# Clear environment of variables and functions
rm(list = ls(all = TRUE))
# Clear environmet of packages
if(is.null(sessionInfo()$otherPkgs) == FALSE)lapply(paste("package:", names(sessionInfo()$otherPkgs), sep=""), detach, character.only = TRUE, unload = TRUE)Load Package and Data
Load Package
#load package
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library(formattable)# For table formatting and table formatting functions
library(htmltools)
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## - the `gifski` package for gif output
## - the `av` package for video output
## and restarting the R sessionlibrary(inspectdf)# Load auto EDA packagesData Loading and Cleaning
#Import Data
df <- read.csv("psam_p53.csv", header=TRUE) #Look at the first 10 rows of data
head(df,10)## RT SERIALNO DIVISION SPORDER PUMA REGION ST ADJINC PWGTP AGEP CIT CITWP
## 1 P 2.017e+12 9 1 11603 4 53 1011189 126 47 1 NA
## 2 P 2.017e+12 9 1 11200 4 53 1011189 126 35 1 NA
## 3 P 2.017e+12 9 2 11200 4 53 1011189 99 38 1 NA
## 4 P 2.017e+12 9 3 11200 4 53 1011189 120 14 1 NA
## 5 P 2.017e+12 9 4 11200 4 53 1011189 144 6 1 NA
## 6 P 2.017e+12 9 1 11702 4 53 1011189 46 71 1 NA
## 7 P 2.017e+12 9 2 11702 4 53 1011189 135 65 4 1990
## 8 P 2.017e+12 9 1 10100 4 53 1011189 53 45 1 NA
## 9 P 2.017e+12 9 2 10100 4 53 1011189 57 12 1 NA
## 10 P 2.017e+12 9 3 10100 4 53 1011189 57 9 1 NA
## COW DDRS DEAR DEYE DOUT DPHY DRAT DRATX DREM ENG FER GCL GCM GCR HINS1 HINS2
## 1 1 2 2 2 2 2 NA NA 2 NA NA 2 NA NA 1 2
## 2 2 2 2 2 2 2 NA NA 2 NA 2 2 NA NA 1 2
## 3 1 2 2 2 2 2 NA NA 2 NA NA 2 NA NA 1 2
## 4 NA 1 2 2 NA 2 NA NA 1 NA NA NA NA NA 1 2
## 5 NA 2 2 2 NA 2 NA NA 2 NA NA NA NA NA 1 2
## 6 7 2 2 2 2 2 NA 2 2 NA NA 2 NA NA 2 2
## 7 5 2 2 2 2 2 NA NA 2 1 NA 2 NA NA 1 2
## 8 1 2 2 2 2 2 NA NA 2 NA 2 2 NA NA 1 2
## 9 NA 2 2 2 NA 2 NA NA 2 NA NA NA NA NA 1 2
## 10 NA 2 2 2 NA 2 NA NA 2 NA NA NA NA NA 1 2
## HINS3 HINS4 HINS5 HINS6 HINS7 INTP JWMNP JWRIP JWTR LANX MAR MARHD MARHM
## 1 2 2 2 2 2 0 20 NA 12 2 5 NA NA
## 2 2 2 2 2 2 0 20 1 1 2 1 2 2
## 3 2 2 2 2 2 0 15 1 1 2 1 2 2
## 4 2 2 2 2 2 NA NA NA NA 2 5 NA NA
## 5 2 2 2 2 2 NA NA NA NA 2 5 NA NA
## 6 1 2 2 2 2 1800 60 1 1 2 1 2 2
## 7 1 2 2 2 2 2400 NA NA NA 1 1 2 2
## 8 2 2 2 2 2 0 5 1 1 2 3 2 2
## 9 2 2 2 2 2 NA NA NA NA 2 5 NA NA
## 10 2 2 2 2 2 NA NA NA NA 2 5 NA NA
## MARHT MARHW MARHYP MIG MIL MLPA MLPB MLPCD MLPE MLPFG MLPH MLPI MLPJ MLPK
## 1 NA NA NA 1 4 NA NA NA NA NA NA NA NA NA
## 2 1 2 2002 1 4 NA NA NA NA NA NA NA NA NA
## 3 1 2 2002 1 4 NA NA NA NA NA NA NA NA NA
## 4 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 6 1 2 1985 1 2 0 0 0 1 0 0 0 0 0
## 7 1 2 1985 1 4 NA NA NA NA NA NA NA NA NA
## 8 1 2 1999 1 4 NA NA NA NA NA NA NA NA NA
## 9 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## NWAB NWAV NWLA NWLK NWRE OIP PAP RELP RETP SCH SCHG SCHL SEMP SEX SSIP
## 1 3 5 3 3 3 0 0 0 0 1 NA 21 0 1 0
## 2 3 5 3 3 3 0 0 0 0 1 NA 21 0 2 0
## 3 3 5 3 3 3 0 0 1 0 1 NA 20 14400 1 0
## 4 NA NA NA NA NA NA NA 2 NA 3 8 8 NA 1 NA
## 5 NA NA NA NA NA NA NA 2 NA 3 3 3 NA 2 NA
## 6 3 5 3 3 3 0 0 0 56000 1 NA 21 0 1 0
## 7 3 5 2 2 3 7000 0 1 0 1 NA 21 0 2 0
## 8 3 5 3 3 3 17000 0 0 0 1 NA 21 0 2 0
## 9 NA NA NA NA NA NA NA 2 NA 2 9 10 NA 1 NA
## 10 NA NA NA NA NA NA NA 2 NA 2 6 7 NA 1 NA
## SSP WAGP WKHP WKL WKW WRK YOEP ANC ANC1P ANC2P DECADE DIS DRIVESP ESP
## 1 0 90000 40 1 1 1 NA 2 32 142 NA 2 NA NA
## 2 0 65000 30 1 1 1 NA 2 82 22 NA 2 1 NA
## 3 0 101000 50 1 1 1 NA 2 50 22 NA 2 1 NA
## 4 NA NA NA NA NA NA NA 2 22 32 NA 1 NA 1
## 5 NA NA NA NA NA NA NA 2 22 32 NA 2 NA 1
## 6 19500 28000 38 1 1 1 NA 1 902 999 NA 2 1 NA
## 7 14100 5100 80 1 6 2 1985 1 720 999 5 2 NA NA
## 8 0 44000 40 1 1 1 NA 2 50 32 NA 2 1 NA
## 9 NA NA NA NA NA NA NA 2 50 32 NA 2 NA 7
## 10 NA NA NA NA NA NA NA 2 50 32 NA 2 NA 7
## ESR FOD1P FOD2P HICOV HISP INDP JWAP JWDP LANP MIGPUMA MIGSP MSP NAICSP
## 1 1 2408 NA 1 1 3380 86 43 NA NA NA 6 3345
## 2 1 6107 NA 1 1 8190 224 130 NA NA NA 1 622
## 3 1 NA NA 1 1 6690 91 49 NA NA NA 1 517Z
## 4 NA NA NA 1 1 NA NA NA NA NA NA NA
## 5 NA NA NA 1 1 NA NA NA NA NA NA NA
## 6 1 6201 6402 1 1 7280 100 49 NA NA NA 1 5412
## 7 6 2309 NA 1 1 6370 NA NA 2910 NA NA 1 491
## 8 1 6402 4001 1 1 8090 83 43 NA NA NA 4 6214
## 9 NA NA NA 1 1 NA NA NA NA NA NA NA
## 10 NA NA NA 1 1 NA NA NA NA NA NA NA
## NATIVITY NOP OC OCCP PAOC PERNP PINCP POBP POVPIP POWPUMA POWSP PRIVCOV
## 1 1 NA 0 1530 NA 90000 90000 55 501 40100 55 1
## 2 1 NA 0 3255 2 65000 65000 53 501 11200 53 1
## 3 1 NA 0 4930 NA 115400 115400 53 501 11200 53 1
## 4 1 1 1 NA NA NA NA 53 501 NA NA 1
## 5 1 1 1 NA NA NA NA 53 501 NA NA 1
## 6 1 NA 0 800 NA 28000 105300 42 501 11700 53 2
## 7 2 NA 0 5560 4 5100 28600 233 501 NA NA 1
## 8 1 NA 0 5110 2 44000 61000 53 313 10100 53 1
## 9 1 7 1 NA NA NA NA 53 313 NA NA 1
## 10 1 7 1 NA NA NA NA 53 313 NA NA 1
## PUBCOV QTRBIR RAC1P RAC2P RAC3P RACAIAN RACASN RACBLK RACNH RACNUM RACPI
## 1 2 2 1 1 1 0 0 0 0 1 0
## 2 2 3 1 1 1 0 0 0 0 1 0
## 3 2 3 1 1 1 0 0 0 0 1 0
## 4 2 1 1 1 1 0 0 0 0 1 0
## 5 2 2 1 1 1 0 0 0 0 1 0
## 6 1 4 2 2 2 0 0 1 0 1 0
## 7 1 3 6 45 6 0 1 0 0 1 0
## 8 2 2 1 1 1 0 0 0 0 1 0
## 9 2 2 1 1 1 0 0 0 0 1 0
## 10 2 2 1 1 1 0 0 0 0 1 0
## RACSOR RACWHT RC SCIENGP SCIENGRLP SFN SFR SOCP VPS WAOB FAGEP FANCP FCITP
## 1 0 1 0 1 2 NA NA 1721YY NA 1 0 0 0
## 2 0 1 0 2 1 NA NA 291141 NA 1 0 0 0
## 3 0 1 0 NA NA NA NA 419031 NA 1 0 0 0
## 4 0 1 1 NA NA NA NA NA 1 0 0 0
## 5 0 1 1 NA NA NA NA NA 1 0 0 0
## 6 0 0 0 2 2 NA NA 132011 6 1 0 0 0
## 7 0 0 0 2 2 NA NA 435053 NA 4 0 0 0
## 8 0 1 0 1 2 NA NA 433021 NA 1 0 0 0
## 9 0 1 1 NA NA NA NA NA 1 0 0 0
## 10 0 1 1 NA NA NA NA NA 1 0 0 0
## FCITWP FCOWP FDDRSP FDEARP FDEYEP FDISP FDOUTP FDPHYP FDRATP FDRATXP FDREMP
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## FENGP FESRP FFERP FFODP FGCLP FGCMP FGCRP FHICOVP FHINS1P FHINS2P FHINS3C
## 1 0 0 0 0 0 0 0 0 0 0 NA
## 2 0 0 0 0 0 0 0 0 0 0 NA
## 3 0 0 0 0 0 0 0 0 0 0 NA
## 4 0 0 0 0 0 0 0 0 0 0 NA
## 5 0 0 0 0 0 0 0 0 0 0 NA
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 NA
## 9 0 0 0 0 0 0 0 0 0 0 NA
## 10 0 0 0 0 0 0 0 0 0 0 NA
## FHINS3P FHINS4C FHINS4P FHINS5C FHINS5P FHINS6P FHINS7P FHISP FINDP FINTP
## 1 0 NA 0 NA 0 0 0 0 0 0
## 2 0 NA 0 NA 0 0 0 0 0 0
## 3 0 NA 0 NA 0 0 0 0 0 0
## 4 0 NA 0 NA 0 0 0 0 0 0
## 5 0 NA 0 NA 0 0 0 0 0 0
## 6 0 NA 0 NA 0 0 0 1 0 0
## 7 0 NA 0 NA 0 0 0 0 0 0
## 8 0 NA 0 NA 0 0 0 0 0 0
## 9 0 NA 0 NA 0 0 0 0 0 0
## 10 0 NA 0 NA 0 0 0 0 0 0
## FJWDP FJWMNP FJWRIP FJWTRP FLANP FLANXP FMARP FMARHDP FMARHMP FMARHTP
## 1 0 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## FMARHWP FMARHYP FMIGP FMIGSP FMILPP FMILSP FOCCP FOIP FPAP FPERNP FPINCP
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## FPOBP FPOWSP FPRIVCOVP FPUBCOVP FRACP FRELP FRETP FSCHGP FSCHLP FSCHP FSEMP
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 1 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## FSEXP FSSIP FSSP FWAGP FWKHP FWKLP FWKWP FWRKP FYOEP PWGTP1 PWGTP2 PWGTP3
## 1 0 0 0 0 0 0 0 0 0 142 45 214
## 2 0 0 0 0 0 0 0 0 0 44 37 187
## 3 0 0 0 0 0 0 0 0 0 33 36 138
## 4 0 0 0 0 0 0 0 0 0 38 40 192
## 5 0 0 0 0 0 0 0 0 0 48 48 216
## 6 0 0 0 0 1 0 1 0 0 83 20 46
## 7 0 0 0 0 0 0 0 0 0 186 49 141
## 8 0 0 0 0 0 0 0 0 0 79 47 83
## 9 0 0 0 0 0 0 0 0 0 96 52 94
## 10 0 0 0 0 0 0 0 0 0 95 53 95
## PWGTP4 PWGTP5 PWGTP6 PWGTP7 PWGTP8 PWGTP9 PWGTP10 PWGTP11 PWGTP12 PWGTP13
## 1 143 119 39 124 43 42 231 201 139 114
## 2 186 142 228 128 109 39 111 32 132 47
## 3 138 99 194 108 94 28 98 22 124 32
## 4 159 127 205 130 109 45 125 28 147 46
## 5 191 142 249 154 128 52 131 30 132 49
## 6 43 47 14 54 64 49 14 17 60 59
## 7 148 198 38 128 215 124 35 46 164 111
## 8 14 51 49 53 15 51 101 51 15 13
## 9 16 53 50 52 17 53 119 55 16 22
## 10 17 53 51 53 16 55 120 55 16 22
## PWGTP14 PWGTP15 PWGTP16 PWGTP17 PWGTP18 PWGTP19 PWGTP20 PWGTP21 PWGTP22
## 1 120 120 37 237 154 132 33 138 203
## 2 134 140 230 126 115 217 203 139 163
## 3 96 97 165 98 88 192 182 115 96
## 4 127 125 191 113 147 255 223 144 146
## 5 131 159 278 150 152 265 276 167 195
## 6 21 68 41 67 73 51 44 91 10
## 7 41 228 144 270 184 136 144 275 43
## 8 60 49 13 114 57 94 55 93 55
## 9 64 53 16 101 65 90 60 84 52
## 10 64 53 16 100 64 90 60 85 52
## PWGTP23 PWGTP24 PWGTP25 PWGTP26 PWGTP27 PWGTP28 PWGTP29 PWGTP30 PWGTP31
## 1 38 131 111 220 116 195 199 31 37
## 2 126 126 178 131 41 142 103 193 144
## 3 99 93 141 87 28 123 100 147 121
## 4 123 119 161 112 39 161 99 184 145
## 5 146 144 209 133 49 180 117 228 148
## 6 43 51 38 11 36 96 56 10 13
## 7 149 114 140 39 145 231 132 39 40
## 8 84 14 56 50 52 16 60 93 61
## 9 102 19 68 61 57 15 65 90 58
## 10 103 19 66 61 55 16 64 90 59
## PWGTP32 PWGTP33 PWGTP34 PWGTP35 PWGTP36 PWGTP37 PWGTP38 PWGTP39 PWGTP40
## 1 122 120 116 126 208 36 128 123 215
## 2 37 122 32 181 145 41 47 144 128
## 3 31 114 25 133 121 32 35 109 102
## 4 35 131 34 154 129 42 41 108 133
## 5 47 150 34 219 171 50 58 148 152
## 6 45 44 17 69 42 79 68 45 53
## 7 95 103 39 190 153 192 217 157 175
## 8 14 15 49 58 18 86 53 82 44
## 9 14 15 62 67 16 85 55 94 56
## 10 14 16 62 66 17 86 55 95 56
## PWGTP41 PWGTP42 PWGTP43 PWGTP44 PWGTP45 PWGTP46 PWGTP47 PWGTP48 PWGTP49
## 1 146 36 264 134 143 35 127 43 40
## 2 37 39 210 206 182 214 111 105 45
## 3 38 29 156 216 130 166 82 85 36
## 4 31 34 197 217 178 193 98 112 38
## 5 38 42 261 272 222 261 127 137 46
## 6 62 12 44 46 38 18 41 70 50
## 7 162 34 133 128 160 54 122 288 150
## 8 97 52 96 16 52 46 59 14 51
## 9 111 58 102 20 58 62 51 15 57
## 10 110 58 101 19 58 62 51 17 55
## PWGTP50 PWGTP51 PWGTP52 PWGTP53 PWGTP54 PWGTP55 PWGTP56 PWGTP57 PWGTP58
## 1 220 222 137 125 144 128 40 209 113
## 2 142 27 117 41 121 128 231 142 114
## 3 97 22 111 31 88 103 203 112 83
## 4 133 26 138 40 120 132 258 141 115
## 5 131 37 148 49 168 146 285 164 133
## 6 14 15 49 43 15 78 54 59 57
## 7 28 43 137 159 59 226 156 198 231
## 8 81 51 14 18 59 60 14 130 59
## 9 90 64 15 18 59 60 17 87 61
## 10 90 62 16 16 61 62 17 89 60
## PWGTP59 PWGTP60 PWGTP61 PWGTP62 PWGTP63 PWGTP64 PWGTP65 PWGTP66 PWGTP67
## 1 147 37 130 195 38 118 123 187 124
## 2 224 258 129 148 119 125 225 123 35
## 3 171 186 87 118 88 97 185 80 30
## 4 233 239 128 131 115 134 243 132 38
## 5 247 252 149 169 134 128 299 158 43
## 6 37 72 63 13 38 62 81 18 65
## 7 117 147 180 41 170 196 145 36 128
## 8 80 61 73 50 97 15 56 49 45
## 9 88 58 85 56 111 14 59 58 58
## 10 88 60 87 56 111 17 60 58 56
## PWGTP68 PWGTP69 PWGTP70 PWGTP71 PWGTP72 PWGTP73 PWGTP74 PWGTP75 PWGTP76
## 1 232 194 39 36 104 125 120 114 216
## 2 187 124 165 144 40 126 34 182 129
## 3 175 85 157 94 31 90 31 135 91
## 4 193 112 175 124 33 105 39 190 116
## 5 213 124 205 165 49 138 36 217 127
## 6 108 47 16 12 54 44 13 67 47
## 7 285 117 41 46 121 108 36 208 123
## 8 15 55 103 63 15 11 49 48 19
## 9 16 57 116 56 13 15 56 60 20
## 10 15 57 114 56 14 16 57 58 20
## PWGTP77 PWGTP78 PWGTP79 PWGTP80
## 1 39 122 101 244
## 2 38 39 113 117
## 3 29 29 92 100
## 4 38 31 113 105
## 5 35 39 144 120
## 6 74 60 50 55
## 7 249 173 116 159
## 8 77 50 88 58
## 9 95 51 108 56
## 10 93 52 109 56# subset dataset to those interested variales
df=df[,c('WKHP','WKW','ESR','PERNP','ADJINC','SCHL','SEX','AGEP','RAC1P')]
head(df,10)## WKHP WKW ESR PERNP ADJINC SCHL SEX AGEP RAC1P
## 1 40 1 1 90000 1011189 21 1 47 1
## 2 30 1 1 65000 1011189 21 2 35 1
## 3 50 1 1 115400 1011189 20 1 38 1
## 4 NA NA NA NA 1011189 8 1 14 1
## 5 NA NA NA NA 1011189 3 2 6 1
## 6 38 1 1 28000 1011189 21 1 71 2
## 7 80 6 6 5100 1011189 21 2 65 6
## 8 40 1 1 44000 1011189 21 2 45 1
## 9 NA NA NA NA 1011189 10 1 12 1
## 10 NA NA NA NA 1011189 7 1 9 1# get the actual earing by multiplying to the adjinc factor
df1 = df
df1$PERNP = df1$PERNP*1.011189
df1$ADJINC = NULL
#change column names to the easy understood ones
colnames(df1) = c('work_hour_per_week','work_week','employment_status','earning','educational_level','gender','age','race_1')
head(df1,10)## work_hour_per_week work_week employment_status earning educational_level
## 1 40 1 1 91007.010 21
## 2 30 1 1 65727.285 21
## 3 50 1 1 116691.211 20
## 4 NA NA NA NA 8
## 5 NA NA NA NA 3
## 6 38 1 1 28313.292 21
## 7 80 6 6 5157.064 21
## 8 40 1 1 44492.316 21
## 9 NA NA NA NA 10
## 10 NA NA NA NA 7
## gender age race_1
## 1 1 47 1
## 2 2 35 1
## 3 1 38 1
## 4 1 14 1
## 5 2 6 1
## 6 1 71 2
## 7 2 65 6
## 8 2 45 1
## 9 1 12 1
## 10 1 9 1#Generate min, max, mean, median, first and third quartiles for each variable
summary(df1)## work_hour_per_week work_week employment_status earning
## Min. : 1.00 Min. :1.00 Min. :1.000 Min. : -5157
## 1st Qu.:32.00 1st Qu.:1.00 1st Qu.:1.000 1st Qu.: 0
## Median :40.00 Median :1.00 Median :1.000 Median : 15168
## Mean :37.93 Mean :1.89 Mean :3.072 Mean : 35968
## 3rd Qu.:42.00 3rd Qu.:2.00 3rd Qu.:6.000 3rd Qu.: 50559
## Max. :99.00 Max. :6.00 Max. :6.000 Max. :830186
## NA's :35389 NA's :35389 NA's :13698 NA's :13698
## educational_level gender age race_1
## Min. : 1.00 Min. :1.000 Min. : 0 Min. :1.000
## 1st Qu.:16.00 1st Qu.:1.000 1st Qu.:21 1st Qu.:1.000
## Median :18.00 Median :2.000 Median :41 Median :1.000
## Mean :16.47 Mean :1.506 Mean :41 Mean :2.153
## 3rd Qu.:21.00 3rd Qu.:2.000 3rd Qu.:60 3rd Qu.:1.000
## Max. :24.00 Max. :2.000 Max. :94 Max. :9.000
## NA's :2434# check the distribution of earnings, some observations has extreme high value of earnings(more than 500K) and extreme low earnings(even negative), we need to discuss the range of earnings incorparated in the following study
hist(df1$earning)
hist(df1$earning,xlim = c(200000,900000),ylim = c(0,500),breaks = 30)
hist(df1$earning,xlim = c(1,1000),ylim = c(0,500),breaks = 30000)
boxplot(df1$earning)
hist(df1$educational_level,main = "Histogram of Educational Level", xlab = "Educational Level", xlim = c(0,25))
boxplot(df1$educational_level,main = "Educational Level")
#change the numerical education attainment to characters showing level of degree, I use following 8 categories but we may further discuss about the categories
df1$education_group = ifelse(df1$educational_level >=1 & df1$educational_level<=15,"NoHSD",
ifelse(df1$educational_level %in% c(16,17), "HSD",
ifelse(df1$educational_level ==18 | df1$educational_level==19,"Some College",
ifelse(df1$educational_level == 20,"ASC",
ifelse(df1$educational_level ==21,"BAC",
ifelse(df1$educational_level==22,"MS",
ifelse(df1$educational_level==23,"PRO",
ifelse(df1$educational_level==24,"Doc",NA))))))))
head(df1,10)## work_hour_per_week work_week employment_status earning educational_level
## 1 40 1 1 91007.010 21
## 2 30 1 1 65727.285 21
## 3 50 1 1 116691.211 20
## 4 NA NA NA NA 8
## 5 NA NA NA NA 3
## 6 38 1 1 28313.292 21
## 7 80 6 6 5157.064 21
## 8 40 1 1 44492.316 21
## 9 NA NA NA NA 10
## 10 NA NA NA NA 7
## gender age race_1 education_group
## 1 1 47 1 BAC
## 2 2 35 1 BAC
## 3 1 38 1 ASC
## 4 1 14 1 NoHSD
## 5 2 6 1 NoHSD
## 6 1 71 2 BAC
## 7 2 65 6 BAC
## 8 2 45 1 BAC
## 9 1 12 1 NoHSD
## 10 1 9 1 NoHSD#barchart of frequency of educational attainment
par(mar=c(4,4,1,1)+.1)
plt=barplot(table(df1$education_group)[c(6,4,8,1,2,5,7,3)],main="Educational Attainment Count", names.arg=c("NoHSD", "HSD",
"Some College", "ASC", "BAC", "MS", "PRO","Doc"),ylab = "Count",ylim = c(0,20000),yaxt="n",cex.names =0.7)
text(plt,y=table(df1$education_group)[c(6,4,8,1,2,5,7,3)],table(df1$education_group)[c(6,4,8,1,2,5,7,3)],pos = 3, cex=0.7)
axis(2,cex.axis = 0.7)
#change the numerical race to characters using following 5 categories
df1$race = ifelse(df1$race_1 ==1,"White",
ifelse(df1$race_1 ==2,"Black or African American",
ifelse(df1$race_1 %in% c(3,4,5),"American Indian or Alaska Native",
ifelse(df1$race_1 ==6,"Asian",
ifelse(df1$race_1 ==7,"Native Hawaiian and Other Pacific Islander",
ifelse(df1$race_1 %in% c(8,9),"Others",NA))))))
head(df1,10)## work_hour_per_week work_week employment_status earning educational_level
## 1 40 1 1 91007.010 21
## 2 30 1 1 65727.285 21
## 3 50 1 1 116691.211 20
## 4 NA NA NA NA 8
## 5 NA NA NA NA 3
## 6 38 1 1 28313.292 21
## 7 80 6 6 5157.064 21
## 8 40 1 1 44492.316 21
## 9 NA NA NA NA 10
## 10 NA NA NA NA 7
## gender age race_1 education_group race
## 1 1 47 1 BAC White
## 2 2 35 1 BAC White
## 3 1 38 1 ASC White
## 4 1 14 1 NoHSD White
## 5 2 6 1 NoHSD White
## 6 1 71 2 BAC Black or African American
## 7 2 65 6 BAC Asian
## 8 2 45 1 BAC White
## 9 1 12 1 NoHSD White
## 10 1 9 1 NoHSD White#barplot of frequency of race
counts <- table(df1$race)
par(mar=c(5,4,1,1)+.1)
plt = barplot(counts[c(6,3,1,2,4,5)], xaxt="n",main = "Race Count",ylab = "Count",ylim = c(0,65000),yaxt="n")
text(plt, par("usr")[3], labels = c("White alone","Black or African American","American Indian or Alaska Native","Asian","Hawaiian and Other Pacific Islander","Others"), srt = 20, adj = c(1.1,1.1), xpd = TRUE, cex=0.7)
text(plt,y=counts[c(6,3,1,2,4,5)],counts[c(6,3,1,2,4,5)],pos = 3, cex=0.7)
axis(2,cex.axis = 0.7)
#crosstable for education attainment and race
xtabs(~ df1$education_group + df1$race)## df1$race
## df1$education_group American Indian or Alaska Native Asian
## ASC 97 411
## BAC 77 1341
## Doc 2 138
## HSD 359 762
## MS 28 665
## NoHSD 625 1490
## PRO 5 152
## Some College 312 776
## df1$race
## df1$education_group Black or African American
## ASC 159
## BAC 239
## Doc 20
## HSD 390
## MS 87
## NoHSD 529
## PRO 17
## Some College 498
## df1$race
## df1$education_group Native Hawaiian and Other Pacific Islander Others White
## ASC 36 309 4866
## BAC 28 526 10230
## Doc 1 29 638
## HSD 105 1001 10990
## MS 9 162 4376
## NoHSD 91 2934 12357
## PRO 0 46 1055
## Some College 66 968 12259hist(df1$age,main = "Histogram of Age", xlab = "Age",xlim=c(0,100))
#Generate correlation coefficients between earning and education attainment, here I use the numerical educational level
cor(df1$earning,df1$educational_level,use = "complete.obs")## [1] 0.263736cor(df1$earning,df1$age,use = "complete.obs")## [1] -0.07296541#generate scatterplot
plot(df1$educational_level,df1$earning)
plot(df1$age,df1$earning)
colnames(df1)## [1] "work_hour_per_week" "work_week" "employment_status"
## [4] "earning" "educational_level" "gender"
## [7] "age" "race_1" "education_group"
## [10] "race"head(df1,10)## work_hour_per_week work_week employment_status earning educational_level
## 1 40 1 1 91007.010 21
## 2 30 1 1 65727.285 21
## 3 50 1 1 116691.211 20
## 4 NA NA NA NA 8
## 5 NA NA NA NA 3
## 6 38 1 1 28313.292 21
## 7 80 6 6 5157.064 21
## 8 40 1 1 44492.316 21
## 9 NA NA NA NA 10
## 10 NA NA NA NA 7
## gender age race_1 education_group race
## 1 1 47 1 BAC White
## 2 2 35 1 BAC White
## 3 1 38 1 ASC White
## 4 1 14 1 NoHSD White
## 5 2 6 1 NoHSD White
## 6 1 71 2 BAC Black or African American
## 7 2 65 6 BAC Asian
## 8 2 45 1 BAC White
## 9 1 12 1 NoHSD White
## 10 1 9 1 NoHSD WhiteSubsample
#removes all observations with missing values
subsample <- na.omit(df1[,c("gender","work_hour_per_week","work_week","employment_status","earning","educational_level","education_group","age","race")])
head(subsample,10)## gender work_hour_per_week work_week employment_status earning
## 1 1 40 1 1 91007.010
## 2 2 30 1 1 65727.285
## 3 1 50 1 1 116691.211
## 6 1 38 1 1 28313.292
## 7 2 80 6 6 5157.064
## 8 2 40 1 1 44492.316
## 11 2 50 1 1 85951.065
## 12 1 40 1 1 6673.847
## 13 2 40 1 1 12639.862
## 16 1 40 2 1 55615.395
## educational_level education_group age race
## 1 21 BAC 47 White
## 2 21 BAC 35 White
## 3 20 ASC 38 White
## 6 21 BAC 71 Black or African American
## 7 21 BAC 65 Asian
## 8 21 BAC 45 White
## 11 21 BAC 31 White
## 12 21 BAC 34 White
## 13 18 Some College 45 American Indian or Alaska Native
## 16 21 BAC 64 White#subset dataset by 1.earning >=28080, 2.employment status showing civilian employed or armed forces, 3. worked at least 40 hr per week
# 4. who worked at least 40 of the past 52 weeks(past year) 5. age between adult age and retirement age
# above selection criteria are just preliminary criteria, we may discuss these later
employedwithpay <- subset(subsample, earning >= 22880 & !(employment_status %in% c(3,6)) & work_hour_per_week>=40 & work_week %in% c(1,2,3) & age >= 18 & age <= 66)
head(employedwithpay)## gender work_hour_per_week work_week employment_status earning
## 1 1 40 1 1 91007.01
## 3 1 50 1 1 116691.21
## 8 2 40 1 1 44492.32
## 11 2 50 1 1 85951.06
## 16 1 40 2 1 55615.39
## 18 2 42 1 1 80895.12
## educational_level education_group age race
## 1 21 BAC 47 White
## 3 20 ASC 38 White
## 8 21 BAC 45 White
## 11 21 BAC 31 White
## 16 21 BAC 64 White
## 18 16 HSD 53 WhiteBasic EDA
head(employedwithpay,10)## gender work_hour_per_week work_week employment_status earning
## 1 1 40 1 1 91007.01
## 3 1 50 1 1 116691.21
## 8 2 40 1 1 44492.32
## 11 2 50 1 1 85951.06
## 16 1 40 2 1 55615.39
## 18 2 42 1 1 80895.12
## 19 1 45 2 1 78872.74
## 25 1 40 1 1 56626.58
## 27 1 50 1 1 101118.90
## 31 2 40 1 1 136510.51
## educational_level education_group age race
## 1 21 BAC 47 White
## 3 20 ASC 38 White
## 8 21 BAC 45 White
## 11 21 BAC 31 White
## 16 21 BAC 64 White
## 18 16 HSD 53 White
## 19 16 HSD 53 White
## 25 16 HSD 54 White
## 27 22 MS 41 White
## 31 20 ASC 55 Whitesummary(employedwithpay)## gender work_hour_per_week work_week employment_status
## Min. :1.000 Min. :40.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:40.00 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :40.00 Median :1.000 Median :1.000
## Mean :1.392 Mean :44.67 Mean :1.086 Mean :1.063
## 3rd Qu.:2.000 3rd Qu.:50.00 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :99.00 Max. :3.000 Max. :4.000
## earning educational_level education_group age
## Min. : 22954 Min. : 1.00 Length:21334 Min. :18.00
## 1st Qu.: 40448 1st Qu.:18.00 Class :character 1st Qu.:34.00
## Median : 60671 Median :20.00 Mode :character Median :44.00
## Mean : 81136 Mean :19.29 Mean :43.76
## 3rd Qu.: 96063 3rd Qu.:21.00 3rd Qu.:54.00
## Max. :830186 Max. :24.00 Max. :66.00
## race
## Length:21334
## Class :character
## Mode :character
##
##
## ## correlation table
library(Hmisc)
employedwithpay %>%
select_if(is.numeric) %>%
as.matrix() %>%
rcorr()## gender work_hour_per_week work_week employment_status
## gender 1.00 -0.12 0.01 -0.07
## work_hour_per_week -0.12 1.00 -0.02 0.19
## work_week 0.01 -0.02 1.00 0.01
## employment_status -0.07 0.19 0.01 1.00
## earning -0.14 0.18 -0.04 -0.05
## educational_level 0.10 0.03 -0.01 -0.02
## age 0.02 0.00 -0.04 -0.14
## earning educational_level age
## gender -0.14 0.10 0.02
## work_hour_per_week 0.18 0.03 0.00
## work_week -0.04 -0.01 -0.04
## employment_status -0.05 -0.02 -0.14
## earning 1.00 0.28 0.16
## educational_level 0.28 1.00 0.02
## age 0.16 0.02 1.00
##
## n= 21334
##
##
## P
## gender work_hour_per_week work_week employment_status
## gender 0.0000 0.1590 0.0000
## work_hour_per_week 0.0000 0.0019 0.0000
## work_week 0.1590 0.0019 0.3826
## employment_status 0.0000 0.0000 0.3826
## earning 0.0000 0.0000 0.0000 0.0000
## educational_level 0.0000 0.0000 0.3965 0.0022
## age 0.0052 0.8788 0.0000 0.0000
## earning educational_level age
## gender 0.0000 0.0000 0.0052
## work_hour_per_week 0.0000 0.0000 0.8788
## work_week 0.0000 0.3965 0.0000
## employment_status 0.0000 0.0022 0.0000
## earning 0.0000 0.0000
## educational_level 0.0000 0.0026
## age 0.0000 0.0026## correlation graph
employedwithpay %>%
select(work_hour_per_week,earning,educational_level,age,education_group) %>%
inspect_cor() %>%
show_plot()
hist(employedwithpay$earning,xlim = c(200000,900000),ylim = c(0,400),breaks = 30)
#change education attainment and race into factors sothat I can compute discriptive statistics
employedwithpay$education_group <- as.factor(employedwithpay$education_group)
employedwithpay$race <- as.factor(employedwithpay$race)
employedwithpay$gender <- as.factor(employedwithpay$gender)
head(employedwithpay,10)## gender work_hour_per_week work_week employment_status earning
## 1 1 40 1 1 91007.01
## 3 1 50 1 1 116691.21
## 8 2 40 1 1 44492.32
## 11 2 50 1 1 85951.06
## 16 1 40 2 1 55615.39
## 18 2 42 1 1 80895.12
## 19 1 45 2 1 78872.74
## 25 1 40 1 1 56626.58
## 27 1 50 1 1 101118.90
## 31 2 40 1 1 136510.51
## educational_level education_group age race
## 1 21 BAC 47 White
## 3 20 ASC 38 White
## 8 21 BAC 45 White
## 11 21 BAC 31 White
## 16 21 BAC 64 White
## 18 16 HSD 53 White
## 19 16 HSD 53 White
## 25 16 HSD 54 White
## 27 22 MS 41 White
## 31 20 ASC 55 White# descriptive statistics
summary(employedwithpay)## gender work_hour_per_week work_week employment_status
## 1:12963 Min. :40.00 Min. :1.000 Min. :1.000
## 2: 8371 1st Qu.:40.00 1st Qu.:1.000 1st Qu.:1.000
## Median :40.00 Median :1.000 Median :1.000
## Mean :44.67 Mean :1.086 Mean :1.063
## 3rd Qu.:50.00 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :99.00 Max. :3.000 Max. :4.000
##
## earning educational_level education_group age
## Min. : 22954 Min. : 1.00 BAC :5792 Min. :18.00
## 1st Qu.: 40448 1st Qu.:18.00 Some College:4747 1st Qu.:34.00
## Median : 60671 Median :20.00 HSD :3920 Median :44.00
## Mean : 81136 Mean :19.29 MS :2650 Mean :43.76
## 3rd Qu.: 96063 3rd Qu.:21.00 ASC :2268 3rd Qu.:54.00
## Max. :830186 Max. :24.00 NoHSD : 941 Max. :66.00
## (Other) :1016
## race
## American Indian or Alaska Native : 282
## Asian : 1940
## Black or African American : 558
## Native Hawaiian and Other Pacific Islander: 107
## Others : 1356
## White :17091
## sd(employedwithpay$earning)## [1] 72714.62sd(employedwithpay$age)## [1] 12.02443hist(employedwithpay$educational_level)
plot(employedwithpay$educational_level,employedwithpay$earning)
plot(employedwithpay$age,employedwithpay$earning)
plot(employedwithpay$education_group,employedwithpay$earning,las=2,cex.axis=0.7,xlab = 'Education Attainment',ylab='Earning')
plot(employedwithpay$race,employedwithpay$earning,las =2,cex.axis=0.7,xlab='Race',ylab="Earning")
employedwithpay$gender = ifelse(employedwithpay$gender == 1,"male","female")
ggplot(employedwithpay,aes(x=education_group,fill=gender)) +
geom_bar(width = 0.8,position = position_dodge())+
geom_text(stat="count",aes(label=..count..),vjust=-1,color="black",position = position_dodge(0.9),size = 3)+
scale_x_discrete(limits = c("NoHSD","HSD","Some College","ASC","BAC","MS","PRO","Doc")) +
theme_minimal()
ggplot(employedwithpay,aes(x=race,fill=gender)) +
geom_bar(width = 0.8,position = position_dodge())+
geom_text(stat="count",aes(label=..count..),vjust=-1,color="black",position = position_dodge(0.9),size = 2.3)+
scale_x_discrete(limits = c("White","Black or African American","American Indian or Alaska Native","Asian","Native Hawaiian and Other Pacific Islander","Others")) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 20,hjust=1))
summary(employedwithpay$education_group)/sum(summary(employedwithpay$education_group))*100## ASC BAC Doc HSD MS NoHSD
## 10.630918 27.149152 1.964001 18.374426 12.421487 4.410800
## PRO Some College
## 2.798350 22.250867summary(employedwithpay$race)/sum(summary(employedwithpay$race))*100## American Indian or Alaska Native
## 1.3218337
## Asian
## 9.0934658
## Black or African American
## 2.6155433
## Native Hawaiian and Other Pacific Islander
## 0.5015468
## Others
## 6.3560514
## White
## 80.1115590Model Create
Dummy Variables
#convert all the categorical variables into dummy variables
employedwithpay$female<-ifelse(employedwithpay$gender == "female",1,0)
employedwithpay$HSD<-ifelse(employedwithpay$education_group == 'HSD',1,0)
employedwithpay$Some_College<-ifelse(employedwithpay$education_group == 'Some College',1,0)
employedwithpay$Associate_degree<-ifelse(employedwithpay$education_group == 'ASC',1,0)
employedwithpay$Bachelor_degree<-ifelse(employedwithpay$education_group == 'BAC',1,0)
employedwithpay$Master_degree<-ifelse(employedwithpay$education_group == 'MS',1,0)
employedwithpay$Professional_degree<-ifelse(employedwithpay$education_group == 'PRO',1,0)
employedwithpay$Doctoral_degree<-ifelse(employedwithpay$education_group == 'Doc',1,0)
employedwithpay$Others<-ifelse(employedwithpay$race == 'Others',1,0)
employedwithpay$Black_or_African_American<-ifelse(employedwithpay$race == 'Black or African American',1,0)
employedwithpay$American_Indian_or_Alaska_Native<-ifelse(employedwithpay$race == 'American Indian or Alaska Native',1,0)
employedwithpay$Asian<-ifelse(employedwithpay$race == 'Asian',1,0)
employedwithpay$Native_Hawaiian_and_Other_Pacific_Islander <-ifelse(employedwithpay$race == 'Native Hawaiian and Other Pacific Islander',1,0)
head(employedwithpay,10)## gender work_hour_per_week work_week employment_status earning
## 1 male 40 1 1 91007.01
## 3 male 50 1 1 116691.21
## 8 female 40 1 1 44492.32
## 11 female 50 1 1 85951.06
## 16 male 40 2 1 55615.39
## 18 female 42 1 1 80895.12
## 19 male 45 2 1 78872.74
## 25 male 40 1 1 56626.58
## 27 male 50 1 1 101118.90
## 31 female 40 1 1 136510.51
## educational_level education_group age race female HSD Some_College
## 1 21 BAC 47 White 0 0 0
## 3 20 ASC 38 White 0 0 0
## 8 21 BAC 45 White 1 0 0
## 11 21 BAC 31 White 1 0 0
## 16 21 BAC 64 White 0 0 0
## 18 16 HSD 53 White 1 1 0
## 19 16 HSD 53 White 0 1 0
## 25 16 HSD 54 White 0 1 0
## 27 22 MS 41 White 0 0 0
## 31 20 ASC 55 White 1 0 0
## Associate_degree Bachelor_degree Master_degree Professional_degree
## 1 0 1 0 0
## 3 1 0 0 0
## 8 0 1 0 0
## 11 0 1 0 0
## 16 0 1 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 25 0 0 0 0
## 27 0 0 1 0
## 31 1 0 0 0
## Doctoral_degree Others Black_or_African_American
## 1 0 0 0
## 3 0 0 0
## 8 0 0 0
## 11 0 0 0
## 16 0 0 0
## 18 0 0 0
## 19 0 0 0
## 25 0 0 0
## 27 0 0 0
## 31 0 0 0
## American_Indian_or_Alaska_Native Asian
## 1 0 0
## 3 0 0
## 8 0 0
## 11 0 0
## 16 0 0
## 18 0 0
## 19 0 0
## 25 0 0
## 27 0 0
## 31 0 0
## Native_Hawaiian_and_Other_Pacific_Islander
## 1 0
## 3 0
## 8 0
## 11 0
## 16 0
## 18 0
## 19 0
## 25 0
## 27 0
## 31 0colnames(employedwithpay)## [1] "gender"
## [2] "work_hour_per_week"
## [3] "work_week"
## [4] "employment_status"
## [5] "earning"
## [6] "educational_level"
## [7] "education_group"
## [8] "age"
## [9] "race"
## [10] "female"
## [11] "HSD"
## [12] "Some_College"
## [13] "Associate_degree"
## [14] "Bachelor_degree"
## [15] "Master_degree"
## [16] "Professional_degree"
## [17] "Doctoral_degree"
## [18] "Others"
## [19] "Black_or_African_American"
## [20] "American_Indian_or_Alaska_Native"
## [21] "Asian"
## [22] "Native_Hawaiian_and_Other_Pacific_Islander"#remove those original variables, such as educational level
employedwithpay = employedwithpay[,-c(1,2,3,4,6,7,9)]
head(employedwithpay)## earning age female HSD Some_College Associate_degree Bachelor_degree
## 1 91007.01 47 0 0 0 0 1
## 3 116691.21 38 0 0 0 1 0
## 8 44492.32 45 1 0 0 0 1
## 11 85951.06 31 1 0 0 0 1
## 16 55615.39 64 0 0 0 0 1
## 18 80895.12 53 1 1 0 0 0
## Master_degree Professional_degree Doctoral_degree Others
## 1 0 0 0 0
## 3 0 0 0 0
## 8 0 0 0 0
## 11 0 0 0 0
## 16 0 0 0 0
## 18 0 0 0 0
## Black_or_African_American American_Indian_or_Alaska_Native Asian
## 1 0 0 0
## 3 0 0 0
## 8 0 0 0
## 11 0 0 0
## 16 0 0 0
## 18 0 0 0
## Native_Hawaiian_and_Other_Pacific_Islander
## 1 0
## 3 0
## 8 0
## 11 0
## 16 0
## 18 0colnames(employedwithpay)## [1] "earning"
## [2] "age"
## [3] "female"
## [4] "HSD"
## [5] "Some_College"
## [6] "Associate_degree"
## [7] "Bachelor_degree"
## [8] "Master_degree"
## [9] "Professional_degree"
## [10] "Doctoral_degree"
## [11] "Others"
## [12] "Black_or_African_American"
## [13] "American_Indian_or_Alaska_Native"
## [14] "Asian"
## [15] "Native_Hawaiian_and_Other_Pacific_Islander"Model 1 - By eaning and all variables
#build model using all the variables
model1 = lm(earning ~ ., data=employedwithpay)
summary(model1)##
## Call:
## lm(formula = earning ~ ., data = employedwithpay)
##
## Residuals:
## Min 1Q Median 3Q Max
## -155563 -30719 -10857 12572 768842
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 17338.53 2763.63 6.274
## age 903.27 37.86 23.856
## female -26599.66 930.08 -28.599
## HSD 9681.26 2406.94 4.022
## Some_College 18680.54 2373.71 7.870
## Associate_degree 19615.16 2577.58 7.610
## Bachelor_degree 51895.74 2345.41 22.127
## Master_degree 64657.99 2535.76 25.498
## Professional_degree 112773.09 3466.68 32.531
## Doctoral_degree 100419.84 3888.05 25.828
## Others -6133.71 1888.04 -3.249
## Black_or_African_American -11613.09 2832.67 -4.100
## American_Indian_or_Alaska_Native -10362.80 3959.76 -2.617
## Asian 3736.19 1592.98 2.345
## Native_Hawaiian_and_Other_Pacific_Islander -14558.89 6386.71 -2.280
## Pr(>|t|)
## (Intercept) 3.59e-10 ***
## age < 2e-16 ***
## female < 2e-16 ***
## HSD 5.79e-05 ***
## Some_College 3.72e-15 ***
## Associate_degree 2.86e-14 ***
## Bachelor_degree < 2e-16 ***
## Master_degree < 2e-16 ***
## Professional_degree < 2e-16 ***
## Doctoral_degree < 2e-16 ***
## Others 0.00116 **
## Black_or_African_American 4.15e-05 ***
## American_Indian_or_Alaska_Native 0.00888 **
## Asian 0.01902 *
## Native_Hawaiian_and_Other_Pacific_Islander 0.02264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 65780 on 21319 degrees of freedom
## Multiple R-squared: 0.1822, Adjusted R-squared: 0.1817
## F-statistic: 339.3 on 14 and 21319 DF, p-value: < 2.2e-16Model 2 - By logarithem of earning
Regression Model
#take logarithem of earning and build model based on that
employedwithpay$earning_log = log(employedwithpay$earning)
model2 = lm(earning_log ~ .-earning, data=employedwithpay)
summary(model2)##
## Call:
## lm(formula = earning_log ~ . - earning, data = employedwithpay)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.76179 -0.35392 -0.03881 0.30802 2.78079
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 10.2965594 0.0219650 468.770
## age 0.0105310 0.0003009 34.995
## female -0.2778588 0.0073922 -37.588
## HSD 0.1518835 0.0191302 7.939
## Some_College 0.2751073 0.0188660 14.582
## Associate_degree 0.3118648 0.0204864 15.223
## Bachelor_degree 0.6384244 0.0186411 34.248
## Master_degree 0.7882579 0.0201540 39.112
## Professional_degree 1.0448463 0.0275528 37.922
## Doctoral_degree 1.0104175 0.0309019 32.698
## Others -0.0891026 0.0150059 -5.938
## Black_or_African_American -0.1510153 0.0225138 -6.708
## American_Indian_or_Alaska_Native -0.1448709 0.0314718 -4.603
## Asian 0.0179381 0.0126608 1.417
## Native_Hawaiian_and_Other_Pacific_Islander -0.1779386 0.0507610 -3.505
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## age < 2e-16 ***
## female < 2e-16 ***
## HSD 2.13e-15 ***
## Some_College < 2e-16 ***
## Associate_degree < 2e-16 ***
## Bachelor_degree < 2e-16 ***
## Master_degree < 2e-16 ***
## Professional_degree < 2e-16 ***
## Doctoral_degree < 2e-16 ***
## Others 2.93e-09 ***
## Black_or_African_American 2.03e-11 ***
## American_Indian_or_Alaska_Native 4.18e-06 ***
## Asian 0.156551
## Native_Hawaiian_and_Other_Pacific_Islander 0.000457 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5228 on 21319 degrees of freedom
## Multiple R-squared: 0.2804, Adjusted R-squared: 0.2799
## F-statistic: 593.4 on 14 and 21319 DF, p-value: < 2.2e-16BPG test for heteroskedasticity
#Conduct BPG Test
library(lmtest)
bptest(model2)##
## studentized Breusch-Pagan test
##
## data: model2
## BP = 663.94, df = 14, p-value < 2.2e-16bptestmodel2 = lm(residuals(model2)*residuals(model2) ~ .-earning_log -earning, data=employedwithpay)
summary(bptestmodel2) ##
## Call:
## lm(formula = residuals(model2) * residuals(model2) ~ . - earning_log -
## earning, data = employedwithpay)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6140 -0.2284 -0.1307 0.0535 7.5141
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.078468 0.020364 3.853
## age 0.003112 0.000279 11.154
## female -0.081469 0.006854 -11.887
## HSD 0.021973 0.017736 1.239
## Some_College 0.053723 0.017491 3.071
## Associate_degree 0.034734 0.018993 1.829
## Bachelor_degree 0.150356 0.017283 8.700
## Master_degree 0.123279 0.018685 6.598
## Professional_degree 0.333128 0.025545 13.041
## Doctoral_degree 0.199111 0.028650 6.950
## Others -0.007766 0.013912 -0.558
## Black_or_African_American -0.005902 0.020873 -0.283
## American_Indian_or_Alaska_Native 0.006477 0.029178 0.222
## Asian 0.024274 0.011738 2.068
## Native_Hawaiian_and_Other_Pacific_Islander -0.048259 0.047062 -1.025
## Pr(>|t|)
## (Intercept) 0.000117 ***
## age < 2e-16 ***
## female < 2e-16 ***
## HSD 0.215402
## Some_College 0.002133 **
## Associate_degree 0.067453 .
## Bachelor_degree < 2e-16 ***
## Master_degree 4.27e-11 ***
## Professional_degree < 2e-16 ***
## Doctoral_degree 3.76e-12 ***
## Others 0.576710
## Black_or_African_American 0.777379
## American_Indian_or_Alaska_Native 0.824337
## Asian 0.038655 *
## Native_Hawaiian_and_Other_Pacific_Islander 0.305168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4847 on 21319 degrees of freedom
## Multiple R-squared: 0.03112, Adjusted R-squared: 0.03048
## F-statistic: 48.91 on 14 and 21319 DF, p-value: < 2.2e-16#note: BP = n*RSquared of model with squared residuals as dependent variable Comments:
data: model2 BP = 683.1, df = 14, p-value < 2.2e-16 Null hypothesis: homoskedasticity With p-value is low (0.05), we have strong evidence against the null hypothesis of homoscedasticity.
Calculate robust standard errors
#Generate the Variance Covariance Matrix of the Parameter Estimates
#coeftest(model2,vcov = vcovHC(model2, type = "HC"))zero condition mean
res = residuals(model2)
summary(res)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.76179 -0.35392 -0.03881 0.00000 0.30802 2.78079plot(density(resid(model2)))
Interact variables Model
Quadratic Age & Higher Education by Race
Regression Model
earn_log_higher_edu_race = lm(log(earning,base = exp(1)) ~ .-earning -earning_log + I(age*age) + I(Bachelor_degree*female) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*female) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*female) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*female) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander), data=employedwithpay)## Warning in terms.formula(formula, data = data): 'varlist' has changed (from
## nvar=41) to new 42 after EncodeVars() -- should no longer happen!summary(earn_log_higher_edu_race)##
## Call:
## lm(formula = log(earning, base = exp(1)) ~ . - earning - earning_log +
## I(age * age) + I(Bachelor_degree * female) + I(Bachelor_degree *
## Others) + I(Bachelor_degree * Black_or_African_American) +
## I(Bachelor_degree * American_Indian_or_Alaska_Native) + I(Bachelor_degree *
## Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * female) + I(Master_degree * Others) + I(Master_degree *
## Black_or_African_American) + I(Master_degree * American_Indian_or_Alaska_Native) +
## I(Master_degree * Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * female) + I(Professional_degree *
## Others) + I(Professional_degree * Black_or_African_American) +
## I(Professional_degree * American_Indian_or_Alaska_Native) +
## I(Professional_degree * Asian) + I(Professional_degree *
## Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree *
## female) + I(Doctoral_degree * Others) + I(Doctoral_degree *
## Black_or_African_American) + I(Doctoral_degree * American_Indian_or_Alaska_Native) +
## I(Doctoral_degree * Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.97080 -0.33973 -0.03502 0.30030 2.68785
##
## Coefficients: (2 not defined because of singularities)
## Estimate
## (Intercept) 9.0917775
## age 0.0704232
## female -0.2534467
## HSD 0.1633354
## Some_College 0.2761979
## Associate_degree 0.3061122
## Bachelor_degree 0.6469673
## Master_degree 0.7275805
## Professional_degree 0.9880088
## Doctoral_degree 0.9411036
## Others -0.1086391
## Black_or_African_American -0.1523047
## American_Indian_or_Alaska_Native -0.1142728
## Asian -0.1367207
## Native_Hawaiian_and_Other_Pacific_Islander -0.1355932
## I(age * age) -0.0006854
## I(Bachelor_degree * female) -0.0504563
## I(Bachelor_degree * Others) 0.0367498
## I(Bachelor_degree * Black_or_African_American) 0.0334423
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.2685060
## I(Bachelor_degree * Asian) 0.1167780
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.3042506
## I(Master_degree * female) -0.0239311
## I(Master_degree * Others) 0.0449162
## I(Master_degree * Black_or_African_American) -0.1044384
## I(Master_degree * American_Indian_or_Alaska_Native) -0.0773061
## I(Master_degree * Asian) 0.4337099
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.3150038
## I(Professional_degree * female) 0.0146512
## I(Professional_degree * Others) -0.0199917
## I(Professional_degree * Black_or_African_American) -0.1549889
## I(Professional_degree * American_Indian_or_Alaska_Native) -0.9975620
## I(Professional_degree * Asian) 0.3275611
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 0.0425092
## I(Doctoral_degree * Others) -0.0024015
## I(Doctoral_degree * Black_or_African_American) 0.4630668
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.3968275
## I(Doctoral_degree * Asian) 0.1939548
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 0.0502538
## age 0.0022598
## female 0.0099048
## HSD 0.0188294
## Some_College 0.0185983
## Associate_degree 0.0201734
## Bachelor_degree 0.0198492
## Master_degree 0.0230311
## Professional_degree 0.0349582
## Doctoral_degree 0.0398463
## Others 0.0175671
## Black_or_African_American 0.0270685
## American_Indian_or_Alaska_Native 0.0340550
## Asian 0.0200091
## Native_Hawaiian_and_Other_Pacific_Islander 0.0561491
## I(age * age) 0.0000257
## I(Bachelor_degree * female) 0.0168668
## I(Bachelor_degree * Others) 0.0368196
## I(Bachelor_degree * Black_or_African_American) 0.0550362
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.1012783
## I(Bachelor_degree * Asian) 0.0293337
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.1304049
## I(Master_degree * female) 0.0223354
## I(Master_degree * Others) 0.0605681
## I(Master_degree * Black_or_African_American) 0.0781512
## I(Master_degree * American_Indian_or_Alaska_Native) 0.1294116
## I(Master_degree * Asian) 0.0345327
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.2623163
## I(Professional_degree * female) 0.0437705
## I(Professional_degree * Others) 0.1066616
## I(Professional_degree * Black_or_African_American) 0.1749725
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.2990826
## I(Professional_degree * Asian) 0.0622374
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 0.0522495
## I(Doctoral_degree * Others) 0.1254941
## I(Doctoral_degree * Black_or_African_American) 0.1668251
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.5150339
## I(Doctoral_degree * Asian) 0.0642719
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) 180.917
## age 31.164
## female -25.588
## HSD 8.674
## Some_College 14.851
## Associate_degree 15.174
## Bachelor_degree 32.594
## Master_degree 31.591
## Professional_degree 28.263
## Doctoral_degree 23.618
## Others -6.184
## Black_or_African_American -5.627
## American_Indian_or_Alaska_Native -3.356
## Asian -6.833
## Native_Hawaiian_and_Other_Pacific_Islander -2.415
## I(age * age) -26.664
## I(Bachelor_degree * female) -2.991
## I(Bachelor_degree * Others) 0.998
## I(Bachelor_degree * Black_or_African_American) 0.608
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -2.651
## I(Bachelor_degree * Asian) 3.981
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -2.333
## I(Master_degree * female) -1.071
## I(Master_degree * Others) 0.742
## I(Master_degree * Black_or_African_American) -1.336
## I(Master_degree * American_Indian_or_Alaska_Native) -0.597
## I(Master_degree * Asian) 12.559
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.201
## I(Professional_degree * female) 0.335
## I(Professional_degree * Others) -0.187
## I(Professional_degree * Black_or_African_American) -0.886
## I(Professional_degree * American_Indian_or_Alaska_Native) -3.335
## I(Professional_degree * Asian) 5.263
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 0.814
## I(Doctoral_degree * Others) -0.019
## I(Doctoral_degree * Black_or_African_American) 2.776
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.770
## I(Doctoral_degree * Asian) 3.018
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) < 2e-16
## age < 2e-16
## female < 2e-16
## HSD < 2e-16
## Some_College < 2e-16
## Associate_degree < 2e-16
## Bachelor_degree < 2e-16
## Master_degree < 2e-16
## Professional_degree < 2e-16
## Doctoral_degree < 2e-16
## Others 6.35e-10
## Black_or_African_American 1.86e-08
## American_Indian_or_Alaska_Native 0.000793
## Asian 8.54e-12
## Native_Hawaiian_and_Other_Pacific_Islander 0.015749
## I(age * age) < 2e-16
## I(Bachelor_degree * female) 0.002780
## I(Bachelor_degree * Others) 0.318240
## I(Bachelor_degree * Black_or_African_American) 0.543431
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.008027
## I(Bachelor_degree * Asian) 6.88e-05
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.019651
## I(Master_degree * female) 0.283981
## I(Master_degree * Others) 0.458349
## I(Master_degree * Black_or_African_American) 0.181445
## I(Master_degree * American_Indian_or_Alaska_Native) 0.550269
## I(Master_degree * Asian) < 2e-16
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.229821
## I(Professional_degree * female) 0.737834
## I(Professional_degree * Others) 0.851325
## I(Professional_degree * Black_or_African_American) 0.375741
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.000853
## I(Professional_degree * Asian) 1.43e-07
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 0.415894
## I(Doctoral_degree * Others) 0.984733
## I(Doctoral_degree * Black_or_African_American) 0.005512
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.441019
## I(Doctoral_degree * Asian) 0.002550
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## female ***
## HSD ***
## Some_College ***
## Associate_degree ***
## Bachelor_degree ***
## Master_degree ***
## Professional_degree ***
## Doctoral_degree ***
## Others ***
## Black_or_African_American ***
## American_Indian_or_Alaska_Native ***
## Asian ***
## Native_Hawaiian_and_Other_Pacific_Islander *
## I(age * age) ***
## I(Bachelor_degree * female) **
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American)
## I(Bachelor_degree * American_Indian_or_Alaska_Native) **
## I(Bachelor_degree * Asian) ***
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) *
## I(Master_degree * female)
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native)
## I(Master_degree * Asian) ***
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Professional_degree * female)
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American)
## I(Professional_degree * American_Indian_or_Alaska_Native) ***
## I(Professional_degree * Asian) ***
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * female)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American) **
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian) **
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.512 on 21296 degrees of freedom
## Multiple R-squared: 0.3106, Adjusted R-squared: 0.3094
## F-statistic: 259.4 on 37 and 21296 DF, p-value: < 2.2e-16BPG test
library(sandwich)
library(lmtest)
bptest(earn_log_higher_edu_race)##
## studentized Breusch-Pagan test
##
## data: earn_log_higher_edu_race
## BP = 691.19, df = 37, p-value < 2.2e-16bp_earn_log_higher_edu_race = lm(residuals(earn_log_higher_edu_race)*residuals(earn_log_higher_edu_race) ~ .-earning -earning_log + I(age*age) + I(Bachelor_degree*female) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*female) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*female) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*female) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander), data=employedwithpay)
summary(bp_earn_log_higher_edu_race)##
## Call:
## lm(formula = residuals(earn_log_higher_edu_race) * residuals(earn_log_higher_edu_race) ~
## . - earning - earning_log + I(age * age) + I(Bachelor_degree *
## female) + I(Bachelor_degree * Others) + I(Bachelor_degree *
## Black_or_African_American) + I(Bachelor_degree * American_Indian_or_Alaska_Native) +
## I(Bachelor_degree * Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * female) + I(Master_degree * Others) +
## I(Master_degree * Black_or_African_American) + I(Master_degree *
## American_Indian_or_Alaska_Native) + I(Master_degree *
## Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * female) + I(Professional_degree *
## Others) + I(Professional_degree * Black_or_African_American) +
## I(Professional_degree * American_Indian_or_Alaska_Native) +
## I(Professional_degree * Asian) + I(Professional_degree *
## Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree *
## female) + I(Doctoral_degree * Others) + I(Doctoral_degree *
## Black_or_African_American) + I(Doctoral_degree * American_Indian_or_Alaska_Native) +
## I(Doctoral_degree * Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6109 -0.2203 -0.1265 0.0499 7.0043
##
## Coefficients: (2 not defined because of singularities)
## Estimate
## (Intercept) -1.551e-01
## age 1.432e-02
## female -5.213e-02
## HSD 1.588e-02
## Some_College 3.959e-02
## Associate_degree 2.224e-02
## Bachelor_degree 1.485e-01
## Master_degree 1.261e-01
## Professional_degree 3.664e-01
## Doctoral_degree 1.919e-01
## Others 5.136e-03
## Black_or_African_American 2.232e-02
## American_Indian_or_Alaska_Native 1.218e-02
## Asian -4.206e-03
## Native_Hawaiian_and_Other_Pacific_Islander -5.693e-02
## I(age * age) -1.280e-04
## I(Bachelor_degree * female) -3.463e-02
## I(Bachelor_degree * Others) -5.312e-02
## I(Bachelor_degree * Black_or_African_American) -5.726e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -2.637e-02
## I(Bachelor_degree * Asian) 9.405e-03
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.360e-02
## I(Master_degree * female) -6.790e-02
## I(Master_degree * Others) 1.079e-02
## I(Master_degree * Black_or_African_American) -9.801e-02
## I(Master_degree * American_Indian_or_Alaska_Native) -2.162e-02
## I(Master_degree * Asian) 2.213e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 9.941e-02
## I(Professional_degree * female) -1.102e-01
## I(Professional_degree * Others) -4.173e-02
## I(Professional_degree * Black_or_African_American) -3.077e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) -3.352e-01
## I(Professional_degree * Asian) -5.936e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) -7.082e-03
## I(Doctoral_degree * Others) 2.731e-02
## I(Doctoral_degree * Black_or_African_American) -4.296e-02
## I(Doctoral_degree * American_Indian_or_Alaska_Native) -3.800e-01
## I(Doctoral_degree * Asian) -1.550e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 4.608e-02
## age 2.072e-03
## female 9.081e-03
## HSD 1.726e-02
## Some_College 1.705e-02
## Associate_degree 1.850e-02
## Bachelor_degree 1.820e-02
## Master_degree 2.112e-02
## Professional_degree 3.205e-02
## Doctoral_degree 3.653e-02
## Others 1.611e-02
## Black_or_African_American 2.482e-02
## American_Indian_or_Alaska_Native 3.122e-02
## Asian 1.835e-02
## Native_Hawaiian_and_Other_Pacific_Islander 5.148e-02
## I(age * age) 2.357e-05
## I(Bachelor_degree * female) 1.546e-02
## I(Bachelor_degree * Others) 3.376e-02
## I(Bachelor_degree * Black_or_African_American) 5.046e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 9.286e-02
## I(Bachelor_degree * Asian) 2.689e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 1.196e-01
## I(Master_degree * female) 2.048e-02
## I(Master_degree * Others) 5.553e-02
## I(Master_degree * Black_or_African_American) 7.165e-02
## I(Master_degree * American_Indian_or_Alaska_Native) 1.187e-01
## I(Master_degree * Asian) 3.166e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 2.405e-01
## I(Professional_degree * female) 4.013e-02
## I(Professional_degree * Others) 9.779e-02
## I(Professional_degree * Black_or_African_American) 1.604e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) 2.742e-01
## I(Professional_degree * Asian) 5.706e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 4.791e-02
## I(Doctoral_degree * Others) 1.151e-01
## I(Doctoral_degree * Black_or_African_American) 1.530e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 4.722e-01
## I(Doctoral_degree * Asian) 5.893e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) -3.365
## age 6.912
## female -5.740
## HSD 0.920
## Some_College 2.322
## Associate_degree 1.202
## Bachelor_degree 8.161
## Master_degree 5.974
## Professional_degree 11.432
## Doctoral_degree 5.253
## Others 0.319
## Black_or_African_American 0.899
## American_Indian_or_Alaska_Native 0.390
## Asian -0.229
## Native_Hawaiian_and_Other_Pacific_Islander -1.106
## I(age * age) -5.431
## I(Bachelor_degree * female) -2.240
## I(Bachelor_degree * Others) -1.574
## I(Bachelor_degree * Black_or_African_American) -1.135
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.284
## I(Bachelor_degree * Asian) 0.350
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.114
## I(Master_degree * female) -3.315
## I(Master_degree * Others) 0.194
## I(Master_degree * Black_or_African_American) -1.368
## I(Master_degree * American_Indian_or_Alaska_Native) -0.182
## I(Master_degree * Asian) 0.699
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.413
## I(Professional_degree * female) -2.747
## I(Professional_degree * Others) -0.427
## I(Professional_degree * Black_or_African_American) -1.918
## I(Professional_degree * American_Indian_or_Alaska_Native) -1.222
## I(Professional_degree * Asian) -1.040
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) -0.148
## I(Doctoral_degree * Others) 0.237
## I(Doctoral_degree * Black_or_African_American) -0.281
## I(Doctoral_degree * American_Indian_or_Alaska_Native) -0.805
## I(Doctoral_degree * Asian) -0.263
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) 0.000766
## age 4.90e-12
## female 9.57e-09
## HSD 0.357674
## Some_College 0.020260
## Associate_degree 0.229216
## Bachelor_degree 3.49e-16
## Master_degree 2.36e-09
## Professional_degree < 2e-16
## Doctoral_degree 1.51e-07
## Others 0.749841
## Black_or_African_American 0.368466
## American_Indian_or_Alaska_Native 0.696498
## Asian 0.818647
## Native_Hawaiian_and_Other_Pacific_Islander 0.268842
## I(age * age) 5.66e-08
## I(Bachelor_degree * female) 0.025130
## I(Bachelor_degree * Others) 0.115589
## I(Bachelor_degree * Black_or_African_American) 0.256470
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.776461
## I(Bachelor_degree * Asian) 0.726569
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.909462
## I(Master_degree * female) 0.000916
## I(Master_degree * Others) 0.845995
## I(Master_degree * Black_or_African_American) 0.171393
## I(Master_degree * American_Indian_or_Alaska_Native) 0.855422
## I(Master_degree * Asian) 0.484510
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.679354
## I(Professional_degree * female) 0.006025
## I(Professional_degree * Others) 0.669566
## I(Professional_degree * Black_or_African_American) 0.055117
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.221556
## I(Professional_degree * Asian) 0.298266
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * female) 0.882481
## I(Doctoral_degree * Others) 0.812354
## I(Doctoral_degree * Black_or_African_American) 0.778826
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.421042
## I(Doctoral_degree * Asian) 0.792476
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## female ***
## HSD
## Some_College *
## Associate_degree
## Bachelor_degree ***
## Master_degree ***
## Professional_degree ***
## Doctoral_degree ***
## Others
## Black_or_African_American
## American_Indian_or_Alaska_Native
## Asian
## Native_Hawaiian_and_Other_Pacific_Islander
## I(age * age) ***
## I(Bachelor_degree * female) *
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American)
## I(Bachelor_degree * American_Indian_or_Alaska_Native)
## I(Bachelor_degree * Asian)
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Master_degree * female) ***
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native)
## I(Master_degree * Asian)
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Professional_degree * female) **
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American) .
## I(Professional_degree * American_Indian_or_Alaska_Native)
## I(Professional_degree * Asian)
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * female)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American)
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian)
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4694 on 21296 degrees of freedom
## Multiple R-squared: 0.0324, Adjusted R-squared: 0.03072
## F-statistic: 19.27 on 37 and 21296 DF, p-value: < 2.2e-16Generate the Robust standard errors
robust_se_log <- diag(vcovHC(earn_log_higher_edu_race, type = "HC"))^0.5
robust_se_log## (Intercept)
## 4.573282e-02
## age
## 2.115939e-03
## female
## 8.697657e-03
## HSD
## 1.641289e-02
## Some_College
## 1.633495e-02
## Associate_degree
## 1.762557e-02
## Bachelor_degree
## 1.875336e-02
## Master_degree
## 2.299335e-02
## Professional_degree
## 4.757952e-02
## Doctoral_degree
## 4.735084e-02
## Others
## 1.562430e-02
## Black_or_African_American
## 2.554036e-02
## American_Indian_or_Alaska_Native
## 3.102617e-02
## Asian
## 1.783313e-02
## Native_Hawaiian_and_Other_Pacific_Islander
## 4.210004e-02
## I(age * age)
## 2.465216e-05
## I(Bachelor_degree * female)
## 1.694868e-02
## I(Bachelor_degree * Others)
## 3.553465e-02
## I(Bachelor_degree * Black_or_African_American)
## 5.518276e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native)
## 1.081597e-01
## I(Bachelor_degree * Asian)
## 2.927904e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## 1.211672e-01
## I(Master_degree * female)
## 2.253478e-02
## I(Master_degree * Others)
## 6.208629e-02
## I(Master_degree * Black_or_African_American)
## 7.167512e-02
## I(Master_degree * American_Indian_or_Alaska_Native)
## 1.221621e-01
## I(Master_degree * Asian)
## 3.501920e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## 2.912581e-01
## I(Professional_degree * female)
## 5.825013e-02
## I(Professional_degree * Others)
## 1.443864e-01
## I(Professional_degree * Black_or_African_American)
## 1.829802e-01
## I(Professional_degree * American_Indian_or_Alaska_Native)
## 2.998204e-01
## I(Professional_degree * Asian)
## 7.967514e-02
## I(Doctoral_degree * female)
## 6.160270e-02
## I(Doctoral_degree * Others)
## 1.561938e-01
## I(Doctoral_degree * Black_or_African_American)
## 1.973773e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## 6.004792e-02
## I(Doctoral_degree * Asian)
## 7.461530e-02#caluclate t-statistics using robust standard error
coeftest(earn_log_higher_edu_race, vcov = vcovHC(earn_log_higher_edu_race))##
## t test of coefficients:
##
## Estimate
## (Intercept) 9.0918e+00
## age 7.0423e-02
## female -2.5345e-01
## HSD 1.6334e-01
## Some_College 2.7620e-01
## Associate_degree 3.0611e-01
## Bachelor_degree 6.4697e-01
## Master_degree 7.2758e-01
## Professional_degree 9.8801e-01
## Doctoral_degree 9.4110e-01
## Others -1.0864e-01
## Black_or_African_American -1.5230e-01
## American_Indian_or_Alaska_Native -1.1427e-01
## Asian -1.3672e-01
## Native_Hawaiian_and_Other_Pacific_Islander -1.3559e-01
## I(age * age) -6.8535e-04
## I(Bachelor_degree * female) -5.0456e-02
## I(Bachelor_degree * Others) 3.6750e-02
## I(Bachelor_degree * Black_or_African_American) 3.3442e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -2.6851e-01
## I(Bachelor_degree * Asian) 1.1678e-01
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -3.0425e-01
## I(Master_degree * female) -2.3931e-02
## I(Master_degree * Others) 4.4916e-02
## I(Master_degree * Black_or_African_American) -1.0444e-01
## I(Master_degree * American_Indian_or_Alaska_Native) -7.7306e-02
## I(Master_degree * Asian) 4.3371e-01
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -3.1500e-01
## I(Professional_degree * female) 1.4651e-02
## I(Professional_degree * Others) -1.9992e-02
## I(Professional_degree * Black_or_African_American) -1.5499e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) -9.9756e-01
## I(Professional_degree * Asian) 3.2756e-01
## I(Doctoral_degree * female) 4.2509e-02
## I(Doctoral_degree * Others) -2.4015e-03
## I(Doctoral_degree * Black_or_African_American) 4.6307e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 3.9683e-01
## I(Doctoral_degree * Asian) 1.9395e-01
## Std. Error
## (Intercept) 4.5816e-02
## age 2.1198e-03
## female 8.7059e-03
## HSD 1.6440e-02
## Some_College 1.6362e-02
## Associate_degree 1.7654e-02
## Bachelor_degree 1.8779e-02
## Master_degree 2.3031e-02
## Professional_degree 4.7778e-02
## Doctoral_degree 4.7658e-02
## Others 1.5647e-02
## Black_or_African_American 2.5618e-02
## American_Indian_or_Alaska_Native 3.1174e-02
## Asian 1.7865e-02
## Native_Hawaiian_and_Other_Pacific_Islander 4.2617e-02
## I(age * age) 2.4698e-05
## I(Bachelor_degree * female) 1.6969e-02
## I(Bachelor_degree * Others) 3.5654e-02
## I(Bachelor_degree * Black_or_African_American) 5.5595e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 1.1176e-01
## I(Bachelor_degree * Asian) 2.9329e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 1.2727e-01
## I(Master_degree * female) 2.2590e-02
## I(Master_degree * Others) 6.2821e-02
## I(Master_degree * Black_or_African_American) 7.2963e-02
## I(Master_degree * American_Indian_or_Alaska_Native) 1.2932e-01
## I(Master_degree * Asian) 3.5104e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 3.8669e-01
## I(Professional_degree * female) 5.8628e-02
## I(Professional_degree * Others) 1.5022e-01
## I(Professional_degree * Black_or_African_American) 2.0442e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) 4.4561e-01
## I(Professional_degree * Asian) 8.0541e-02
## I(Doctoral_degree * female) 6.2393e-02
## I(Doctoral_degree * Others) 1.6518e-01
## I(Doctoral_degree * Black_or_African_American) 2.1893e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 3.8141e-01
## I(Doctoral_degree * Asian) 7.5436e-02
## t value
## (Intercept) 198.4414
## age 33.2217
## female -29.1119
## HSD 9.9350
## Some_College 16.8805
## Associate_degree 17.3399
## Bachelor_degree 34.4513
## Master_degree 31.5911
## Professional_degree 20.6790
## Doctoral_degree 19.7470
## Others -6.9432
## Black_or_African_American -5.9451
## American_Indian_or_Alaska_Native -3.6657
## Asian -7.6530
## Native_Hawaiian_and_Other_Pacific_Islander -3.1817
## I(age * age) -27.7493
## I(Bachelor_degree * female) -2.9735
## I(Bachelor_degree * Others) 1.0307
## I(Bachelor_degree * Black_or_African_American) 0.6015
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -2.4026
## I(Bachelor_degree * Asian) 3.9817
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -2.3905
## I(Master_degree * female) -1.0593
## I(Master_degree * Others) 0.7150
## I(Master_degree * Black_or_African_American) -1.4314
## I(Master_degree * American_Indian_or_Alaska_Native) -0.5978
## I(Master_degree * Asian) 12.3551
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.8146
## I(Professional_degree * female) 0.2499
## I(Professional_degree * Others) -0.1331
## I(Professional_degree * Black_or_African_American) -0.7582
## I(Professional_degree * American_Indian_or_Alaska_Native) -2.2387
## I(Professional_degree * Asian) 4.0670
## I(Doctoral_degree * female) 0.6813
## I(Doctoral_degree * Others) -0.0145
## I(Doctoral_degree * Black_or_African_American) 2.1152
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 1.0404
## I(Doctoral_degree * Asian) 2.5711
## Pr(>|t|)
## (Intercept) < 2.2e-16 ***
## age < 2.2e-16 ***
## female < 2.2e-16 ***
## HSD < 2.2e-16 ***
## Some_College < 2.2e-16 ***
## Associate_degree < 2.2e-16 ***
## Bachelor_degree < 2.2e-16 ***
## Master_degree < 2.2e-16 ***
## Professional_degree < 2.2e-16 ***
## Doctoral_degree < 2.2e-16 ***
## Others 3.943e-12 ***
## Black_or_African_American 2.805e-09 ***
## American_Indian_or_Alaska_Native 0.0002473 ***
## Asian 2.047e-14 ***
## Native_Hawaiian_and_Other_Pacific_Islander 0.0014662 **
## I(age * age) < 2.2e-16 ***
## I(Bachelor_degree * female) 0.0029480 **
## I(Bachelor_degree * Others) 0.3026780
## I(Bachelor_degree * Black_or_African_American) 0.5474882
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.0162871 *
## I(Bachelor_degree * Asian) 6.865e-05 ***
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.0168319 *
## I(Master_degree * female) 0.2894540
## I(Master_degree * Others) 0.4746225
## I(Master_degree * Black_or_African_American) 0.1523337
## I(Master_degree * American_Indian_or_Alaska_Native) 0.5499839
## I(Master_degree * Asian) < 2.2e-16 ***
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.4153011
## I(Professional_degree * female) 0.8026658
## I(Professional_degree * Others) 0.8941281
## I(Professional_degree * Black_or_African_American) 0.4483380
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.0251879 *
## I(Professional_degree * Asian) 4.779e-05 ***
## I(Doctoral_degree * female) 0.4956788
## I(Doctoral_degree * Others) 0.9884006
## I(Doctoral_degree * Black_or_African_American) 0.0344281 *
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.2981594
## I(Doctoral_degree * Asian) 0.0101442 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic Age & Higher Education by Female
#female sample data
employedwithpay_female <- subset(employedwithpay, female == 1)
employedwithpay_female## earning age female HSD Some_College Associate_degree Bachelor_degree
## 8 44492.32 45 1 0 0 0 1
## 11 85951.06 31 1 0 0 0 1
## 18 80895.12 53 1 1 0 0 0
## 31 136510.51 55 1 0 0 1 0
## 34 50559.45 48 1 0 0 0 1
## 41 41964.34 27 1 0 0 0 1
## 42 45503.50 31 1 0 0 0 1
## 51 91007.01 64 1 0 0 1 0
## 55 33369.24 24 1 0 0 0 1
## 58 24268.54 34 1 1 0 0 0
## 63 131454.57 31 1 0 0 0 1
## 84 64716.10 46 1 0 0 1 0
## 91 40245.32 53 1 1 0 0 0
## 98 49548.26 32 1 0 1 0 0
## 100 77861.55 54 1 0 0 0 0
## 101 54604.21 33 1 0 1 0 0
## 123 85951.06 42 1 0 0 0 1
## 126 49548.26 64 1 0 1 0 0
## 140 40447.56 45 1 0 0 0 1
## 142 41964.34 62 1 0 1 0 0
## 143 202237.80 60 1 0 0 0 0
## 147 101118.90 30 1 0 0 0 0
## 152 62693.72 52 1 0 0 1 0
## 155 83928.69 43 1 0 0 0 1
## 165 28313.29 50 1 0 1 0 0
## 177 35391.61 63 1 0 0 0 1
## 205 23257.35 35 1 0 0 0 0
## 208 50559.45 61 1 0 1 0 0
## 213 27807.70 24 1 1 0 0 0
## 214 30335.67 39 1 0 1 0 0
## 228 36402.80 26 1 0 0 0 1
## 232 53593.02 46 1 0 1 0 0
## 235 65727.28 44 1 0 1 0 0
## 236 63704.91 42 1 0 1 0 0
## 256 30335.67 28 1 1 0 0 0
## 273 27302.10 27 1 0 0 1 0
## 288 35391.61 56 1 1 0 0 0
## 291 62693.72 47 1 0 1 0 0
## 296 42469.94 49 1 0 0 0 0
## 314 31346.86 25 1 0 0 0 1
## 315 40447.56 34 1 1 0 0 0
## 318 85951.06 61 1 0 0 1 0
## 334 37919.59 53 1 0 0 1 0
## 337 24976.37 62 1 0 0 0 1
## 342 69772.04 44 1 0 0 0 0
## 353 91007.01 34 1 0 0 1 0
## 360 53593.02 32 1 0 1 0 0
## 365 106174.84 38 1 0 0 0 1
## 375 136510.51 34 1 0 0 0 1
## 378 26189.80 22 1 0 1 0 0
## 403 51570.64 36 1 0 1 0 0
## 414 30335.67 63 1 0 0 0 0
## 450 76850.36 54 1 1 0 0 0
## 458 24268.54 55 1 1 0 0 0
## 465 30335.67 36 1 1 0 0 0
## 469 43481.13 23 1 0 1 0 0
## 473 73816.80 49 1 0 0 0 0
## 478 59255.68 41 1 0 0 0 0
## 491 30335.67 26 1 0 1 0 0
## 494 49548.26 33 1 0 0 0 0
## 495 40447.56 36 1 0 0 0 0
## 516 131454.57 48 1 0 0 0 1
## 522 146622.40 66 1 1 0 0 0
## 526 111230.79 42 1 0 0 0 1
## 539 116286.73 26 1 0 0 0 1
## 542 44492.32 25 1 0 0 0 1
## 544 116286.73 40 1 0 0 1 0
## 547 65727.28 41 1 0 0 0 0
## 567 62693.72 48 1 0 0 0 0
## 580 25279.72 35 1 0 0 0 1
## 585 30335.67 54 1 0 1 0 0
## 590 40447.56 22 1 0 1 0 0
## 592 58648.96 42 1 0 0 0 1
## 602 84939.88 63 1 0 0 1 0
## 618 65727.28 43 1 0 0 0 0
## 626 52581.83 60 1 0 0 0 1
## 637 31549.10 37 1 0 0 1 0
## 650 50559.45 30 1 0 0 0 1
## 662 52581.83 51 1 0 1 0 0
## 675 75839.17 47 1 0 0 0 0
## 686 32792.86 32 1 0 0 0 1
## 691 36402.80 61 1 1 0 0 0
## 693 50559.45 41 1 0 1 0 0
## 701 85951.06 53 1 0 0 0 1
## 715 75839.17 63 1 0 0 0 1
## 717 40447.56 57 1 0 0 1 0
## 731 30335.67 44 1 0 0 1 0
## 749 145611.22 40 1 0 0 0 0
## 753 24268.54 20 1 0 0 1 0
## 761 33369.24 22 1 1 0 0 0
## 768 26290.91 61 1 0 0 0 1
## 773 70783.23 56 1 0 1 0 0
## 780 30335.67 26 1 1 0 0 0
## 782 25279.72 41 1 0 0 1 0
## 787 24268.54 21 1 0 0 1 0
## 789 35694.97 60 1 0 0 1 0
## 797 40447.56 26 1 0 0 1 0
## 799 51166.16 32 1 0 0 0 1
## 809 45503.50 50 1 0 0 0 1
## 811 116286.73 27 1 0 0 0 1
## 812 50559.45 25 1 0 0 0 1
## 817 65727.28 24 1 0 0 0 1
## 818 93029.39 55 1 0 0 0 0
## 820 182014.02 47 1 0 0 0 1
## 824 101118.90 55 1 1 0 0 0
## 829 73816.80 54 1 0 0 0 1
## 834 121342.68 52 1 0 0 0 0
## 838 141566.46 30 1 0 0 0 0
## 847 60671.34 31 1 0 0 0 0
## 863 27453.78 24 1 0 1 0 0
## 872 80895.12 60 1 0 0 0 0
## 879 25279.72 41 1 0 0 1 0
## 890 54604.21 40 1 0 0 1 0
## 892 50559.45 63 1 0 0 0 0
## 893 151678.35 56 1 1 0 0 0
## 895 29324.48 29 1 0 0 0 1
## 901 92018.20 64 1 0 0 0 1
## 902 32054.69 63 1 0 0 1 0
## 910 46312.46 61 1 0 1 0 0
## 915 91007.01 40 1 0 0 0 1
## 935 36402.80 46 1 0 1 0 0
## 942 39436.37 23 1 0 0 0 1
## 952 50559.45 36 1 0 1 0 0
## 966 80895.12 33 1 0 0 0 0
## 971 41458.75 52 1 0 0 0 1
## 986 130443.38 38 1 0 0 0 0
## 996 28313.29 37 1 0 1 0 0
## 1003 232573.47 45 1 0 0 0 1
## 1007 101118.90 46 1 0 0 0 0
## 1012 59660.15 51 1 0 0 0 0
## 1014 80895.12 60 1 0 1 0 0
## 1015 66738.47 27 1 0 1 0 0
## 1017 42469.94 52 1 0 0 0 1
## 1022 52581.83 59 1 0 0 0 1
## 1042 49042.67 37 1 0 0 0 1
## 1045 30335.67 57 1 0 1 0 0
## 1049 50559.45 25 1 0 0 0 1
## 1050 151678.35 36 1 0 0 0 0
## 1061 41054.27 31 1 0 0 0 0
## 1067 86962.25 32 1 0 0 1 0
## 1071 36402.80 52 1 1 0 0 0
## 1081 91007.01 46 1 0 0 0 1
## 1090 42469.94 29 1 0 0 0 1
## 1093 40447.56 26 1 0 0 0 1
## 1096 26290.91 23 1 0 1 0 0
## 1102 72805.61 61 1 0 0 1 0
## 1110 478292.40 43 1 0 0 0 1
## 1115 40447.56 38 1 0 0 0 1
## 1144 56626.58 46 1 0 1 0 0
## 1171 35391.61 23 1 1 0 0 0
## 1182 40447.56 56 1 0 0 1 0
## 1183 60671.34 59 1 1 0 0 0
## 1204 28313.29 27 1 1 0 0 0
## 1206 62693.72 46 1 0 0 0 1
## 1213 24268.54 24 1 1 0 0 0
## 1215 252797.25 48 1 0 0 0 0
## 1240 131454.57 55 1 1 0 0 0
## 1255 106174.84 55 1 0 0 0 0
## 1257 41458.75 56 1 0 1 0 0
## 1263 45503.50 32 1 0 1 0 0
## 1264 75839.17 29 1 0 0 0 1
## 1265 40447.56 48 1 0 1 0 0
## 1272 104152.47 51 1 0 0 0 0
## 1285 33975.95 19 1 0 0 1 0
## 1288 55615.39 48 1 0 0 0 0
## 1302 42469.94 26 1 0 0 0 1
## 1304 58648.96 62 1 1 0 0 0
## 1316 76850.36 58 1 0 0 0 0
## 1318 60671.34 29 1 0 0 0 0
## 1322 70783.23 60 1 0 1 0 0
## 1326 60671.34 36 1 0 0 0 1
## 1344 32358.05 56 1 1 0 0 0
## 1346 42469.94 26 1 0 0 0 1
## 1352 44896.79 62 1 0 1 0 0
## 1354 28313.29 25 1 1 0 0 0
## 1363 56626.58 57 1 0 1 0 0
## 1366 75839.17 41 1 0 0 0 0
## 1378 55615.39 56 1 0 0 1 0
## 1380 58648.96 48 1 0 0 1 0
## 1388 55615.39 39 1 0 0 0 0
## 1391 23257.35 43 1 0 1 0 0
## 1403 70783.23 42 1 0 0 0 0
## 1415 43481.13 35 1 0 0 1 0
## 1428 49244.90 26 1 0 1 0 0
## 1433 34380.43 30 1 0 1 0 0
## 1436 101118.90 62 1 0 0 0 1
## 1441 45503.50 62 1 0 1 0 0
## 1443 101118.90 65 1 0 0 1 0
## 1454 61682.53 56 1 0 0 1 0
## 1460 26290.91 47 1 0 0 0 0
## 1469 60671.34 46 1 0 0 0 0
## 1475 141566.46 47 1 0 0 0 0
## 1483 68760.85 54 1 0 0 1 0
## 1492 151678.35 28 1 0 0 0 1
## 1497 70783.23 43 1 0 0 0 0
## 1504 85951.06 36 1 0 0 0 1
## 1510 61682.53 62 1 0 0 0 1
## 1523 50559.45 54 1 0 0 0 0
## 1537 161790.24 37 1 0 0 0 1
## 1541 93029.39 39 1 0 0 1 0
## 1546 30335.67 63 1 1 0 0 0
## 1548 36807.28 59 1 0 1 0 0
## 1551 36402.80 33 1 0 0 1 0
## 1559 101118.90 62 1 0 0 0 0
## 1560 40447.56 23 1 0 0 0 1
## 1564 30335.67 57 1 1 0 0 0
## 1575 49548.26 66 1 0 0 1 0
## 1582 37413.99 23 1 0 1 0 0
## 1602 97074.14 55 1 0 0 0 0
## 1623 50559.45 42 1 0 0 0 0
## 1631 26695.39 47 1 0 1 0 0
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## 53976 0 10.79008
## 54027 0 11.20934
## [ reached 'max' / getOption("max.print") -- omitted 2122 rows ]earning_log_equation_female = lm(log(earning,base = exp(1)) ~ . -earning -earning_log - female + I(age*age) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander) , data=employedwithpay_female)## Warning in terms.formula(formula, data = data): 'varlist' has changed (from
## nvar=37) to new 38 after EncodeVars() -- should no longer happen!summary(earning_log_equation_female)##
## Call:
## lm(formula = log(earning, base = exp(1)) ~ . - earning - earning_log -
## female + I(age * age) + I(Bachelor_degree * Others) + I(Bachelor_degree *
## Black_or_African_American) + I(Bachelor_degree * American_Indian_or_Alaska_Native) +
## I(Bachelor_degree * Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * Others) + I(Master_degree * Black_or_African_American) +
## I(Master_degree * American_Indian_or_Alaska_Native) + I(Master_degree *
## Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * Others) + I(Professional_degree *
## Black_or_African_American) + I(Professional_degree * American_Indian_or_Alaska_Native) +
## I(Professional_degree * Asian) + I(Professional_degree *
## Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree *
## Others) + I(Doctoral_degree * Black_or_African_American) +
## I(Doctoral_degree * American_Indian_or_Alaska_Native) + I(Doctoral_degree *
## Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay_female)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.48944 -0.32333 -0.04333 0.27989 2.42715
##
## Coefficients: (3 not defined because of singularities)
## Estimate
## (Intercept) 9.065e+00
## age 6.158e-02
## HSD 1.482e-01
## Some_College 2.338e-01
## Associate_degree 2.766e-01
## Bachelor_degree 5.751e-01
## Master_degree 6.940e-01
## Professional_degree 9.789e-01
## Doctoral_degree 9.894e-01
## Others -5.058e-02
## Black_or_African_American -3.022e-02
## American_Indian_or_Alaska_Native -1.292e-02
## Asian -6.848e-02
## Native_Hawaiian_and_Other_Pacific_Islander -1.077e-01
## I(age * age) -6.007e-04
## I(Bachelor_degree * Others) 4.154e-02
## I(Bachelor_degree * Black_or_African_American) -4.527e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -8.967e-02
## I(Bachelor_degree * Asian) 3.250e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -2.470e-01
## I(Master_degree * Others) -1.543e-02
## I(Master_degree * Black_or_African_American) -1.553e-01
## I(Master_degree * American_Indian_or_Alaska_Native) -2.666e-01
## I(Master_degree * Asian) 3.459e-01
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 8.419e-02
## I(Professional_degree * Others) -7.941e-02
## I(Professional_degree * Black_or_African_American) -2.135e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 3.477e-01
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 7.443e-02
## I(Doctoral_degree * Black_or_African_American) 3.853e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 2.799e-01
## I(Doctoral_degree * Asian) 2.306e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 7.616e-02
## age 3.320e-03
## HSD 3.439e-02
## Some_College 3.361e-02
## Associate_degree 3.510e-02
## Bachelor_degree 3.411e-02
## Master_degree 3.553e-02
## Professional_degree 4.600e-02
## Doctoral_degree 5.444e-02
## Others 2.819e-02
## Black_or_African_American 4.125e-02
## American_Indian_or_Alaska_Native 4.785e-02
## Asian 2.758e-02
## Native_Hawaiian_and_Other_Pacific_Islander 8.513e-02
## I(age * age) 3.775e-05
## I(Bachelor_degree * Others) 5.191e-02
## I(Bachelor_degree * Black_or_African_American) 7.856e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 1.352e-01
## I(Bachelor_degree * Asian) 3.992e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 1.790e-01
## I(Master_degree * Others) 7.779e-02
## I(Master_degree * Black_or_African_American) 1.194e-01
## I(Master_degree * American_Indian_or_Alaska_Native) 1.356e-01
## I(Master_degree * Asian) 4.912e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 3.443e-01
## I(Professional_degree * Others) 1.721e-01
## I(Professional_degree * Black_or_African_American) 4.743e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 8.764e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 1.856e-01
## I(Doctoral_degree * Black_or_African_American) 2.432e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 4.760e-01
## I(Doctoral_degree * Asian) 9.318e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) 119.023
## age 18.552
## HSD 4.310
## Some_College 6.958
## Associate_degree 7.881
## Bachelor_degree 16.861
## Master_degree 19.535
## Professional_degree 21.278
## Doctoral_degree 18.175
## Others -1.794
## Black_or_African_American -0.732
## American_Indian_or_Alaska_Native -0.270
## Asian -2.483
## Native_Hawaiian_and_Other_Pacific_Islander -1.265
## I(age * age) -15.911
## I(Bachelor_degree * Others) 0.800
## I(Bachelor_degree * Black_or_African_American) -0.576
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.663
## I(Bachelor_degree * Asian) 0.814
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.380
## I(Master_degree * Others) -0.198
## I(Master_degree * Black_or_African_American) -1.301
## I(Master_degree * American_Indian_or_Alaska_Native) -1.967
## I(Master_degree * Asian) 7.041
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.245
## I(Professional_degree * Others) -0.461
## I(Professional_degree * Black_or_African_American) -0.450
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 3.968
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.401
## I(Doctoral_degree * Black_or_African_American) 1.584
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.588
## I(Doctoral_degree * Asian) 0.247
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) < 2e-16
## age < 2e-16
## HSD 1.65e-05
## Some_College 3.72e-12
## Associate_degree 3.66e-15
## Bachelor_degree < 2e-16
## Master_degree < 2e-16
## Professional_degree < 2e-16
## Doctoral_degree < 2e-16
## Others 0.0729
## Black_or_African_American 0.4639
## American_Indian_or_Alaska_Native 0.7871
## Asian 0.0130
## Native_Hawaiian_and_Other_Pacific_Islander 0.2060
## I(age * age) < 2e-16
## I(Bachelor_degree * Others) 0.4236
## I(Bachelor_degree * Black_or_African_American) 0.5645
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.5072
## I(Bachelor_degree * Asian) 0.4155
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.1677
## I(Master_degree * Others) 0.8428
## I(Master_degree * Black_or_African_American) 0.1934
## I(Master_degree * American_Indian_or_Alaska_Native) 0.0493
## I(Master_degree * Asian) 2.06e-12
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.8068
## I(Professional_degree * Others) 0.6446
## I(Professional_degree * Black_or_African_American) 0.6526
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 7.32e-05
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.6884
## I(Doctoral_degree * Black_or_African_American) 0.1132
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.5566
## I(Doctoral_degree * Asian) 0.8045
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## HSD ***
## Some_College ***
## Associate_degree ***
## Bachelor_degree ***
## Master_degree ***
## Professional_degree ***
## Doctoral_degree ***
## Others .
## Black_or_African_American
## American_Indian_or_Alaska_Native
## Asian *
## Native_Hawaiian_and_Other_Pacific_Islander
## I(age * age) ***
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American)
## I(Bachelor_degree * American_Indian_or_Alaska_Native)
## I(Bachelor_degree * Asian)
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native) *
## I(Master_degree * Asian) ***
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American)
## I(Professional_degree * American_Indian_or_Alaska_Native)
## I(Professional_degree * Asian) ***
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American)
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian)
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4713 on 8339 degrees of freedom
## Multiple R-squared: 0.2826, Adjusted R-squared: 0.2799
## F-statistic: 106 on 31 and 8339 DF, p-value: < 2.2e-16BPG test
bptest(earning_log_equation_female)##
## studentized Breusch-Pagan test
##
## data: earning_log_equation_female
## BP = 238.91, df = 31, p-value < 2.2e-16bptestlogequation_female = lm(residuals(earning_log_equation_female)*residuals(earning_log_equation_female) ~ .-earning -earning_log- female + I(age*age) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander) , data=employedwithpay_female)
summary(bptestlogequation_female)##
## Call:
## lm(formula = residuals(earning_log_equation_female) * residuals(earning_log_equation_female) ~
## . - earning - earning_log - female + I(age * age) + I(Bachelor_degree *
## Others) + I(Bachelor_degree * Black_or_African_American) +
## I(Bachelor_degree * American_Indian_or_Alaska_Native) +
## I(Bachelor_degree * Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * Others) + I(Master_degree * Black_or_African_American) +
## I(Master_degree * American_Indian_or_Alaska_Native) +
## I(Master_degree * Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * Others) + I(Professional_degree *
## Black_or_African_American) + I(Professional_degree *
## American_Indian_or_Alaska_Native) + I(Professional_degree *
## Asian) + I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Doctoral_degree * Others) + I(Doctoral_degree * Black_or_African_American) +
## I(Doctoral_degree * American_Indian_or_Alaska_Native) +
## I(Doctoral_degree * Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay_female)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4542 -0.1892 -0.1030 0.0444 5.7260
##
## Coefficients: (3 not defined because of singularities)
## Estimate
## (Intercept) -3.278e-01
## age 1.966e-02
## HSD 3.167e-02
## Some_College 4.177e-02
## Associate_degree 5.104e-02
## Bachelor_degree 1.220e-01
## Master_degree 6.387e-02
## Professional_degree 2.615e-01
## Doctoral_degree 2.014e-01
## Others 2.002e-02
## Black_or_African_American 5.488e-02
## American_Indian_or_Alaska_Native 7.842e-04
## Asian 9.611e-03
## Native_Hawaiian_and_Other_Pacific_Islander -1.024e-02
## I(age * age) -1.913e-04
## I(Bachelor_degree * Others) -5.302e-02
## I(Bachelor_degree * Black_or_African_American) 1.697e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -8.916e-03
## I(Bachelor_degree * Asian) 4.029e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -8.757e-02
## I(Master_degree * Others) 9.005e-03
## I(Master_degree * Black_or_African_American) -1.120e-01
## I(Master_degree * American_Indian_or_Alaska_Native) 2.772e-02
## I(Master_degree * Asian) 8.487e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.933e-01
## I(Professional_degree * Others) -6.193e-02
## I(Professional_degree * Black_or_African_American) -4.645e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 1.964e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) -1.970e-01
## I(Doctoral_degree * Black_or_African_American) 5.522e-02
## I(Doctoral_degree * American_Indian_or_Alaska_Native) -3.443e-01
## I(Doctoral_degree * Asian) -1.192e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 6.633e-02
## age 2.891e-03
## HSD 2.995e-02
## Some_College 2.927e-02
## Associate_degree 3.057e-02
## Bachelor_degree 2.970e-02
## Master_degree 3.094e-02
## Professional_degree 4.006e-02
## Doctoral_degree 4.741e-02
## Others 2.455e-02
## Black_or_African_American 3.592e-02
## American_Indian_or_Alaska_Native 4.167e-02
## Asian 2.402e-02
## Native_Hawaiian_and_Other_Pacific_Islander 7.414e-02
## I(age * age) 3.288e-05
## I(Bachelor_degree * Others) 4.521e-02
## I(Bachelor_degree * Black_or_African_American) 6.842e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 1.177e-01
## I(Bachelor_degree * Asian) 3.477e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 1.559e-01
## I(Master_degree * Others) 6.775e-02
## I(Master_degree * Black_or_African_American) 1.040e-01
## I(Master_degree * American_Indian_or_Alaska_Native) 1.180e-01
## I(Master_degree * Asian) 4.278e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 2.998e-01
## I(Professional_degree * Others) 1.499e-01
## I(Professional_degree * Black_or_African_American) 4.130e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 7.632e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 1.616e-01
## I(Doctoral_degree * Black_or_African_American) 2.118e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 4.146e-01
## I(Doctoral_degree * Asian) 8.115e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) -4.943
## age 6.803
## HSD 1.058
## Some_College 1.427
## Associate_degree 1.670
## Bachelor_degree 4.106
## Master_degree 2.064
## Professional_degree 6.526
## Doctoral_degree 4.248
## Others 0.815
## Black_or_African_American 1.528
## American_Indian_or_Alaska_Native 0.019
## Asian 0.400
## Native_Hawaiian_and_Other_Pacific_Islander -0.138
## I(age * age) -5.819
## I(Bachelor_degree * Others) -1.173
## I(Bachelor_degree * Black_or_African_American) 0.248
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.076
## I(Bachelor_degree * Asian) 1.159
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.562
## I(Master_degree * Others) 0.133
## I(Master_degree * Black_or_African_American) -1.077
## I(Master_degree * American_Indian_or_Alaska_Native) 0.235
## I(Master_degree * Asian) 1.984
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -0.645
## I(Professional_degree * Others) -0.413
## I(Professional_degree * Black_or_African_American) -1.125
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 0.257
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) -1.219
## I(Doctoral_degree * Black_or_African_American) 0.261
## I(Doctoral_degree * American_Indian_or_Alaska_Native) -0.830
## I(Doctoral_degree * Asian) -0.147
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) 7.86e-07
## age 1.10e-11
## HSD 0.2903
## Some_College 0.1536
## Associate_degree 0.0950
## Bachelor_degree 4.06e-05
## Master_degree 0.0390
## Professional_degree 7.13e-11
## Doctoral_degree 2.18e-05
## Others 0.4149
## Black_or_African_American 0.1266
## American_Indian_or_Alaska_Native 0.9850
## Asian 0.6890
## Native_Hawaiian_and_Other_Pacific_Islander 0.8902
## I(age * age) 6.14e-09
## I(Bachelor_degree * Others) 0.2409
## I(Bachelor_degree * Black_or_African_American) 0.8042
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.9396
## I(Bachelor_degree * Asian) 0.2465
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.5743
## I(Master_degree * Others) 0.8943
## I(Master_degree * Black_or_African_American) 0.2815
## I(Master_degree * American_Indian_or_Alaska_Native) 0.8143
## I(Master_degree * Asian) 0.0473
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.5191
## I(Professional_degree * Others) 0.6795
## I(Professional_degree * Black_or_African_American) 0.2608
## I(Professional_degree * American_Indian_or_Alaska_Native) NA
## I(Professional_degree * Asian) 0.7969
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.2231
## I(Doctoral_degree * Black_or_African_American) 0.7943
## I(Doctoral_degree * American_Indian_or_Alaska_Native) 0.4063
## I(Doctoral_degree * Asian) 0.8832
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## HSD
## Some_College
## Associate_degree .
## Bachelor_degree ***
## Master_degree *
## Professional_degree ***
## Doctoral_degree ***
## Others
## Black_or_African_American
## American_Indian_or_Alaska_Native
## Asian
## Native_Hawaiian_and_Other_Pacific_Islander
## I(age * age) ***
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American)
## I(Bachelor_degree * American_Indian_or_Alaska_Native)
## I(Bachelor_degree * Asian)
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native)
## I(Master_degree * Asian) *
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American)
## I(Professional_degree * American_Indian_or_Alaska_Native)
## I(Professional_degree * Asian)
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American)
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian)
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4105 on 8339 degrees of freedom
## Multiple R-squared: 0.02854, Adjusted R-squared: 0.02493
## F-statistic: 7.903 on 31 and 8339 DF, p-value: < 2.2e-16Quadratic Age & Higher Education by Male
#female sample data
employedwithpay_male <- subset(employedwithpay, female == 0)
employedwithpay_male## earning age female HSD Some_College Associate_degree Bachelor_degree
## 1 91007.01 47 0 0 0 0 1
## 3 116691.21 38 0 0 0 1 0
## 16 55615.39 64 0 0 0 0 1
## 19 78872.74 53 0 1 0 0 0
## 25 56626.58 54 0 1 0 0 0
## 27 101118.90 41 0 0 0 0 0
## 37 252797.25 48 0 0 1 0 0
## 40 75839.17 44 0 1 0 0 0
## 54 40447.56 24 0 0 0 0 1
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## Native_Hawaiian_and_Other_Pacific_Islander earning_log
## 1 0 11.41869
## 3 0 11.66729
## 16 0 10.92622
## 19 0 11.27559
## 25 0 10.94423
## 27 0 11.52405
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## [ reached 'max' / getOption("max.print") -- omitted 6714 rows ]earning_log_equation_male = lm(log(earning,base = exp(1)) ~ . -earning -earning_log - female + I(age*age) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander) , data=employedwithpay_male)## Warning in terms.formula(formula, data = data): 'varlist' has changed (from
## nvar=37) to new 38 after EncodeVars() -- should no longer happen!summary(earning_log_equation_male)##
## Call:
## lm(formula = log(earning, base = exp(1)) ~ . - earning - earning_log -
## female + I(age * age) + I(Bachelor_degree * Others) + I(Bachelor_degree *
## Black_or_African_American) + I(Bachelor_degree * American_Indian_or_Alaska_Native) +
## I(Bachelor_degree * Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * Others) + I(Master_degree * Black_or_African_American) +
## I(Master_degree * American_Indian_or_Alaska_Native) + I(Master_degree *
## Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * Others) + I(Professional_degree *
## Black_or_African_American) + I(Professional_degree * American_Indian_or_Alaska_Native) +
## I(Professional_degree * Asian) + I(Professional_degree *
## Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree *
## Others) + I(Doctoral_degree * Black_or_African_American) +
## I(Doctoral_degree * American_Indian_or_Alaska_Native) + I(Doctoral_degree *
## Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay_male)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.91740 -0.35341 -0.03129 0.31049 2.69042
##
## Coefficients: (3 not defined because of singularities)
## Estimate
## (Intercept) 8.956e+00
## age 7.601e-02
## HSD 1.667e-01
## Some_College 2.927e-01
## Associate_degree 3.155e-01
## Bachelor_degree 6.496e-01
## Master_degree 7.226e-01
## Professional_degree 9.949e-01
## Doctoral_degree 9.249e-01
## Others -1.365e-01
## Black_or_African_American -2.220e-01
## American_Indian_or_Alaska_Native -1.867e-01
## Asian -1.926e-01
## Native_Hawaiian_and_Other_Pacific_Islander -1.569e-01
## I(age * age) -7.380e-04
## I(Bachelor_degree * Others) 1.053e-02
## I(Bachelor_degree * Black_or_African_American) 7.136e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -4.592e-01
## I(Bachelor_degree * Asian) 1.877e-01
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -3.647e-01
## I(Master_degree * Others) 7.287e-02
## I(Master_degree * Black_or_African_American) -7.497e-02
## I(Master_degree * American_Indian_or_Alaska_Native) 4.050e-01
## I(Master_degree * Asian) 5.019e-01
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -7.328e-01
## I(Professional_degree * Others) -2.292e-03
## I(Professional_degree * Black_or_African_American) -1.032e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) -9.363e-01
## I(Professional_degree * Asian) 3.147e-01
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) -6.329e-02
## I(Doctoral_degree * Black_or_African_American) 5.120e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 3.176e-01
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 6.675e-02
## age 3.037e-03
## HSD 2.287e-02
## Some_College 2.275e-02
## Associate_degree 2.532e-02
## Bachelor_degree 2.330e-02
## Master_degree 2.666e-02
## Professional_degree 3.919e-02
## Doctoral_degree 4.494e-02
## Others 2.244e-02
## Black_or_African_American 3.553e-02
## American_Indian_or_Alaska_Native 4.717e-02
## Asian 2.825e-02
## Native_Hawaiian_and_Other_Pacific_Islander 7.397e-02
## I(age * age) 3.454e-05
## I(Bachelor_degree * Others) 5.150e-02
## I(Bachelor_degree * Black_or_African_American) 7.557e-02
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 1.465e-01
## I(Bachelor_degree * Asian) 4.177e-02
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 1.851e-01
## I(Master_degree * Others) 9.230e-02
## I(Master_degree * Black_or_African_American) 1.024e-01
## I(Master_degree * American_Indian_or_Alaska_Native) 3.132e-01
## I(Master_degree * Asian) 4.753e-02
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 3.861e-01
## I(Professional_degree * Others) 1.359e-01
## I(Professional_degree * Black_or_African_American) 1.954e-01
## I(Professional_degree * American_Indian_or_Alaska_Native) 3.145e-01
## I(Professional_degree * Asian) 8.608e-02
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 1.678e-01
## I(Doctoral_degree * Black_or_African_American) 2.251e-01
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 8.712e-02
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) 134.182
## age 25.030
## HSD 7.288
## Some_College 12.868
## Associate_degree 12.460
## Bachelor_degree 27.883
## Master_degree 27.102
## Professional_degree 25.386
## Doctoral_degree 20.581
## Others -6.084
## Black_or_African_American -6.250
## American_Indian_or_Alaska_Native -3.958
## Asian -6.816
## Native_Hawaiian_and_Other_Pacific_Islander -2.121
## I(age * age) -21.364
## I(Bachelor_degree * Others) 0.204
## I(Bachelor_degree * Black_or_African_American) 0.944
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -3.135
## I(Bachelor_degree * Asian) 4.493
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.971
## I(Master_degree * Others) 0.790
## I(Master_degree * Black_or_African_American) -0.732
## I(Master_degree * American_Indian_or_Alaska_Native) 1.293
## I(Master_degree * Asian) 10.560
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) -1.898
## I(Professional_degree * Others) -0.017
## I(Professional_degree * Black_or_African_American) -0.528
## I(Professional_degree * American_Indian_or_Alaska_Native) -2.977
## I(Professional_degree * Asian) 3.656
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) -0.377
## I(Doctoral_degree * Black_or_African_American) 2.275
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 3.645
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) < 2e-16
## age < 2e-16
## HSD 3.34e-13
## Some_College < 2e-16
## Associate_degree < 2e-16
## Bachelor_degree < 2e-16
## Master_degree < 2e-16
## Professional_degree < 2e-16
## Doctoral_degree < 2e-16
## Others 1.20e-09
## Black_or_African_American 4.23e-10
## American_Indian_or_Alaska_Native 7.61e-05
## Asian 9.80e-12
## Native_Hawaiian_and_Other_Pacific_Islander 0.033957
## I(age * age) < 2e-16
## I(Bachelor_degree * Others) 0.838057
## I(Bachelor_degree * Black_or_African_American) 0.345021
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.001722
## I(Bachelor_degree * Asian) 7.07e-06
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.048753
## I(Master_degree * Others) 0.429820
## I(Master_degree * Black_or_African_American) 0.464250
## I(Master_degree * American_Indian_or_Alaska_Native) 0.196002
## I(Master_degree * Asian) < 2e-16
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.057749
## I(Professional_degree * Others) 0.986545
## I(Professional_degree * Black_or_African_American) 0.597366
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.002912
## I(Professional_degree * Asian) 0.000257
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.705973
## I(Doctoral_degree * Black_or_African_American) 0.022914
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 0.000268
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## HSD ***
## Some_College ***
## Associate_degree ***
## Bachelor_degree ***
## Master_degree ***
## Professional_degree ***
## Doctoral_degree ***
## Others ***
## Black_or_African_American ***
## American_Indian_or_Alaska_Native ***
## Asian ***
## Native_Hawaiian_and_Other_Pacific_Islander *
## I(age * age) ***
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American)
## I(Bachelor_degree * American_Indian_or_Alaska_Native) **
## I(Bachelor_degree * Asian) ***
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) *
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native)
## I(Master_degree * Asian) ***
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) .
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American)
## I(Professional_degree * American_Indian_or_Alaska_Native) **
## I(Professional_degree * Asian) ***
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American) *
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian) ***
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5355 on 12931 degrees of freedom
## Multiple R-squared: 0.2971, Adjusted R-squared: 0.2954
## F-statistic: 176.3 on 31 and 12931 DF, p-value: < 2.2e-16BPG test
bptest(earning_log_equation_male)##
## studentized Breusch-Pagan test
##
## data: earning_log_equation_male
## BP = 396.38, df = 31, p-value < 2.2e-16bptestlogequation_male = lm(residuals(earning_log_equation_male)*residuals(earning_log_equation_male) ~ .-earning - female + I(age*age) + I(Bachelor_degree*Others) + I(Bachelor_degree*Black_or_African_American) + I(Bachelor_degree*American_Indian_or_Alaska_Native) + I(Bachelor_degree*Asian) + I(Bachelor_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Master_degree*Others) + I(Master_degree*Black_or_African_American) + I(Master_degree*American_Indian_or_Alaska_Native) + I(Master_degree*Asian) + I(Master_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Professional_degree*Others) + I(Professional_degree*Black_or_African_American) + I(Professional_degree*American_Indian_or_Alaska_Native) + I(Professional_degree*Asian) + I(Professional_degree*Native_Hawaiian_and_Other_Pacific_Islander) + I(Doctoral_degree*Others) + I(Doctoral_degree*Black_or_African_American) + I(Doctoral_degree*American_Indian_or_Alaska_Native) + I(Doctoral_degree*Asian) + I(Doctoral_degree*Native_Hawaiian_and_Other_Pacific_Islander) , data=employedwithpay_male)
summary(bptestlogequation_male)##
## Call:
## lm(formula = residuals(earning_log_equation_male) * residuals(earning_log_equation_male) ~
## . - earning - female + I(age * age) + I(Bachelor_degree *
## Others) + I(Bachelor_degree * Black_or_African_American) +
## I(Bachelor_degree * American_Indian_or_Alaska_Native) +
## I(Bachelor_degree * Asian) + I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Master_degree * Others) + I(Master_degree * Black_or_African_American) +
## I(Master_degree * American_Indian_or_Alaska_Native) +
## I(Master_degree * Asian) + I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Professional_degree * Others) + I(Professional_degree *
## Black_or_African_American) + I(Professional_degree *
## American_Indian_or_Alaska_Native) + I(Professional_degree *
## Asian) + I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) +
## I(Doctoral_degree * Others) + I(Doctoral_degree * Black_or_African_American) +
## I(Doctoral_degree * American_Indian_or_Alaska_Native) +
## I(Doctoral_degree * Asian) + I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander),
## data = employedwithpay_male)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6511 -0.2629 -0.1467 0.0909 6.1491
##
## Coefficients: (3 not defined because of singularities)
## Estimate
## (Intercept) -2.8847202
## age -0.0125440
## HSD -0.0425192
## Some_College -0.0517278
## Associate_degree -0.0934453
## Bachelor_degree -0.0561037
## Master_degree -0.0954280
## Professional_degree 0.0573511
## Doctoral_degree -0.1044557
## Others 0.0397680
## Black_or_African_American 0.0608566
## American_Indian_or_Alaska_Native 0.0706772
## Asian 0.0417793
## Native_Hawaiian_and_Other_Pacific_Islander -0.0341921
## earning_log 0.3121558
## I(age * age) 0.0001406
## I(Bachelor_degree * Others) -0.0655108
## I(Bachelor_degree * Black_or_African_American) -0.1314637
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.0275574
## I(Bachelor_degree * Asian) -0.0752585
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.1345716
## I(Master_degree * Others) -0.0164027
## I(Master_degree * Black_or_African_American) -0.0645720
## I(Master_degree * American_Indian_or_Alaska_Native) -0.4541589
## I(Master_degree * Asian) -0.1732794
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.2225852
## I(Professional_degree * Others) -0.0278622
## I(Professional_degree * Black_or_African_American) -0.2329723
## I(Professional_degree * American_Indian_or_Alaska_Native) -0.0490815
## I(Professional_degree * Asian) -0.2183809
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.1621932
## I(Doctoral_degree * Black_or_African_American) -0.2746548
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) -0.1171461
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Std. Error
## (Intercept) 0.0913658
## age 0.0027519
## HSD 0.0202795
## Some_College 0.0202586
## Associate_degree 0.0225392
## Bachelor_degree 0.0212284
## Master_degree 0.0242575
## Professional_degree 0.0355385
## Doctoral_degree 0.0404154
## Others 0.0198853
## Black_or_African_American 0.0314883
## American_Indian_or_Alaska_Native 0.0417726
## Asian 0.0250502
## Native_Hawaiian_and_Other_Pacific_Islander 0.0654713
## earning_log 0.0077826
## I(age * age) 0.0000311
## I(Bachelor_degree * Others) 0.0455750
## I(Bachelor_degree * Black_or_African_American) 0.0668814
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.1296602
## I(Bachelor_degree * Asian) 0.0369992
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.1637962
## I(Master_degree * Others) 0.0816885
## I(Master_degree * Black_or_African_American) 0.0906586
## I(Master_degree * American_Indian_or_Alaska_Native) 0.2771821
## I(Master_degree * Asian) 0.0422420
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.3417840
## I(Professional_degree * Others) 0.1202515
## I(Professional_degree * Black_or_African_American) 0.1729652
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.2784059
## I(Professional_degree * Asian) 0.0762209
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.1484678
## I(Doctoral_degree * Black_or_African_American) 0.1992154
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 0.0771367
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## t value
## (Intercept) -31.573
## age -4.558
## HSD -2.097
## Some_College -2.553
## Associate_degree -4.146
## Bachelor_degree -2.643
## Master_degree -3.934
## Professional_degree 1.614
## Doctoral_degree -2.585
## Others 2.000
## Black_or_African_American 1.933
## American_Indian_or_Alaska_Native 1.692
## Asian 1.668
## Native_Hawaiian_and_Other_Pacific_Islander -0.522
## earning_log 40.110
## I(age * age) 4.519
## I(Bachelor_degree * Others) -1.437
## I(Bachelor_degree * Black_or_African_American) -1.966
## I(Bachelor_degree * American_Indian_or_Alaska_Native) -0.213
## I(Bachelor_degree * Asian) -2.034
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.822
## I(Master_degree * Others) -0.201
## I(Master_degree * Black_or_African_American) -0.712
## I(Master_degree * American_Indian_or_Alaska_Native) -1.638
## I(Master_degree * Asian) -4.102
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.651
## I(Professional_degree * Others) -0.232
## I(Professional_degree * Black_or_African_American) -1.347
## I(Professional_degree * American_Indian_or_Alaska_Native) -0.176
## I(Professional_degree * Asian) -2.865
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 1.092
## I(Doctoral_degree * Black_or_African_American) -1.379
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) -1.519
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## Pr(>|t|)
## (Intercept) < 2e-16
## age 5.20e-06
## HSD 0.03604
## Some_College 0.01068
## Associate_degree 3.41e-05
## Bachelor_degree 0.00823
## Master_degree 8.40e-05
## Professional_degree 0.10660
## Doctoral_degree 0.00976
## Others 0.04553
## Black_or_African_American 0.05330
## American_Indian_or_Alaska_Native 0.09068
## Asian 0.09537
## Native_Hawaiian_and_Other_Pacific_Islander 0.60151
## earning_log < 2e-16
## I(age * age) 6.27e-06
## I(Bachelor_degree * Others) 0.15062
## I(Bachelor_degree * Black_or_African_American) 0.04936
## I(Bachelor_degree * American_Indian_or_Alaska_Native) 0.83169
## I(Bachelor_degree * Asian) 0.04197
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.41133
## I(Master_degree * Others) 0.84086
## I(Master_degree * Black_or_African_American) 0.47632
## I(Master_degree * American_Indian_or_Alaska_Native) 0.10134
## I(Master_degree * Asian) 4.12e-05
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander) 0.51490
## I(Professional_degree * Others) 0.81678
## I(Professional_degree * Black_or_African_American) 0.17803
## I(Professional_degree * American_Indian_or_Alaska_Native) 0.86007
## I(Professional_degree * Asian) 0.00418
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
## I(Doctoral_degree * Others) 0.27466
## I(Doctoral_degree * Black_or_African_American) 0.16802
## I(Doctoral_degree * American_Indian_or_Alaska_Native) NA
## I(Doctoral_degree * Asian) 0.12887
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander) NA
##
## (Intercept) ***
## age ***
## HSD *
## Some_College *
## Associate_degree ***
## Bachelor_degree **
## Master_degree ***
## Professional_degree
## Doctoral_degree **
## Others *
## Black_or_African_American .
## American_Indian_or_Alaska_Native .
## Asian .
## Native_Hawaiian_and_Other_Pacific_Islander
## earning_log ***
## I(age * age) ***
## I(Bachelor_degree * Others)
## I(Bachelor_degree * Black_or_African_American) *
## I(Bachelor_degree * American_Indian_or_Alaska_Native)
## I(Bachelor_degree * Asian) *
## I(Bachelor_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Master_degree * Others)
## I(Master_degree * Black_or_African_American)
## I(Master_degree * American_Indian_or_Alaska_Native)
## I(Master_degree * Asian) ***
## I(Master_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Professional_degree * Others)
## I(Professional_degree * Black_or_African_American)
## I(Professional_degree * American_Indian_or_Alaska_Native)
## I(Professional_degree * Asian) **
## I(Professional_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## I(Doctoral_degree * Others)
## I(Doctoral_degree * Black_or_African_American)
## I(Doctoral_degree * American_Indian_or_Alaska_Native)
## I(Doctoral_degree * Asian)
## I(Doctoral_degree * Native_Hawaiian_and_Other_Pacific_Islander)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4739 on 12930 degrees of freedom
## Multiple R-squared: 0.1378, Adjusted R-squared: 0.1357
## F-statistic: 64.61 on 32 and 12930 DF, p-value: < 2.2e-16