Week_4_day_1
Jahanvi Khedwal
21 January 2018
TASK :2a
starting_salaries <- read.csv(paste("MBA Starting Salaries Data.csv", sep=""))
View(starting_salaries)
attach(starting_salaries)#people who have a certain salary which they have disclosed and given a rating of satisfaction also.
mytable <- subset.data.frame(starting_salaries, (salary!=998 & salary !=999))
View(mytable)library(corrplot)## corrplot 0.84 loadedlibrary(corrgram)
str(mytable)## 'data.frame': 193 obs. of 13 variables:
## $ age : int 23 24 24 24 24 25 25 27 27 28 ...
## $ sex : int 2 1 1 1 1 1 2 1 1 2 ...
## $ gmat_tot: int 620 610 670 570 640 610 650 740 750 540 ...
## $ gmat_qpc: int 77 90 99 56 82 89 88 99 99 75 ...
## $ gmat_vpc: int 87 71 78 81 89 74 89 96 98 50 ...
## $ gmat_tpc: int 87 87 95 75 91 87 92 99 99 65 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.9 3.4 3.3 3.5 3.4 3.6 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.5 3.75 3.5 3.5 4 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 3 1 5 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 0 0 0 0 0 0 ...
## $ satis : int 7 6 6 7 6 5 6 6 5 5 ...boxplot(mytable$age, xlab= "age", ylab = "age", main= "age distribution", horizontal = TRUE)
boxplot(mytable$gmat_tot, xlab= "gmat_total", ylab = "gmat_total", main= "gmat_total distribution", horizontal = TRUE)
boxplot(mytable$gmat_qpc, xlab= "gmat_qpc", ylab = "gmat_qpc", main= "gmat_qpc distribution", horizontal = TRUE)
boxplot(mytable$gmat_vpc, xlab= "gmat_vpc", ylab = "gmat_vpc", main= "gmat_vpc distribution", horizontal = TRUE)
boxplot(mytable$gmat_tpc, xlab= "gmat_tpc", ylab = "gmat_tpc", main= "gmat_tpc distribution", horizontal = TRUE)
boxplot(mytable$s_avg, xlab= "s_avg", ylab = "s_avg", main= "s_avg distribution", horizontal = TRUE)
boxplot(mytable$f_avg, xlab= "f_avg", ylab = "f_avg", main= "f_avg distribution", horizontal = TRUE)
boxplot(mytable$work_yrs , xlab= "work_yrs ", ylab = "work_yrs ", main= "work_yrs distribution", horizontal = TRUE)
boxplot(mytable$salary, xlab= "salary", ylab = "salary", main= "salary distribution", horizontal = TRUE)
boxplot(mytable$satis, xlab= "satis", ylab = "satis", main= "satis distribution", horizontal = TRUE)
plot(x = mytable$age, y=mytable$gmat_tot, xlab= "age", ylab = "gmat_tot")
plot(x = mytable$gmat_tot, y=mytable$salary, xlab= "gmat_tot", ylab = "salary")
plot(x = mytable$work_yrs, y=mytable$salary, xlab= "work_yrs", ylab = "salary")
plot(x = mytable$age, y=mytable$satis, xlab= "age", ylab = "satisfaction")
plot(x = mytable$salary, y=mytable$satis, xlab= "salary", ylab = "satisfaction")
plot(x = mytable$s_avg, y=mytable$salary, xlab= "s_avg", ylab = "salary")
library(corrplot)
library(corrgram)
cov(mytable)## age sex gmat_tot gmat_qpc
## age 1.778562e+01 -0.060503022 -29.954933 -14.0897291
## sex -6.050302e-02 0.202558290 -1.107243 -1.1445110
## gmat_tot -2.995493e+01 -1.107243092 3196.950561 636.3509283
## gmat_qpc -1.408973e+01 -1.144511010 636.350928 229.3840674
## gmat_vpc -4.564443e-01 0.718776986 685.464432 42.7985481
## gmat_tpc -7.512764e+00 -0.078232945 672.465188 141.4933074
## s_avg 2.626913e-01 0.012515382 3.076706 0.1092870
## f_avg -7.513817e-02 0.010030764 2.969557 1.0252407
## quarter -3.567573e-01 -0.043042962 -5.248543 0.1438364
## work_yrs 1.355880e+01 -0.039561744 -36.222204 -13.4840782
## frstlang 1.105084e-01 -0.001025475 -1.450507 0.3843372
## salary -2.918528e+04 442.963190846 -170.881369 22855.7178325
## satis -2.399342e-01 -0.021507988 3.493361 -0.2345369
## gmat_vpc gmat_tpc s_avg f_avg
## age -0.4564443 -7.512764e+00 0.26269133 -0.075138169
## sex 0.7187770 -7.823294e-02 0.01251538 0.010030764
## gmat_tot 685.4644322 6.724652e+02 3.07670553 2.969556887
## gmat_qpc 42.7985481 1.414933e+02 0.10928703 1.025240717
## gmat_vpc 259.2695920 1.498748e+02 1.16361534 0.276970261
## gmat_tpc 149.8747571 1.830114e+02 0.96881989 0.771858538
## s_avg 1.1636153 9.688199e-01 0.14365606 0.102512632
## f_avg 0.2769703 7.718585e-01 0.10251263 0.269959639
## quarter -2.4783571 -1.919905e+00 -0.30776770 -0.219389573
## work_yrs -2.4562014 -8.289778e+00 0.22246519 -0.091892541
## frstlang -1.2757448 -3.918124e-01 -0.01285163 -0.007786593
## salary 2901.3078044 4.382253e+04 1940.52763601 244.315688687
## satis 2.4320758 1.391489e+00 -0.01361264 -0.046131531
## quarter work_yrs frstlang salary
## age -3.567573e-01 1.355880e+01 0.110508420 -2.918528e+04
## sex -4.304296e-02 -3.956174e-02 -0.001025475 4.429632e+02
## gmat_tot -5.248543e+00 -3.622220e+01 -1.450507340 -1.708814e+02
## gmat_qpc 1.438364e-01 -1.348408e+01 0.384337219 2.285572e+04
## gmat_vpc -2.478357e+00 -2.456201e+00 -1.275744819 2.901308e+03
## gmat_tpc -1.919905e+00 -8.289778e+00 -0.391812392 4.382253e+04
## s_avg -3.077677e-01 2.224652e-01 -0.012851630 1.940528e+03
## f_avg -2.193896e-01 -9.189254e-02 -0.007786593 2.443157e+02
## quarter 1.219128e+00 -5.149773e-01 0.026527418 -8.642229e+03
## work_yrs -5.149773e-01 1.360379e+01 -0.002887522 -1.044263e+04
## frstlang 2.652742e-02 -2.887522e-03 0.072053109 1.016680e+02
## salary -8.642229e+03 -1.044263e+04 101.668015976 2.825177e+09
## satis 5.788536e-02 -2.204771e-02 -0.028254534 6.436294e+03
## satis
## age -0.23993415
## sex -0.02150799
## gmat_tot 3.49336140
## gmat_qpc -0.23453692
## gmat_vpc 2.43207578
## gmat_tpc 1.39148856
## s_avg -0.01361264
## f_avg -0.04613153
## quarter 0.05788536
## work_yrs -0.02204771
## frstlang -0.02825453
## salary 6436.29447323
## satis 0.59914724cor(mytable)## age sex gmat_tot gmat_qpc gmat_vpc
## age 1.000000000 -0.031876273 -1.256220e-01 -0.220590341 -0.006721674
## sex -0.031876273 1.000000000 -4.351109e-02 -0.167904888 0.099184398
## gmat_tot -0.125622047 -0.043511095 1.000000e+00 0.743099719 0.752906719
## gmat_qpc -0.220590341 -0.167904888 7.430997e-01 1.000000000 0.175497777
## gmat_vpc -0.006721674 0.099184398 7.529067e-01 0.175497777 1.000000000
## gmat_tpc -0.131681932 -0.012849186 8.791496e-01 0.690581939 0.688039929
## s_avg 0.164342257 0.073368077 1.435675e-01 0.019038162 0.190665307
## f_avg -0.034290725 0.042895288 1.010821e-01 0.130285115 0.033106093
## quarter -0.076614994 -0.086616877 -8.407099e-02 0.008601267 -0.139400223
## work_yrs 0.871679595 -0.023832548 -1.736909e-01 -0.241384675 -0.041357878
## frstlang 0.097619028 -0.008488358 -9.557089e-02 0.094537575 -0.295162826
## salary -0.130198680 0.018516965 -5.685962e-05 0.028391635 0.003389965
## satis -0.073500580 -0.061738773 7.981946e-02 -0.020006117 0.195134711
## gmat_tpc s_avg f_avg quarter work_yrs
## age -0.13168193 0.16434226 -0.034290725 -0.076614994 0.871679595
## sex -0.01284919 0.07336808 0.042895288 -0.086616877 -0.023832548
## gmat_tot 0.87914961 0.14356746 0.101082103 -0.084070990 -0.173690863
## gmat_qpc 0.69058194 0.01903816 0.130285115 0.008601267 -0.241384675
## gmat_vpc 0.68803993 0.19066531 0.033106093 -0.139400223 -0.041357878
## gmat_tpc 1.00000000 0.18894788 0.109811857 -0.128533421 -0.166139876
## s_avg 0.18894788 1.00000000 0.520554250 -0.735421726 0.159136628
## f_avg 0.10981186 0.52055425 1.000000000 -0.382421186 -0.047951357
## quarter -0.12853342 -0.73542173 -0.382421186 1.000000000 -0.126454286
## work_yrs -0.16613988 0.15913663 -0.047951357 -0.126454286 1.000000000
## frstlang -0.10789784 -0.12631935 -0.055830525 0.089504320 -0.002916547
## salary 0.06094464 0.09632412 0.008846655 -0.147257809 -0.053266846
## satis 0.13288434 -0.04639953 -0.114704819 0.067729421 -0.007722658
## frstlang salary satis
## age 0.097619028 -1.301987e-01 -0.073500580
## sex -0.008488358 1.851696e-02 -0.061738773
## gmat_tot -0.095570885 -5.685962e-05 0.079819458
## gmat_qpc 0.094537575 2.839164e-02 -0.020006117
## gmat_vpc -0.295162826 3.389965e-03 0.195134711
## gmat_tpc -0.107897839 6.094464e-02 0.132884339
## s_avg -0.126319350 9.632412e-02 -0.046399534
## f_avg -0.055830525 8.846655e-03 -0.114704819
## quarter 0.089504320 -1.472578e-01 0.067729421
## work_yrs -0.002916547 -5.326685e-02 -0.007722658
## frstlang 1.000000000 7.125825e-03 -0.135986251
## salary 0.007125825 1.000000e+00 0.156439455
## satis -0.135986251 1.564395e-01 1.000000000corrgram(mytable, order= TRUE, lower.panel = panel.shade, upper.panel=panel.pie, text.panel = panel.txt,main="Corrgram of MBA Starting salaries intercorrelation")
#TASK 2b:
#people who got a job
mytable1 <- subset.data.frame(starting_salaries, (salary!=998 & salary !=0 ))
View(mytable1)model <- lm(salary ~ . , data= mytable1)
summary(model)##
## Call:
## lm(formula = salary ~ ., data = mytable1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -106074 -18770 6309 22085 130167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61318.83 102115.00 0.600 0.549
## age 446.07 2287.54 0.195 0.846
## sex 8357.64 8021.94 1.042 0.299
## gmat_tot -296.81 263.45 -1.127 0.262
## gmat_qpc 296.57 711.50 0.417 0.678
## gmat_vpc -93.41 691.00 -0.135 0.893
## gmat_tpc 675.03 505.03 1.337 0.184
## s_avg 10327.60 17641.58 0.585 0.559
## f_avg -2134.04 9173.69 -0.233 0.816
## quarter -8570.89 5689.33 -1.506 0.134
## work_yrs 1171.68 2390.24 0.490 0.625
## frstlang -15309.00 13461.41 -1.137 0.258
## satis 20286.55 3169.84 6.400 2.84e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38670 on 125 degrees of freedom
## Multiple R-squared: 0.3863, Adjusted R-squared: 0.3273
## F-statistic: 6.556 on 12 and 125 DF, p-value: 5.51e-09mytable2<- xtabs(~ sex + satis + work_yrs, data= mytable1)
mytable2## , , work_yrs = 0
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 1 1 0 0
## 2 0 0 0 0 0 0 0
##
## , , work_yrs = 1
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 4 1 1
## 2 0 0 0 0 3 0 1
##
## , , work_yrs = 2
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 4 4 14 6
## 2 0 1 0 0 5 5 5
##
## , , work_yrs = 3
##
## satis
## sex 1 2 3 4 5 6 7
## 1 1 0 1 2 5 13 1
## 2 0 0 1 0 2 1 2
##
## , , work_yrs = 4
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 2 3 4 8 3
## 2 0 0 0 1 1 0 0
##
## , , work_yrs = 5
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 1 2 3 0
## 2 0 0 0 0 1 1 1
##
## , , work_yrs = 6
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 2 4 0
## 2 0 0 0 1 1 1 0
##
## , , work_yrs = 7
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 1 0 2 0 0
## 2 0 0 0 0 0 0 0
##
## , , work_yrs = 8
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 0 2 1
## 2 0 0 0 0 0 1 0
##
## , , work_yrs = 9
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0
##
## , , work_yrs = 10
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 0
##
## , , work_yrs = 15
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 0 1 0
## 2 0 0 0 0 0 1 0
##
## , , work_yrs = 16
##
## satis
## sex 1 2 3 4 5 6 7
## 1 0 0 0 0 1 0 1
## 2 0 0 0 0 0 0 0mytable3 <- xtabs(~ sex + satis, data= mytable1)
mytable3## satis
## sex 1 2 3 4 5 6 7
## 1 1 0 4 11 25 47 14
## 2 0 1 1 2 13 10 9chisq.test(mytable3)## Warning in chisq.test(mytable3): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable3
## X-squared = 9.5421, df = 6, p-value = 0.1453chisq.test(mytable3)$p.value## Warning in chisq.test(mytable3): Chi-squared approximation may be incorrect## [1] 0.1453057t.test(salary ~ sex, data= mytable1)##
## Welch Two Sample t-test
##
## data: salary by sex
## t = -1.2601, df = 72.408, p-value = 0.2117
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -27337.580 6161.152
## sample estimates:
## mean in group 1 mean in group 2
## 74390.98 84979.19t.test(work_yrs ~ sex, data= mytable1)##
## Welch Two Sample t-test
##
## data: work_yrs by sex
## t = 1.0187, df = 65.372, p-value = 0.3121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5052135 1.5575011
## sample estimates:
## mean in group 1 mean in group 2
## 3.803922 3.277778fit <- lm(salary ~ age + gmat_qpc + gmat_tot + gmat_tpc + gmat_vpc + s_avg + f_avg + work_yrs , data=mytable1)
summary(fit)##
## Call:
## lm(formula = salary ~ age + gmat_qpc + gmat_tot + gmat_tpc +
## gmat_vpc + s_avg + f_avg + work_yrs, data = mytable1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -103721 -24776 13153 26311 148502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 212081.2 98430.6 2.155 0.03305 *
## age -2822.5 2341.4 -1.206 0.23021
## gmat_qpc 642.6 802.7 0.801 0.42489
## gmat_tot -595.7 295.1 -2.018 0.04561 *
## gmat_tpc 779.1 567.1 1.374 0.17188
## gmat_vpc 898.3 733.7 1.224 0.22303
## s_avg 35619.0 12239.8 2.910 0.00426 **
## f_avg -480.2 10399.4 -0.046 0.96324
## work_yrs 4248.5 2612.4 1.626 0.10633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 44430 on 129 degrees of freedom
## Multiple R-squared: 0.1638, Adjusted R-squared: 0.1119
## F-statistic: 3.158 on 8 and 129 DF, p-value: 0.00268fitted(fit)## 5 9 21 26 30 35
## 80464.687 92556.234 104719.948 95005.546 31593.444 97650.379
## 36 37 38 39 40 41
## 68151.049 85259.487 93496.865 61562.579 93492.945 75432.808
## 42 43 44 45 46 47
## 90308.874 100357.914 81186.699 99249.699 93098.964 90868.952
## 48 49 50 51 52 53
## 102575.671 87872.062 95060.001 114166.574 98205.326 92176.242
## 54 55 56 57 58 59
## 105551.115 107859.589 92516.765 87341.391 103781.599 104692.509
## 60 61 62 63 64 65
## 122800.922 92310.260 69153.763 109071.933 97600.692 93471.161
## 66 67 68 69 78 87
## 102020.206 93515.246 131953.497 78072.091 68937.871 70014.687
## 91 99 101 105 108 115
## 41071.224 77936.282 76571.673 95494.961 70968.328 67903.512
## 116 117 118 119 120 121
## 80865.997 90762.858 78205.780 92487.613 93938.080 78108.243
## 122 123 124 125 126 127
## 98753.423 88935.247 68859.186 84750.840 83443.760 76584.562
## 128 129 130 131 132 133
## 80865.997 79309.972 85263.674 73793.506 84243.016 113998.878
## 134 135 136 137 138 139
## 76983.787 81610.737 79073.292 91061.408 88703.987 83286.027
## 145 152 158 161 166 170
## 61099.089 65571.286 71337.197 60420.603 29816.948 48508.177
## 179 181 186 187 188 189
## 93992.144 71898.842 76627.417 64296.780 78879.895 70771.187
## 190 191 192 193 194 195
## 81432.579 80733.390 82985.385 69398.364 71615.379 81179.192
## 196 197 198 199 200 201
## 73497.418 60019.816 65777.710 63680.506 59384.892 74606.568
## 202 203 204 205 206 207
## 77310.618 82684.202 67454.123 76092.671 66987.746 50618.905
## 208 209 212 214 217 221
## 82224.900 61588.047 65042.377 65012.833 48547.918 61442.062
## 223 226 228 231 235 239
## 62845.469 55881.295 -3771.632 87205.430 51949.184 47119.312
## 240 245 246 251 252 256
## 70335.700 51905.088 65710.219 73010.930 43067.744 103077.541
## 257 258 259 260 261 262
## 64203.074 75150.670 59998.015 75386.284 63911.527 77807.613
## 263 264 265 266 267 268
## 63903.262 53885.011 56333.325 71577.906 57545.856 60036.479
## 269 270 271 272 273 274
## 63805.282 46942.933 62213.563 65824.182 67220.802 71497.677residuals(fit)## 5 9 21 26 30
## -79465.6868 -91557.2343 -103720.9476 -94006.5458 -30594.4445
## 35 36 37 38 39
## -12650.3791 16848.9514 740.5127 -5496.8651 30437.4214
## 40 41 42 43 44
## -492.9446 19567.1922 4691.1257 -5357.9141 14813.3005
## 45 46 47 48 49
## -3249.6995 6901.0357 9131.0484 -2575.6710 17127.9379
## 50 51 52 53 54
## 9939.9994 -9166.5744 6794.6740 12823.7580 -551.1151
## 55 56 57 58 59
## -1859.5890 13483.2355 20158.6094 4218.4014 5307.4907
## 60 61 62 63 64
## -10800.9217 22689.7402 45846.2375 8928.0674 22399.3080
## 65 66 67 68 69
## 26528.8387 17979.7943 26484.7540 14046.5029 83927.9092
## 78 87 91 99 101
## -67938.8715 -69015.6867 -40072.2240 -76937.2821 -75572.6729
## 105 108 115 116 117
## -94495.9607 -69969.3282 14096.4881 11134.0033 2237.1419
## 118 119 120 121 122
## 16794.2196 2512.3866 2061.9204 18391.7569 -753.4230
## 123 124 125 126 127
## 9064.7532 29140.8138 14249.1604 16556.2403 23415.4385
## 128 129 130 131 132
## 20134.0033 23690.0279 18736.3264 31206.4937 20756.9844
## 133 134 135 136 137
## -8998.8785 30016.2126 30389.2626 35926.7082 23938.5920
## 138 139 145 152 158
## 41296.0132 62513.9733 -60100.0892 -64572.2860 -70338.1969
## 161 166 170 179 181
## -59421.6032 -28817.9483 -47509.1770 -92993.1441 -70899.8421
## 186 187 188 189 190
## 1628.5827 24203.2204 11120.1052 19228.8127 11567.4211
## 191 192 193 194 195
## 14266.6097 14014.6147 27601.6355 26384.6211 16820.8078
## 196 197 198 199 200
## 24502.5815 37980.1841 32222.2896 34319.4940 40615.1081
## 201 202 203 204 205
## 25393.4322 23689.3823 18415.7978 35045.8775 28907.3292
## 206 207 208 209 212
## 39012.2536 56681.0949 25775.1002 50411.9526 -64043.3774
## 214 217 221 223 226
## -64013.8328 -47548.9179 -60443.0623 -61846.4687 -54882.2946
## 228 231 235 239 240
## 4770.6318 -86206.4299 -50950.1836 -46120.3123 -69336.7004
## 245 246 251 252 256
## -50906.0879 -64711.2190 -72011.9296 -42068.7441 -39077.5412
## 257 258 259 260 261
## 12796.9263 9849.3304 25001.9846 10613.7159 26088.4733
## 262 263 264 265 266
## 14192.3874 31096.7382 42114.9893 41666.6753 28422.0942
## 267 268 269 270 271
## 42454.1444 40363.5209 37794.7181 57057.0667 42786.4373
## 272 273 274
## 49175.8179 59489.1979 148502.3231Interpretation:
The regression coefficients are significantly different from zero, (p < 0.001) and this indicates that there is an expected decrease/increase in the salary, depending upon several factors, for example, the salary increases 779.1 times when the ‘gmat_tpc’ score is increased by one.
The multiple R-squared (0.991) indicates that the model accounts for 11.1% of the variance in salary.
mytable4 <- subset.data.frame(starting_salaries, (salary!=998 & salary==0 & salary!=999 ))
View(mytable4)mytable5<- xtabs(~ sex + satis + work_yrs, data= mytable4)
mytable5## , , work_yrs = 0
##
## satis
## sex 4 5 6 7
## 1 0 1 0 0
## 2 0 0 0 0
##
## , , work_yrs = 1
##
## satis
## sex 4 5 6 7
## 1 1 5 4 2
## 2 0 0 0 0
##
## , , work_yrs = 2
##
## satis
## sex 4 5 6 7
## 1 0 3 10 3
## 2 1 1 2 2
##
## , , work_yrs = 3
##
## satis
## sex 4 5 6 7
## 1 0 3 6 0
## 2 2 2 1 0
##
## , , work_yrs = 4
##
## satis
## sex 4 5 6 7
## 1 0 4 4 0
## 2 0 1 0 0
##
## , , work_yrs = 5
##
## satis
## sex 4 5 6 7
## 1 0 6 1 0
## 2 0 2 3 0
##
## , , work_yrs = 6
##
## satis
## sex 4 5 6 7
## 1 0 0 1 1
## 2 0 0 0 0
##
## , , work_yrs = 7
##
## satis
## sex 4 5 6 7
## 1 0 1 2 0
## 2 0 1 1 0
##
## , , work_yrs = 8
##
## satis
## sex 4 5 6 7
## 1 0 1 1 0
## 2 0 0 0 0
##
## , , work_yrs = 9
##
## satis
## sex 4 5 6 7
## 1 0 0 0 0
## 2 0 0 1 0
##
## , , work_yrs = 10
##
## satis
## sex 4 5 6 7
## 1 0 0 0 0
## 2 0 0 0 1
##
## , , work_yrs = 11
##
## satis
## sex 4 5 6 7
## 1 0 0 1 0
## 2 0 0 0 1
##
## , , work_yrs = 12
##
## satis
## sex 4 5 6 7
## 1 0 2 0 0
## 2 0 0 0 0
##
## , , work_yrs = 13
##
## satis
## sex 4 5 6 7
## 1 0 0 0 0
## 2 0 1 0 0
##
## , , work_yrs = 16
##
## satis
## sex 4 5 6 7
## 1 0 1 0 0
## 2 0 0 0 0
##
## , , work_yrs = 18
##
## satis
## sex 4 5 6 7
## 1 0 0 1 0
## 2 0 0 0 0
##
## , , work_yrs = 22
##
## satis
## sex 4 5 6 7
## 1 0 1 1 0
## 2 0 0 0 0mytable6 <- xtabs(~ sex + satis, data= mytable4)
mytable6## satis
## sex 4 5 6 7
## 1 1 28 32 6
## 2 3 8 8 4chisq.test(mytable4)## Warning in chisq.test(mytable4): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable4
## X-squared = NaN, df = 1068, p-value = NAchisq.test(mytable3)$p.value## Warning in chisq.test(mytable3): Chi-squared approximation may be incorrect## [1] 0.1453057