Draft5:Race_Beauty_Income
Zainab
2023-01-19
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setwd("/Users/zainaboketokoun/Documents")
data1= read.csv("beauty.csv")
sapply(data1, sd)## X wage lwage belavg abvavg exper
## 363.8749785 4.6606390 0.5945075 0.3285858 0.4601517 11.9634853
## looks union goodhlth black female married
## 0.6848774 0.4452804 0.2495429 0.2615645 0.4758923 0.4621530
## south bigcity smllcity service expersq educ
## 0.3797781 0.4137652 0.4990857 0.4460895 534.6454250 2.6244892vsee= summary(data1)
print(vsee)## X wage lwage belavg
## Min. : 1.0 Min. : 1.020 Min. :0.0198 Min. :0.000
## 1st Qu.: 315.8 1st Qu.: 3.708 1st Qu.:1.3104 1st Qu.:0.000
## Median : 630.5 Median : 5.300 Median :1.6677 Median :0.000
## Mean : 630.5 Mean : 6.307 Mean :1.6588 Mean :0.123
## 3rd Qu.: 945.2 3rd Qu.: 7.695 3rd Qu.:2.0406 3rd Qu.:0.000
## Max. :1260.0 Max. :77.720 Max. :4.3531 Max. :1.000
## abvavg exper looks union
## Min. :0.000 Min. : 0.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.: 8.00 1st Qu.:3.000 1st Qu.:0.0000
## Median :0.000 Median :15.00 Median :3.000 Median :0.0000
## Mean :0.304 Mean :18.21 Mean :3.186 Mean :0.2722
## 3rd Qu.:1.000 3rd Qu.:27.00 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :1.000 Max. :48.00 Max. :5.000 Max. :1.0000
## goodhlth black female married
## Min. :0.0000 Min. :0.00000 Min. :0.000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.0000
## Median :1.0000 Median :0.00000 Median :0.000 Median :1.0000
## Mean :0.9333 Mean :0.07381 Mean :0.346 Mean :0.6913
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.00000 Max. :1.000 Max. :1.0000
## south bigcity smllcity service
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.000 Median :0.0000 Median :0.0000
## Mean :0.1746 Mean :0.219 Mean :0.4667 Mean :0.2738
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## expersq educ
## Min. : 0.0 Min. : 5.00
## 1st Qu.: 64.0 1st Qu.:12.00
## Median : 225.0 Median :12.00
## Mean : 474.5 Mean :12.56
## 3rd Qu.: 729.0 3rd Qu.:13.00
## Max. :2304.0 Max. :17.00CONCLUSION
The mean for years of work experience in this sample is 18.21 years while the median is 15 years.The distribution is slightly skewed to the right. The mean and median for looks was approximate 3, when the mean = the median that suggest that all samples are equal. The Looks data is normally distributed.The distribution above the mean is symmetrical to the distribution below the mean. Similarly, lwage and years of education are normally distribution.This suggest these data do not have extreme values and/or have a flatter dome, which means they have wide standard deviation. The standard deviation for both looks and lwage is below 1, not very wide.The standard deviation for education is estimate 3 years which is small under the curve. Implication: The samples under these columns are equal.
#Make a new df with a subset of the original dataset, make sure to rename the dataset and new names for the columns
Beauty2Pwr<- data1[c(1:1260),c(1:3,7,6,9:12,17,18)]
Beauty2Pwr$black[Beauty2Pwr$black == 0]<-"Non-AA"
Beauty2Pwr$black[Beauty2Pwr$black == 1]<-"AA"
Beauty2Pwr$female[Beauty2Pwr$female == 1] <- "Female"
Beauty2Pwr$female[Beauty2Pwr$female == 0] <- "Male"
Beauty2Pwr<- setNames(Beauty2Pwr,c("SampleID","Wages","Log_Wages","Attractive","Exper_yrs","GudHlth","Black_NotBlack","Sex_bio","MartialStat","ExperSQ","Edu_yrs"))
Beauty2Pwr## SampleID Wages Log_Wages Attractive Exper_yrs GudHlth Black_NotBlack
## 1 1 5.73 1.74571550 4 30 1 Non-AA
## 2 2 4.28 1.45395303 3 28 1 Non-AA
## 3 3 7.96 2.07442904 4 35 1 Non-AA
## 4 4 11.57 2.44841552 3 38 1 Non-AA
## 5 5 11.42 2.43536615 3 27 1 Non-AA
## 6 6 3.91 1.36353743 3 20 0 Non-AA
## 7 7 8.76 2.17019582 3 12 1 Non-AA
## 8 8 7.69 2.03992081 4 5 1 Non-AA
## 9 9 5.00 1.60943794 3 5 1 Non-AA
## 10 10 3.89 1.35840917 3 12 1 Non-AA
## 11 11 3.45 1.23837423 4 3 1 Non-AA
## 12 12 4.03 1.39376640 4 6 1 Non-AA
## 13 13 5.14 1.63705301 2 19 1 Non-AA
## 14 14 3.00 1.09861231 3 8 1 Non-AA
## 15 15 7.99 2.07819080 4 12 1 Non-AA
## 16 16 6.01 1.79342484 4 17 1 Non-AA
## 17 17 5.16 1.64093649 3 7 1 Non-AA
## 18 18 11.54 2.44581914 4 12 1 Non-AA
## 19 19 10.44 2.34564447 4 10 1 Non-AA
## 20 20 7.69 2.03992081 3 7 1 Non-AA
## 21 21 7.69 2.03992081 4 7 1 Non-AA
## 22 22 6.79 1.91545093 3 19 1 Non-AA
## 23 23 6.87 1.92716408 3 33 1 Non-AA
## 24 24 17.03 2.83497643 3 32 1 Non-AA
## 25 25 10.05 2.30757260 4 12 1 Non-AA
## 26 26 15.81 2.76064277 4 24 1 Non-AA
## 27 27 14.84 2.69732618 5 29 1 Non-AA
## 28 28 19.08 2.94864058 5 17 1 Non-AA
## 29 29 8.35 2.12226152 1 41 0 Non-AA
## 30 30 9.62 2.26384425 3 40 0 Non-AA
## 31 31 5.96 1.78507054 3 10 1 Non-AA
## 32 32 5.73 1.74571550 3 43 0 Non-AA
## 33 33 6.73 1.90657520 4 41 1 Non-AA
## 34 34 8.17 2.10046887 2 18 1 Non-AA
## 35 35 12.39 2.51688981 4 28 1 Non-AA
## 36 36 9.62 2.26384425 2 37 1 Non-AA
## 37 37 4.83 1.57484651 4 20 1 AA
## 38 38 7.69 2.03992081 2 10 1 Non-AA
## 39 39 12.50 2.52572870 3 31 1 Non-AA
## 40 40 2.82 1.03673685 3 18 1 Non-AA
## 41 41 12.31 2.51041198 3 9 1 Non-AA
## 42 42 13.22 2.58173084 3 42 1 Non-AA
## 43 43 16.83 2.82316303 4 14 1 Non-AA
## 44 44 11.54 2.44581914 3 27 1 Non-AA
## 45 45 4.95 1.59938753 3 20 1 AA
## 46 46 7.21 1.97546899 3 14 1 Non-AA
## 47 47 11.58 2.44927955 3 4 1 Non-AA
## 48 48 15.38 2.73306799 3 13 1 Non-AA
## 49 49 6.10 1.80828881 3 5 1 Non-AA
## 50 50 7.93 2.07065296 3 32 1 Non-AA
## 51 51 9.16 2.21484613 4 19 1 Non-AA
## 52 52 6.91 1.93296957 4 14 1 Non-AA
## 53 53 20.18 3.00469208 3 28 1 Non-AA
## 54 54 9.93 2.29556060 3 31 1 Non-AA
## 55 55 7.79 2.05284095 4 18 1 Non-AA
## 56 56 5.13 1.63510573 3 23 1 Non-AA
## 57 57 10.99 2.39698577 4 14 1 Non-AA
## 58 58 6.56 1.88099062 2 17 1 Non-AA
## 59 59 10.10 2.31253552 3 16 1 Non-AA
## 60 60 10.74 2.37397504 3 19 1 Non-AA
## 61 61 16.35 2.79422784 3 26 1 Non-AA
## 62 62 4.62 1.53039467 4 20 1 Non-AA
## 63 63 7.49 2.01356888 3 18 1 Non-AA
## 64 64 19.23 2.95647144 3 35 1 Non-AA
## 65 65 4.77 1.56234634 4 9 1 Non-AA
## 66 66 7.14 1.96571279 2 20 1 Non-AA
## 67 67 14.42 2.66861606 4 7 1 Non-AA
## 68 68 9.28 2.22786140 4 40 1 Non-AA
## 69 69 29.98 3.40053058 3 12 1 Non-AA
## 70 70 32.79 3.49012351 4 33 1 Non-AA
## 71 71 9.62 2.26384425 3 16 1 Non-AA
## 72 72 8.36 2.12345839 3 15 1 Non-AA
## 73 73 21.63 3.07408118 3 11 1 Non-AA
## 74 74 15.90 2.76631904 4 24 1 Non-AA
## 75 75 5.77 1.75267208 2 6 1 Non-AA
## 76 76 20.99 3.04404616 2 40 1 Non-AA
## 77 77 23.32 3.14931130 5 15 1 Non-AA
## 78 78 4.97 1.60341978 4 9 1 Non-AA
## 79 79 6.89 1.93007112 2 16 1 Non-AA
## 80 80 5.73 1.74571550 2 12 1 Non-AA
## 81 81 5.19 1.64673376 3 8 1 Non-AA
## 82 82 7.79 2.05284095 3 25 1 Non-AA
## 83 83 8.79 2.17361474 3 24 1 Non-AA
## 84 84 9.32 2.23216271 4 25 1 Non-AA
## 85 85 8.33 2.11986351 4 7 1 Non-AA
## 86 86 10.12 2.31451368 3 40 1 AA
## 87 87 1.98 0.68309683 3 29 0 Non-AA
## 88 88 8.24 2.10900021 4 13 1 Non-AA
## 89 89 5.05 1.61938822 4 5 1 Non-AA
## 90 90 6.55 1.87946510 3 10 1 Non-AA
## 91 91 4.47 1.49738836 3 25 1 Non-AA
## 92 92 7.96 2.07442904 3 10 1 Non-AA
## 93 93 6.25 1.83258152 2 20 1 Non-AA
## 94 94 4.13 1.41827738 3 7 1 Non-AA
## 95 95 6.73 1.90657520 2 25 1 Non-AA
## 96 96 7.69 2.03992081 3 8 1 Non-AA
## 97 97 4.35 1.47017586 2 8 1 Non-AA
## 98 98 7.69 2.03992081 3 28 1 Non-AA
## 99 99 4.81 1.57069707 2 37 1 Non-AA
## 100 100 5.24 1.65632141 3 31 1 Non-AA
## 101 101 2.14 0.76080590 4 24 1 Non-AA
## 102 102 4.49 1.50185263 4 33 1 Non-AA
## 103 103 3.61 1.28370774 4 8 1 Non-AA
## 104 104 5.45 1.69561553 3 33 1 Non-AA
## 105 105 5.00 1.60943794 3 30 1 Non-AA
## 106 106 6.73 1.90657520 3 23 1 Non-AA
## 107 107 10.05 2.30757260 3 24 1 Non-AA
## 108 108 6.54 1.87793720 4 4 1 Non-AA
## 109 109 9.20 2.21920347 3 40 1 Non-AA
## 110 110 4.62 1.53039467 3 1 1 Non-AA
## 111 111 3.37 1.21491265 3 36 1 AA
## 112 112 5.54 1.71199453 4 7 1 Non-AA
## 113 113 6.11 1.80992675 5 7 1 Non-AA
## 114 114 4.99 1.60743582 3 14 1 Non-AA
## 115 115 5.88 1.77155674 3 7 1 Non-AA
## 116 116 5.66 1.73342383 4 11 1 Non-AA
## 117 117 4.00 1.38629436 3 6 1 Non-AA
## 118 118 6.87 1.92716408 3 23 1 Non-AA
## 119 119 4.37 1.47476304 2 19 1 Non-AA
## 120 120 5.77 1.75267208 3 30 1 Non-AA
## 121 121 5.77 1.75267208 4 21 1 Non-AA
## 122 122 12.31 2.51041198 4 9 1 Non-AA
## 123 123 19.08 2.94864058 3 29 1 Non-AA
## 124 124 7.55 2.02154756 3 9 1 Non-AA
## 125 125 5.90 1.77495241 4 18 1 Non-AA
## 126 126 6.37 1.85159945 3 26 1 Non-AA
## 127 127 6.15 1.81645215 4 21 1 Non-AA
## 128 128 1.92 0.65232515 2 12 1 Non-AA
## 129 129 3.27 1.18479002 3 11 1 Non-AA
## 130 130 2.18 0.77932489 4 18 1 Non-AA
## 131 131 5.62 1.72633159 3 22 1 Non-AA
## 132 132 9.29 2.22893858 4 38 1 Non-AA
## 133 133 6.41 1.85785925 3 10 1 Non-AA
## 134 134 7.46 2.00955534 3 22 1 Non-AA
## 135 135 6.59 1.88555336 2 16 1 Non-AA
## 136 136 6.73 1.90657520 2 20 1 Non-AA
## 137 137 5.61 1.72455072 3 14 1 Non-AA
## 138 138 3.65 1.29472721 4 14 1 Non-AA
## 139 139 13.01 2.56571841 4 15 1 Non-AA
## 140 140 6.97 1.94161522 3 18 1 Non-AA
## 141 141 7.69 2.03992081 3 21 1 Non-AA
## 142 142 6.73 1.90657520 3 15 1 Non-AA
## 143 143 5.13 1.63510573 3 10 1 Non-AA
## 144 144 2.14 0.76080590 4 6 1 Non-AA
## 145 145 4.53 1.51072204 2 8 1 Non-AA
## 146 146 9.10 2.20827436 2 18 1 Non-AA
## 147 147 9.78 2.28033948 3 15 1 Non-AA
## 148 148 3.63 1.28923273 4 11 1 Non-AA
## 149 149 7.21 1.97546899 3 20 0 AA
## 150 150 10.62 2.36273909 3 18 1 Non-AA
## 151 151 4.62 1.53039467 3 10 1 Non-AA
## 152 152 6.54 1.87793720 3 26 1 Non-AA
## 153 153 12.12 2.49485707 3 35 0 Non-AA
## 154 154 7.21 1.97546899 4 30 1 Non-AA
## 155 155 6.25 1.83258152 4 6 0 Non-AA
## 156 156 3.85 1.34807312 3 13 1 Non-AA
## 157 157 3.03 1.10856259 3 10 0 Non-AA
## 158 158 2.01 0.69813472 3 16 1 Non-AA
## 159 159 3.90 1.36097658 4 5 1 Non-AA
## 160 160 6.35 1.84845483 3 11 1 Non-AA
## 161 161 2.00 0.69314718 4 6 1 Non-AA
## 162 162 7.69 2.03992081 3 22 1 Non-AA
## 163 163 2.63 0.96698391 3 4 1 Non-AA
## 164 164 5.00 1.60943794 4 15 1 Non-AA
## 165 165 6.54 1.87793720 3 7 1 Non-AA
## 166 166 5.19 1.64673376 3 10 1 Non-AA
## 167 167 9.75 2.27726722 3 8 1 Non-AA
## 168 168 2.81 1.03318441 3 14 1 AA
## 169 169 4.62 1.53039467 3 11 1 Non-AA
## 170 170 7.33 1.99197555 3 34 1 Non-AA
## 171 171 7.69 2.03992081 4 12 1 Non-AA
## 172 172 3.26 1.18172717 3 10 1 Non-AA
## 173 173 5.77 1.75267208 2 11 1 Non-AA
## 174 174 5.59 1.72097933 4 8 1 Non-AA
## 175 175 5.13 1.63510573 3 28 1 AA
## 176 176 14.74 2.69056487 2 24 1 Non-AA
## 177 177 3.67 1.30019164 3 8 1 Non-AA
## 178 178 9.73 2.27521396 4 13 1 Non-AA
## 179 179 3.46 1.24126863 3 4 1 Non-AA
## 180 180 6.37 1.85159945 4 43 1 Non-AA
## 181 181 6.58 1.88403475 3 30 1 Non-AA
## 182 182 10.15 2.31747365 3 16 1 AA
## 183 183 9.23 2.22245908 3 19 1 Non-AA
## 184 184 6.75 1.90954256 3 21 0 Non-AA
## 185 185 10.22 2.32434654 3 26 1 Non-AA
## 186 186 4.76 1.56024766 3 8 1 Non-AA
## 187 187 1.76 0.56531382 3 24 1 Non-AA
## 188 188 12.73 2.54396129 4 15 1 Non-AA
## 189 189 5.85 1.76644170 3 26 1 Non-AA
## 190 190 3.17 1.15373158 3 6 0 Non-AA
## 191 191 5.99 1.79009140 2 19 1 Non-AA
## 192 192 4.62 1.53039467 3 4 1 Non-AA
## 193 193 1.80 0.58778661 4 23 1 Non-AA
## 194 194 5.95 1.78339124 4 25 1 Non-AA
## 195 195 7.46 2.00955534 3 18 1 Non-AA
## 196 196 8.70 2.16332293 2 27 1 Non-AA
## 197 197 3.75 1.32175589 3 16 1 Non-AA
## 198 198 7.69 2.03992081 3 20 1 Non-AA
## 199 199 5.24 1.65632141 3 12 0 Non-AA
## 200 200 10.10 2.31253552 4 35 1 Non-AA
## 201 201 3.75 1.32175589 1 36 1 Non-AA
## 202 202 5.79 1.75613225 3 35 1 Non-AA
## 203 203 5.06 1.62136650 3 5 1 Non-AA
## 204 204 7.96 2.07442904 4 15 1 Non-AA
## 205 205 12.82 2.55100632 3 44 1 Non-AA
## 206 206 8.46 2.13534927 3 28 1 Non-AA
## 207 207 3.76 1.32441890 4 2 1 Non-AA
## 208 208 2.86 1.05082154 3 43 1 Non-AA
## 209 209 11.81 2.46894670 3 30 1 Non-AA
## 210 210 7.69 2.03992081 3 26 1 Non-AA
## 211 211 7.21 1.97546899 3 40 0 Non-AA
## 212 212 2.88 1.05779028 4 7 1 AA
## 213 213 5.73 1.74571550 3 10 1 Non-AA
## 214 214 4.49 1.50185263 2 37 1 Non-AA
## 215 215 9.57 2.25863314 4 27 1 Non-AA
## 216 216 3.61 1.28370774 3 5 1 Non-AA
## 217 217 7.77 2.05027008 2 14 1 Non-AA
## 218 218 9.23 2.22245908 2 16 1 Non-AA
## 219 219 5.58 1.71918881 3 4 1 Non-AA
## 220 220 5.34 1.67522573 3 39 1 Non-AA
## 221 221 5.17 1.64287269 3 32 1 Non-AA
## 222 222 6.41 1.85785925 4 16 1 Non-AA
## 223 223 7.40 2.00148010 4 10 1 Non-AA
## 224 224 6.75 1.90954256 4 15 1 Non-AA
## 225 225 8.46 2.13534927 4 10 1 Non-AA
## 226 226 4.61 1.52822793 3 10 1 Non-AA
## 227 227 8.61 2.15292430 3 12 0 Non-AA
## 228 228 5.24 1.65632141 4 4 1 Non-AA
## 229 229 15.38 2.73306799 3 12 1 Non-AA
## 230 230 2.37 0.86288989 4 15 1 Non-AA
## 231 231 6.72 1.90508807 4 37 1 Non-AA
## 232 232 9.40 2.24070954 4 23 1 Non-AA
## 233 233 7.84 2.05923891 3 15 1 Non-AA
## 234 234 15.00 2.70805025 4 10 1 Non-AA
## 235 235 4.70 1.54756248 4 14 1 Non-AA
## 236 236 11.28 2.42303133 3 17 1 Non-AA
## 237 237 11.97 2.48240352 3 24 1 Non-AA
## 238 238 13.99 2.63834286 4 13 1 Non-AA
## 239 239 3.00 1.09861231 3 5 1 Non-AA
## 240 240 3.41 1.22671235 3 20 1 Non-AA
## 241 241 9.62 2.26384425 4 29 1 Non-AA
## 242 242 6.73 1.90657520 4 10 1 Non-AA
## 243 243 9.62 2.26384425 3 36 1 Non-AA
## 244 244 5.19 1.64673376 3 15 1 Non-AA
## 245 245 4.62 1.53039467 3 26 1 Non-AA
## 246 246 5.03 1.61541998 4 13 1 Non-AA
## 247 247 9.84 2.28645563 3 26 1 Non-AA
## 248 248 5.29 1.66581821 3 9 1 Non-AA
## 249 249 10.99 2.39698577 1 40 1 Non-AA
## 250 250 7.08 1.95727384 3 28 0 Non-AA
## 251 251 8.17 2.10046887 2 12 1 Non-AA
## 252 252 5.88 1.77155674 4 25 1 Non-AA
## 253 253 7.92 2.06939125 4 18 1 Non-AA
## 254 254 4.44 1.49065435 3 9 1 Non-AA
## 255 255 6.64 1.89311194 3 9 1 Non-AA
## 256 256 15.80 2.76001000 4 30 0 Non-AA
## 257 257 9.23 2.22245908 3 40 1 Non-AA
## 258 258 12.82 2.55100632 3 36 1 Non-AA
## 259 259 14.18 2.65183258 4 30 1 Non-AA
## 260 260 11.54 2.44581914 3 27 1 Non-AA
## 261 261 9.40 2.24070954 3 22 1 Non-AA
## 262 262 10.39 2.34084392 3 40 1 Non-AA
## 263 263 5.54 1.71199453 3 40 1 AA
## 264 264 7.86 2.06178665 3 21 1 Non-AA
## 265 265 8.17 2.10046887 3 36 0 Non-AA
## 266 266 4.05 1.39871693 4 16 1 Non-AA
## 267 267 7.81 2.05540490 3 36 0 Non-AA
## 268 268 2.56 0.94000721 3 18 1 Non-AA
## 269 269 12.98 2.56340957 4 11 1 Non-AA
## 270 270 41.67 3.72978139 4 16 0 Non-AA
## 271 271 4.81 1.57069707 4 12 1 Non-AA
## 272 272 1.32 0.27763179 3 2 1 Non-AA
## 273 273 3.71 1.31103194 3 4 1 Non-AA
## 274 274 4.81 1.57069707 3 21 1 Non-AA
## 275 275 4.68 1.54329813 3 4 1 Non-AA
## 276 276 8.21 2.10535288 2 32 1 Non-AA
## 277 277 4.03 1.39376640 3 7 1 Non-AA
## 278 278 4.81 1.57069707 3 5 0 Non-AA
## 279 279 15.63 2.74919224 3 23 1 Non-AA
## 280 280 7.69 2.03992081 3 23 1 Non-AA
## 281 281 7.17 1.96990561 3 14 1 Non-AA
## 282 282 8.08 2.08939195 4 13 1 Non-AA
## 283 283 9.03 2.20055223 3 28 1 Non-AA
## 284 284 6.75 1.90954256 4 35 1 Non-AA
## 285 285 8.44 2.13298225 4 16 1 Non-AA
## 286 286 6.00 1.79175949 4 12 1 Non-AA
## 287 287 6.15 1.81645215 3 7 1 Non-AA
## 288 288 4.10 1.41098690 3 25 1 Non-AA
## 289 289 14.17 2.65112710 3 22 1 Non-AA
## 290 290 6.41 1.85785925 3 22 1 Non-AA
## 291 291 31.09 3.43688631 3 32 0 Non-AA
## 292 292 10.29 2.33117247 4 35 1 Non-AA
## 293 293 5.06 1.62136650 3 28 1 Non-AA
## 294 294 6.37 1.85159945 3 18 1 Non-AA
## 295 295 2.98 1.09192336 3 11 1 Non-AA
## 296 296 13.28 2.58625913 3 40 1 Non-AA
## 297 297 4.20 1.43508446 2 10 1 Non-AA
## 298 298 8.92 2.18829584 3 40 1 Non-AA
## 299 299 2.63 0.96698391 3 7 1 Non-AA
## 300 300 26.24 3.26728487 3 24 1 Non-AA
## 301 301 7.54 2.02022219 4 36 1 Non-AA
## 302 302 8.65 2.15755939 3 34 1 Non-AA
## 303 303 20.99 3.04404616 3 44 1 Non-AA
## 304 304 10.26 2.32825279 3 44 1 Non-AA
## 305 305 8.55 2.14593124 3 30 1 Non-AA
## 306 306 2.08 0.73236787 4 23 1 Non-AA
## 307 307 3.00 1.09861231 3 5 0 Non-AA
## 308 308 6.01 1.79342484 4 25 1 Non-AA
## 309 309 10.05 2.30757260 4 14 1 Non-AA
## 310 310 5.73 1.74571550 3 5 1 Non-AA
## 311 311 5.93 1.78002417 3 20 1 Non-AA
## 312 312 4.49 1.50185263 2 1 1 Non-AA
## 313 313 5.66 1.73342383 4 13 1 Non-AA
## 314 314 10.19 2.32140684 4 15 1 Non-AA
## 315 315 20.99 3.04404616 3 35 1 Non-AA
## 316 316 9.05 2.20276475 4 20 1 Non-AA
## 317 317 3.92 1.36609173 5 12 1 Non-AA
## 318 318 4.28 1.45395303 4 7 1 Non-AA
## 319 319 2.75 1.01160097 3 7 1 Non-AA
## 320 320 2.88 1.05779028 4 32 1 Non-AA
## 321 321 6.41 1.85785925 3 13 1 Non-AA
## 322 322 7.69 2.03992081 3 15 1 Non-AA
## 323 323 3.75 1.32175589 3 33 0 Non-AA
## 324 324 6.41 1.85785925 3 48 1 Non-AA
## 325 325 11.54 2.44581914 4 38 1 Non-AA
## 326 326 11.40 2.43361330 3 28 1 Non-AA
## 327 327 2.20 0.78845739 4 18 1 Non-AA
## 328 328 1.65 0.50077528 1 24 1 Non-AA
## 329 329 6.59 1.88555336 3 27 1 Non-AA
## 330 330 11.06 2.40333509 3 24 1 Non-AA
## 331 331 1.63 0.48858002 3 16 1 Non-AA
## 332 332 3.58 1.27536273 3 44 1 Non-AA
## 333 333 12.31 2.51041198 4 14 1 Non-AA
## 334 334 6.66 1.89611948 3 16 1 Non-AA
## 335 335 10.53 2.35422826 3 18 1 Non-AA
## 336 336 1.92 0.65232515 2 18 1 Non-AA
## 337 337 19.23 2.95647144 4 15 1 Non-AA
## 338 338 7.21 1.97546899 4 41 1 Non-AA
## 339 339 25.64 3.24415350 3 28 1 Non-AA
## 340 340 3.85 1.34807312 3 24 1 Non-AA
## 341 341 10.99 2.39698577 3 13 1 Non-AA
## 342 342 10.02 2.30458307 3 22 1 Non-AA
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## 348 348 3.85 1.34807312 4 30 1 Non-AA
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## 361 361 14.35 2.66374993 3 23 1 Non-AA
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## 364 364 10.66 2.36649847 5 8 1 Non-AA
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## 386 386 5.10 1.62924051 4 34 0 AA
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## 390 390 1.92 0.65232515 3 25 1 AA
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## 410 410 3.16 1.15057206 3 13 1 Non-AA
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## 414 414 5.92 1.77833641 4 11 1 Non-AA
## 415 415 13.12 2.57413769 3 40 1 Non-AA
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## 496 496 4.81 1.57069707 3 5 1 Non-AA
## 497 497 4.81 1.57069707 4 3 1 Non-AA
## 498 498 7.07 1.95586050 3 8 1 AA
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## 513 513 5.09 1.62727785 5 7 1 Non-AA
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## 525 525 8.44 2.13298225 4 19 1 Non-AA
## 526 526 7.96 2.07442904 3 30 1 Non-AA
## 527 527 7.21 1.97546899 4 38 0 AA
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## 531 531 1.92 0.65232515 3 2 1 Non-AA
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## 535 535 3.81 1.33762920 5 4 1 Non-AA
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## 537 537 3.08 1.12492955 3 5 1 AA
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## 544 544 4.17 1.42791605 4 34 1 Non-AA
## 545 545 3.34 1.20597076 2 20 1 AA
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## 549 549 7.80 2.05412364 3 6 1 AA
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## 552 552 4.52 1.50851202 3 7 1 Non-AA
## 553 553 4.47 1.49738836 3 12 1 Non-AA
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## 561 561 3.92 1.36609173 2 15 1 Non-AA
## 562 562 3.46 1.24126863 4 13 1 Non-AA
## 563 563 4.95 1.59938753 5 10 1 AA
## 564 564 2.18 0.77932489 3 2 1 Non-AA
## 565 565 4.10 1.41098690 4 23 1 Non-AA
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## 567 567 6.59 1.88555336 3 30 1 Non-AA
## 568 568 3.96 1.37624407 2 15 1 Non-AA
## 569 569 4.81 1.57069707 3 11 1 Non-AA
## 570 570 3.75 1.32175589 3 27 1 Non-AA
## 571 571 7.34 1.99333882 3 9 1 Non-AA
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## 575 575 3.75 1.32175589 3 3 1 Non-AA
## 576 576 5.45 1.69561553 4 19 1 Non-AA
## 577 577 4.09 1.40854502 4 18 0 Non-AA
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## 587 587 3.85 1.34807312 3 22 1 Non-AA
## 588 588 7.15 1.96711242 4 30 1 Non-AA
## 589 589 4.05 1.39871693 3 3 1 AA
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## 592 592 4.42 1.48613966 3 9 1 Non-AA
## 593 593 8.49 2.13888907 3 7 1 Non-AA
## 594 594 4.28 1.45395303 3 3 1 AA
## 595 595 3.33 1.20297229 4 5 1 Non-AA
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## 599 599 1.98 0.68309683 3 5 1 AA
## 600 600 2.57 0.94390589 3 3 1 Non-AA
## 601 601 2.58 0.94778937 4 6 1 AA
## 602 602 2.51 0.92028272 4 10 1 Non-AA
## 603 603 77.72 4.35311270 4 9 1 AA
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## 605 605 4.09 1.40854502 3 33 1 Non-AA
## 606 606 5.73 1.74571550 3 12 1 Non-AA
## 607 607 4.81 1.57069707 3 11 1 Non-AA
## 608 608 7.69 2.03992081 4 10 1 Non-AA
## 609 609 5.73 1.74571550 3 9 1 Non-AA
## 610 610 5.88 1.77155674 3 17 1 Non-AA
## 611 611 4.27 1.45161378 3 37 1 Non-AA
## 612 612 9.62 2.26384425 4 28 1 Non-AA
## 613 613 9.36 2.23644519 3 16 1 Non-AA
## 614 614 3.95 1.37371564 5 2 1 Non-AA
## 615 615 5.73 1.74571550 3 34 1 Non-AA
## 616 616 2.60 0.95551139 3 7 1 Non-AA
## 617 617 3.75 1.32175589 4 4 1 Non-AA
## 618 618 5.61 1.72455072 4 12 1 Non-AA
## 619 619 5.73 1.74571550 3 20 1 Non-AA
## 620 620 3.85 1.34807312 3 17 1 Non-AA
## 621 621 2.88 1.05779028 2 0 1 Non-AA
## 622 622 2.75 1.01160097 4 7 1 Non-AA
## 623 623 11.57 2.44841552 4 13 1 Non-AA
## 624 624 4.33 1.46556747 3 4 1 AA
## 625 625 2.75 1.01160097 3 3 1 Non-AA
## 626 626 5.34 1.67522573 3 5 1 Non-AA
## 627 627 6.56 1.88099062 3 19 1 Non-AA
## 628 628 4.68 1.54329813 3 12 1 Non-AA
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## 630 630 7.96 2.07442904 3 18 1 Non-AA
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## 633 633 6.25 1.83258152 3 5 1 Non-AA
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## 635 635 3.75 1.32175589 4 12 1 Non-AA
## 636 636 3.75 1.32175589 4 6 1 Non-AA
## 637 637 6.54 1.87793720 4 13 1 AA
## 638 638 4.19 1.43270075 3 10 1 Non-AA
## 639 639 5.37 1.68082786 2 20 1 Non-AA
## 640 640 2.14 0.76080590 5 32 1 Non-AA
## 641 641 4.81 1.57069707 3 32 1 Non-AA
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## 650 650 10.12 2.31451368 3 29 1 Non-AA
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## 655 655 8.24 2.10900021 2 20 1 Non-AA
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## 657 657 7.96 2.07442904 3 36 1 Non-AA
## 658 658 8.65 2.15755939 4 11 1 Non-AA
## 659 659 4.27 1.45161378 3 3 1 AA
## 660 660 12.66 2.53844738 5 22 1 Non-AA
## 661 661 7.96 2.07442904 3 16 1 Non-AA
## 662 662 3.49 1.24990177 3 44 1 Non-AA
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## 666 666 2.75 1.01160097 4 2 1 Non-AA
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## 680 680 7.96 2.07442904 3 6 1 Non-AA
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## 693 693 5.55 1.71379793 3 41 1 Non-AA
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## 696 696 8.17 2.10046887 3 12 1 Non-AA
## 697 697 6.40 1.85629797 3 26 1 Non-AA
## 698 698 3.57 1.27256560 3 19 1 AA
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## 700 700 7.69 2.03992081 3 27 1 Non-AA
## 701 701 10.26 2.32825279 3 27 1 AA
## 702 702 7.08 1.95727384 3 25 1 Non-AA
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## 704 704 8.08 2.08939195 3 7 1 Non-AA
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## 720 720 4.73 1.55392516 2 21 1 Non-AA
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## 727 727 3.75 1.32175589 3 9 1 Non-AA
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## 732 732 7.50 2.01490307 4 35 1 Non-AA
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## 735 735 4.16 1.42551506 3 32 1 Non-AA
## 736 736 8.17 2.10046887 3 23 1 Non-AA
## 737 737 8.12 2.09433007 3 22 1 Non-AA
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## 739 739 12.50 2.52572870 3 30 1 Non-AA
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## 744 744 9.28 2.22786140 3 29 1 Non-AA
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## 746 746 7.21 1.97546899 3 34 1 AA
## 747 747 8.00 2.07944155 3 18 1 Non-AA
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## 1005 1005 10.54 2.35517764 3 35 1 AA
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## 1109 1109 26.24 3.26728487 2 37 1 Non-AA
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## 1121 1121 8.65 2.15755939 4 5 1 Non-AA
## 1122 1122 2.08 0.73236787 4 1 1 Non-AA
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## 1260 Female 0 576 16Data Transformation I have modified the data, so that I vsisual if there’s a sex difference (between male to Females) in the data . Furthermore I wish to compare samples that identify as African American(AA)/Identify as Black to those that do no identify as AA/Black. It has been studied that Black and Brown American citizens make less than their white counterparts, and experience more finical insecurity. Paper have also studied that “beauty” give people privileges that can affect economic status. India and China have been reported by their own citizens, to be countries where darker skin can lead to less favorable results when seeking education , employment and social mobility. Seeing that America is more heterogeneous, it is interesting to see if nonwhite citizens are perceive as less attractive and that in compund with America’s racial history plays a role in Income.
Question:Does Attractiveness, Sex(biological) and/or Race an play a role in Income differences My hypothesis is that I do believe subjective values like Attractiveness and Race play a role in wage Income because it has been reported that African-American/Black(AA)males make 20% less in annual income than their white(nonAA) counterparts .Plus, AA women make less than both AA and nonAA men. I ,also, believe that there are sex differences within and between the racial groups . I hypothesize the men in both groups will earn more. ## Including Plots
You can also embed plots, for example:
library(ggplot2)
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, unionprint(ggplot(data1,aes(x= exper, y= wage,group=1))+
geom_boxplot())+
labs(title = "Distribution of Wage in relation to Work experience ")
h<- ggplot(data1,aes(x= exper, y= wage,group=1))
print(h+geom_point())+
labs(title = "Distribution of Wage in relation to Work experience ")
wag<- ggplot(data = Beauty2Pwr, mapping = aes(x=Wages))+
labs(title = "Distribution of Wage ")
print(wag+geom_histogram(binwidth = 2))
#1identify if wages are dependent on sex
#0 identify if wages are dependent on attractive score and see if theres a sex difference
#2nd identify if wages are dependent Race and see if there is a sex difference by sex
ggplot(data=Beauty2Pwr, aes(x= Sex_bio, y= Wages))+
geom_point()
Question1= ggplot(data=Beauty2Pwr, mapping = aes(x= Sex_bio, y= Wages))+
geom_point(alpha= 0.25)
print(Question1)
Question0= ggplot(data= Beauty2Pwr, mapping = aes(x= Attractive, y= Wages))+
geom_point()+
labs(x="Attractive Scale(DisagreetoAgree)", y="Wage",title = "Does Beauty affect Income")
print(Question0)
Question0_sex= ggplot(data= Beauty2Pwr, mapping = aes(x= Attractive, y= Wages, colour= Sex_bio))+
geom_point(alpha=.8)+
facet_wrap(~Sex_bio)+
labs(x="Attractive Scale(DisagreetoAgree)", y="Wage",title = "Is there a sex difference in the potential of Beauty to affect Income ")
print(Question0_sex)
print(Question0)
Question0box= ggplot(data= Beauty2Pwr, mapping = aes(x= Attractive, y=Wages, group=1))+
geom_boxplot()
print(Question0box)
Race0= ggplot(data= Beauty2Pwr, mapping = aes(x= Black_NotBlack, y= Wages, colour= Sex_bio))+
geom_boxplot()+
facet_wrap(~Black_NotBlack)+
labs(x="Race", y="Wage",title = "Is there Racial Discrimination in Income")
print(Race0)
Race1= ggplot(data= Beauty2Pwr, mapping = aes(x= Attractive, y= Wages, colour= Sex_bio))+
geom_point(alpha= 0.3)+
facet_wrap(~Black_NotBlack)+
labs(x="Attractive Scale(DisagreetoAgree)", y="Wage",title = "Is there Discrimination in Income")
print(Race1)
Raceline= ggplot(data=Beauty2Pwr, aes(Sex_bio, Log_Wages, colour=Black_NotBlack))+
geom_point(size=0.5)+
geom_smooth(method = 1, se=F)+
labs(x="Sex", y="Wage",title = "Is there a linear relatonship between sex differences and Income ")
print(Question0_sex)
print(Raceline)## `geom_smooth()` using formula = 'y ~ x'
Eduline= ggplot(data=Beauty2Pwr, aes(Edu_yrs, Log_Wages, colour=Sex_bio))+
geom_point(size=0.5)+
geom_smooth()+
labs(x="Education(yrs)", y="Wage",title = "Is there a linear relatonship between Education and Income ")
print(Eduline)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 12## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.9551e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 12## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 7.9551e-16
library(ggplot2)
library(ggpubr)
Figure1= ggline(data= Beauty2Pwr, x = "Sex_bio", y = "Wages", color = "Black_NotBlack",
add = c("mean_se", "dotplot"),
title=("Interaction between Sex and Race"),
palette = c("#00AFBB", "#E7B800"))
library("ggpubr")
Figure2= ggline(data= Beauty2Pwr, x = "Edu_yrs", y = "Wages", color = "Black_NotBlack",
add = c("mean_se", "dotplot"),
title=("Interaction between Education and Race"),
palette = c("#00AFBB", "#E7B800"))
two.way<- aov(Wages~Black_NotBlack+Edu_yrs, data=Beauty2Pwr)
print(two.way)## Call:
## aov(formula = Wages ~ Black_NotBlack + Edu_yrs, data = Beauty2Pwr)
##
## Terms:
## Black_NotBlack Edu_yrs Residuals
## Sum of Squares 95.363 1162.222 26089.854
## Deg. of Freedom 1 1 1257
##
## Residual standard error: 4.555837
## Estimated effects may be unbalancedsummary(two.way)## Df Sum Sq Mean Sq F value Pr(>F)
## Black_NotBlack 1 95 95.4 4.595 0.0323 *
## Edu_yrs 1 1162 1162.2 55.995 1.36e-13 ***
## Residuals 1257 26090 20.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(two.way))## Df Sum Sq Mean Sq F value Pr(>F)
## Black_NotBlack 1 95 95.4 4.595 0.0323 *
## Edu_yrs 1 1162 1162.2 55.995 1.36e-13 ***
## Residuals 1257 26090 20.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(Figure1)## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
print(Figure2)## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
Conclusion: Based on the first graphs, Attractiveness was normally distributed in the orginal data and between the sex.We did see that non-AA(non AfricanAmerican/Black) males earned higher wages in compared to non-AA women and AA men and AA women even when they had the same attractiveness score. The data for attractiveness for each group was distributed normally so samples above and samples below the mean were symmetrical. The AA population did not have a large number of males, which leads to the potential for more error and posed difficulty when analyzing comparison.
Furthermore, the group with the highest raw number of representatives was non-AA males. Since Attractive was pretty normal, I looked for another variable that could explain Income differences. We included graphs that utilize the log of the Wage data in order to run better linear analysis.
We see from the two way Anova that Education and Race are statistically significant when it comes to Wage. Plus, these two variables interact. Thus the effect of Education and Race (independent variables) on Wage income (dependent variables) is not a constant value. There are differences in the impact of Education and Race at different values on wage. There is no straightforward, main value that can predict Wage.
I accept the Null Hypothesis that values like Race and Sex(biological definition) play a role in Wage differences. These values are not as independent of each other as assumed.
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.