 0 0 1 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  48000  0 0 0 0 0 0 0 1 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  48200  0 0 0 0 0 0 0 0 0 0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  48293  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  48345  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  48500  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
  48905  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
  48950  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  49156  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
  49360  0 0 0 0 0 0 0 0 0 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  49553  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  50200  0 0 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  50209  0 0 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
  50367  0 0 0 0 1 0 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  50500  0 0 0 0 0 0 0 0 0 0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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Menampilkan distribusi frekuensi salary berdasar experience

barplot(dt2,
        main = "Distribusi Salary Berdasarkan Experience",
        xlab = "Experience",
        ylab = "Frekuensi",
        legend = FALSE,
        beside = TRUE,
        col = c("green", "yellow"))

visualisasi distribusi frekuensi salary berdasarkan experience dengan barplot

Mean_score <- mean(big9salary$exper, na.rm = TRUE)
Mean_score

[1] 18.33476

menampilkan hasil rata-rata dari variabel experience yaitu 18.33476 (tahun)

Median_score <- median(big9salary$exper, na.rm = TRUE)
Median_score

[1] 18

nilai tengah atau kuartil kedua dari variabel experience yaitu 18 tahun

Kuartil_score <- quantile(big9salary$exper, probs = c(0.25, 0.5, 0.75), na.rm = TRUE)
Kuartil_score

25% 50% 75% 
 10  18  25 

menampilkan nilai kuartil 1,2,3 dari variabel experience yaitu 10,18,25

Decile_0.9_score <- quantile(big9salary$exper, probs = 0.9, na.rm = TRUE)
Decile_0.9_score

90% 
 33 

menampilkan hasil desil ke-9 dari variabel experience sebesar 33

Percentile_0.45_score <- quantile(big9salary$exper, probs = 0.45, na.rm = TRUE)
Percentile_0.45_score

45% 
 16 

menampilkan hasil pesentil 45 dari variabel experience sebesar 16

Variance_score <- var(big9salary$exper, na.rm = TRUE)
Variance_score

[1] 115.8878

nilai variansi variabel experience sebesar 115.8878

Standar_deviasi_score <- sd(big9salary$exper, na.rm = TRUE)
Standar_deviasi_score

[1] 10.76512

nilai standar deviasi variabel experience sebesar 10.76512

hist(big9salary$salary, 
     main = "Distribusi Frekuensi Salary", 
     xlab = "Salary", 
     ylab = "Frekuensi", 
     col = "skyblue", 
     breaks = 10)

histogram menampilkan distribusi frekuensi salary dimana hasil tertinggi terletak pada rentang 50000-100000

qqnorm(big9salary$salary)
qqline(big9salary$salary)

QQplot menunjukkan pesebaran data frekuensi salary, Jika data mengikuti distribusi normal, titik-titik akan mengikuti garis lurus

library(moments)
skewness(na.omit(big9salary$exper))

[1] 0.4452345

hasil uji kemiringan variabel experience dengan tidak memperhatikan nilai NA sebesar 0.4452345

skewness(na.omit(big9salary$salary))

[1] 1.050623

hasil uji kemiringan variabel salary dengan tidak memperhatikan nilai NA sebesar 1.050623 jika nilai skewness mendekati nol, ini menunjukkan bahwa distribusi data adalah simetris

library(lattice)
densityplot(big9salary$exper,data=big9salary,xlab="Sample Experience")

Warning in densityplot.numeric(big9salary$exper, data = big9salary, xlab =
"Sample Experience"): explicit 'data' specification ignored

dengan menggunakan package lattice kita dapat mengetahui nilai kepadatan variabel experience

t.test(big9salary$salary, mu=1000000, alternative="greater")

    One Sample t-test

data:  big9salary$salary
t = -849.16, df = 678, p-value = 1
alternative hypothesis: true mean is greater than 1e+06
95 percent confidence interval:
 80264.32      Inf
sample estimates:
mean of x 
 82044.86 

uji hipotesis menunjukkan apakah rata-rata salary secara signifikan lebih tinggi dari $1,000,000. Jika p-value dari uji t kurang dari 0.05, kita menolak hipotesis nol dan menyimpulkan bahwa rata-rata salary lebih tinggi dari $1,000,000

Varians

anova_model <- aov(exper ~ as.factor(salary), data=big9salary)
summary(anova_model)

                   Df Sum Sq Mean Sq F value  Pr(>F)   
as.factor(salary) 568  59990  105.62   1.656 0.00863 **
Residuals          59   3763   63.78                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
158 observations deleted due to missingness

fungsi aov digunakan untuk melakukan analisis varians (ANOVA). ANOVA adalah metode statistik yang digunakan untuk membandingkan rata-rata dari dua atau lebih grup (kategori) untuk melihat apakah ada perbedaan yang signifikan di antara mereka, nilai p (p-value) untuk uji F. Jika nilai p lebih kecil dari tingkat signifikansi yang dipilih (misalnya 0.05), kita menolak hipotesis nol dan menyimpulkan bahwa ada perbedaan yang signifikan antara rata-rata grup

Regresi

model <- lm(salary ~ yearphd, data=big9salary)
summary(model)

Call:
lm(formula = salary ~ yearphd, data = big9salary)

Residuals:
   Min     1Q Median     3Q    Max 
-71480 -17343  -2953  11815 111745 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 156789.9     7861.0  19.945   <2e-16 ***
yearphd       -974.1      102.0  -9.554   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 25620 on 626 degrees of freedom
  (158 observations deleted due to missingness)
Multiple R-squared:  0.1273,    Adjusted R-squared:  0.1259 
F-statistic: 91.28 on 1 and 626 DF,  p-value: < 2.2e-16

koefisien menunjukkan bagaimana perubahan dalam yearphd mempengaruhi salary

Peramalan

new_data <- data.frame(yearphd=c(5, 10, 15))
predictions <- predict(model, new_data)
predictions

       1        2        3 
151919.3 147048.7 142178.1 

memberikan estimasi salary untuk nilai yearphd yang diberikan (5, 10, dan 15 tahun), peramalan menggunakan model regresi memberikan estimasi salary berdasarkan nilai pengalaman kerja tertentu. Misalnya, memperkirakan salary dengan pengalaman kerja 5, 10, dan 15 tahun

Sekian Terimakasih