Kowshik Kumar Bandameedipalli
2024-11-05
Income inequality among different socioeconomic and racial groups was a salient subject in the 20th century in the United States.
Does this inequality still exist in the 21st century? Some economists and social scientists see the relationship between income inequality and the economic growth of people.
Moreover, there is a relationship between income inequality and a variety of social variables such as education, health, and crime.
The importance of income equality is a key link to a healthy society.
Our team decided to address the income inequality issues that existed in today’s world. We hope that our work will help us understand and identify possible reasons for income inequality.
The main aim of our project is to conduct a comprehensive analysis to highlight the key factors that are necessary for improving an individual’s income.
We believe that analysis could help create a policy of fair distribution of income and fight social-economic inequality among the different groups of members of society in order to improve the income levels of individuals.
We are going to check the associativity between Income variable and other factors of the Census Income dataset and draw conclusions whether to accept or to reject the following hypothesis:
For our project, we have been looking for a dataset that would give us the possibility to address income inequality issues in the United States. Also, other requirements of the dataset were dataset that contains categorical variables. Moreover, when we have been planning our project we pictured our work on certain sets of variables such as sex, age, education, and race.
The Census Income dataset was extracted from the 1994 US Census database and publicly available at the University of California Irvine (UCI) Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/Adult This dataset includes information on 48,842 different records, 14 attributes, and 42 nations. The 14 attributes consist of 8 categorical and 6 continuous variables containing information such as age, level of education, employment status of an individual, marital status of an individual, the general type of occupation of an individual, relationship, descriptions of an individual’s race, the biological sex of the individual, capital gains for an individual, capital loss for an individual, the hours an individual has reported working per week, country of origin for an individual, whether or not an individual makes more than $50,000 annually.
The Dataset contains the following information about an individual:
● age: the age of an individual and the value entries are of datatype: Integer greater than 0.
● workclass: a general term to represent the employment status of an individual and the value entries are as follows: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
● fnlwgt: final weight. In other words, this is the number of people the census believes the entry represents and the value entries are of following datatype: Integer greater than 0.
● education: the highest level of education achieved by an individual and the value entries are as follows: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
● education-num: the highest level of education achieved in numerical form and the value entries are of following datatype: Integer greater than 0.
● marital-status: marital status of an individual. Married-civ-spouse corresponds to a civilian spouse while Married-AF-spouse is a spouse in the Armed Forces and the value entries are follows: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
● occupation: the general type of occupation of an individual and the value entries are as follows: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
● relationship: represents what this individual is relative to others. For example, an individual could be a Husband. Each entry only has one relationship attribute and is somewhat redundant with marital status. We might not make use of this attribute at all and the value entries are as follows: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
● race: Descriptions of an individual’s race and the value entries are as follows: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
● sex: the biological sex of the individual and the value entries are either: Male, Female.
● capital-gain: capital gains for an individual and the value entries are of datatype Integer greater than or equal to 0.
● capital-loss: capital loss for an individual and the value entries are of datatype: Integer greater than or equal to 0.
● hours-per-week: the hours an individual has reported to work per week and the value entries are of datatype: continuous.
● native-country: country of origin for an individual and the value entries are as follows: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
● income: whether an individual makes more than $50,000 annually and the value entries are as follows: <=50k, >50k
## age workclass fnlwgt education
## Min. :17.00 Length:32561 Min. : 12285 Length:32561
## 1st Qu.:28.00 Class :character 1st Qu.: 117827 Class :character
## Median :37.00 Mode :character Median : 178356 Mode :character
## Mean :38.58 Mean : 189778
## 3rd Qu.:48.00 3rd Qu.: 237051
## Max. :90.00 Max. :1484705
## education.num marital.status occupation relationship
## Min. : 1.00 Length:32561 Length:32561 Length:32561
## 1st Qu.: 9.00 Class :character Class :character Class :character
## Median :10.00 Mode :character Mode :character Mode :character
## Mean :10.08
## 3rd Qu.:12.00
## Max. :16.00
## race sex capital.gain capital.loss
## Length:32561 Length:32561 Min. : 0 Min. : 0.0
## Class :character Class :character 1st Qu.: 0 1st Qu.: 0.0
## Mode :character Mode :character Median : 0 Median : 0.0
## Mean : 1078 Mean : 87.3
## 3rd Qu.: 0 3rd Qu.: 0.0
## Max. :99999 Max. :4356.0
## hours.per.week native.country income
## Min. : 1.00 Length:32561 Length:32561
## 1st Qu.:40.00 Class :character Class :character
## Median :40.00 Mode :character Mode :character
## Mean :40.44
## 3rd Qu.:45.00
## Max. :99.00There is a statistical relationship between Gender and Income (\(\chi^2\) = 1517.8 and p = 2.2e-16). We reject the null hypothesis as the chi-square is 1517.8 and p-val=32.2e-16 which is much lesser than the level of signifiance i.e 0.05 makes the relationship between gender and Income statistically significant.
We can observe that majority of the people earn less than 50k per annum but few people earn more than 50k per annum in their work. Also, we do not have sufficient data of ages after 50 years.
We can notice that the female count is much higher in count compared to the male in all the age categories.
##
## Call:
## glm(formula = income ~ ., family = binomial, data = train.data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.0815 -0.5107 -0.2127 -0.0383 3.6490
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.990e+00 7.626e-01 -2.609 0.009082
## age 2.522e-02 1.892e-03 13.329 < 2e-16
## workclassLocal-gov -7.618e-01 1.309e-01 -5.819 5.90e-09
## workclassNever-worked -1.197e+01 3.836e+02 -0.031 0.975114
## workclassPrivate -5.334e-01 1.091e-01 -4.888 1.02e-06
## workclassSelf-emp-inc -3.028e-01 1.454e-01 -2.083 0.037235
## workclassSelf-emp-not-inc -1.075e+00 1.289e-01 -8.337 < 2e-16
## workclassState-gov -8.000e-01 1.436e-01 -5.570 2.54e-08
## workclassUnknown -1.100e+00 1.627e-01 -6.760 1.38e-11
## workclassWithout-pay -1.354e+01 2.786e+02 -0.049 0.961240
## education.num 2.687e-01 1.079e-02 24.901 < 2e-16
## marital.statusMarried-AF-spouse 3.168e+00 6.140e-01 5.159 2.48e-07
## marital.statusMarried-civ-spouse 2.059e+00 7.845e-02 26.251 < 2e-16
## marital.statusMarried-spouse-absent -1.380e-01 2.840e-01 -0.486 0.627077
## marital.statusNever-married -5.552e-01 9.708e-02 -5.720 1.07e-08
## marital.statusSeparated -1.048e-01 1.814e-01 -0.578 0.563324
## marital.statusWidowed -1.426e-01 1.780e-01 -0.801 0.423115
## occupationArmed-Forces -7.830e-01 1.755e+00 -0.446 0.655516
## occupationCraft-repair 1.192e-02 9.181e-02 0.130 0.896717
## occupationExec-managerial 7.500e-01 8.822e-02 8.502 < 2e-16
## occupationFarming-fishing -1.102e+00 1.670e-01 -6.603 4.04e-11
## occupationHandlers-cleaners -7.725e-01 1.664e-01 -4.642 3.44e-06
## occupationMachine-op-inspct -3.510e-01 1.199e-01 -2.929 0.003403
## occupationOther-service -8.982e-01 1.387e-01 -6.474 9.54e-11
## occupationPriv-house-serv -3.895e+00 2.041e+00 -1.908 0.056376
## occupationProf-specialty 5.657e-01 9.133e-02 6.194 5.85e-10
## occupationProtective-serv 5.085e-01 1.482e-01 3.432 0.000600
## occupationSales 2.349e-01 9.510e-02 2.470 0.013509
## occupationTech-support 5.606e-01 1.299e-01 4.317 1.58e-05
## occupationTransport-moving -8.893e-02 1.144e-01 -0.777 0.437026
## occupationUnknown NA NA NA NA
## raceAsian-Pac-Islander 5.477e-01 3.001e-01 1.825 0.068013
## raceBlack 5.011e-01 2.538e-01 1.974 0.048349
## raceOther 3.462e-01 4.003e-01 0.865 0.387035
## raceWhite 5.445e-01 2.405e-01 2.264 0.023587
## sexMale 2.227e-01 6.165e-02 3.613 0.000303
## capital.gain 3.250e-04 1.221e-05 26.611 < 2e-16
## capital.loss 6.729e-04 4.328e-05 15.546 < 2e-16
## hours.per.week 2.914e-02 1.909e-03 15.264 < 2e-16
## native.countryCanada -4.432e-01 7.750e-01 -0.572 0.567419
## native.countryChina -1.573e+00 8.000e-01 -1.966 0.049311
## native.countryColumbia -2.782e+00 1.097e+00 -2.536 0.011226
## native.countryCuba -4.973e-01 8.030e-01 -0.619 0.535762
## native.countryDominican-Republic -1.866e+00 1.288e+00 -1.449 0.147411
## native.countryEcuador -1.514e+00 1.143e+00 -1.324 0.185348
## native.countryEl-Salvador -1.156e+00 9.005e-01 -1.284 0.199179
## native.countryEngland -3.934e-01 8.070e-01 -0.487 0.625974
## native.countryFrance -4.064e-01 9.206e-01 -0.441 0.658918
## native.countryGermany -8.526e-01 7.824e-01 -1.090 0.275833
## native.countryGreece -2.330e+00 1.024e+00 -2.275 0.022877
## native.countryGuatemala -6.016e-01 1.057e+00 -0.569 0.569360
## native.countryHaiti -1.111e+00 1.041e+00 -1.067 0.285910
## native.countryHoland-Netherlands -1.256e+01 8.827e+02 -0.014 0.988652
## native.countryHonduras -1.613e+00 2.248e+00 -0.717 0.473157
## native.countryHong -1.093e+00 1.087e+00 -1.006 0.314404
## native.countryHungary -3.752e-01 1.144e+00 -0.328 0.742928
## native.countryIndia -1.054e+00 7.659e-01 -1.376 0.168709
## native.countryIran -8.158e-01 8.478e-01 -0.962 0.335931
## native.countryIreland -7.089e-01 1.016e+00 -0.697 0.485528
## native.countryItaly -1.346e-01 8.214e-01 -0.164 0.869816
## native.countryJamaica -8.965e-01 8.790e-01 -1.020 0.307793
## native.countryJapan -7.223e-01 8.502e-01 -0.850 0.395598
## native.countryLaos -1.112e+00 1.100e+00 -1.011 0.311973
## native.countryMexico -1.002e+00 7.495e-01 -1.337 0.181246
## native.countryNicaragua -1.076e+00 1.065e+00 -1.010 0.312392
## native.countryOutlying-US(Guam-USVI-etc) -1.284e+01 2.450e+02 -0.052 0.958196
## native.countryPeru -1.271e+01 1.717e+02 -0.074 0.941010
## native.countryPhilippines -3.797e-01 7.342e-01 -0.517 0.605107
## native.countryPoland -1.152e+00 8.701e-01 -1.324 0.185505
## native.countryPortugal -1.109e+00 1.016e+00 -1.092 0.274885
## native.countryPuerto-Rico -1.632e+00 8.776e-01 -1.860 0.062859
## native.countryScotland -8.556e-01 1.176e+00 -0.728 0.466909
## native.countrySouth -1.824e+00 8.243e-01 -2.212 0.026940
## native.countryTaiwan -5.610e-01 8.479e-01 -0.662 0.508235
## native.countryThailand -8.708e-01 1.100e+00 -0.792 0.428488
## native.countryTrinadad&Tobago -1.303e+01 2.055e+02 -0.063 0.949444
## native.countryUnited-States -7.121e-01 7.129e-01 -0.999 0.317872
## native.countryUnknown -1.015e+00 7.239e-01 -1.403 0.160722
## native.countryVietnam -1.696e+00 9.850e-01 -1.722 0.085094
## native.countryYugoslavia -9.522e-01 1.054e+00 -0.904 0.366238
##
## (Intercept) **
## age ***
## workclassLocal-gov ***
## workclassNever-worked
## workclassPrivate ***
## workclassSelf-emp-inc *
## workclassSelf-emp-not-inc ***
## workclassState-gov ***
## workclassUnknown ***
## workclassWithout-pay
## education.num ***
## marital.statusMarried-AF-spouse ***
## marital.statusMarried-civ-spouse ***
## marital.statusMarried-spouse-absent
## marital.statusNever-married ***
## marital.statusSeparated
## marital.statusWidowed
## occupationArmed-Forces
## occupationCraft-repair
## occupationExec-managerial ***
## occupationFarming-fishing ***
## occupationHandlers-cleaners ***
## occupationMachine-op-inspct **
## occupationOther-service ***
## occupationPriv-house-serv .
## occupationProf-specialty ***
## occupationProtective-serv ***
## occupationSales *
## occupationTech-support ***
## occupationTransport-moving
## occupationUnknown
## raceAsian-Pac-Islander .
## raceBlack *
## raceOther
## raceWhite *
## sexMale ***
## capital.gain ***
## capital.loss ***
## hours.per.week ***
## native.countryCanada
## native.countryChina *
## native.countryColumbia *
## native.countryCuba
## native.countryDominican-Republic
## native.countryEcuador
## native.countryEl-Salvador
## native.countryEngland
## native.countryFrance
## native.countryGermany
## native.countryGreece *
## native.countryGuatemala
## native.countryHaiti
## native.countryHoland-Netherlands
## native.countryHonduras
## native.countryHong
## native.countryHungary
## native.countryIndia
## native.countryIran
## native.countryIreland
## native.countryItaly
## native.countryJamaica
## native.countryJapan
## native.countryLaos
## native.countryMexico
## native.countryNicaragua
## native.countryOutlying-US(Guam-USVI-etc)
## native.countryPeru
## native.countryPhilippines
## native.countryPoland
## native.countryPortugal
## native.countryPuerto-Rico .
## native.countryScotland
## native.countrySouth *
## native.countryTaiwan
## native.countryThailand
## native.countryTrinadad&Tobago
## native.countryUnited-States
## native.countryUnknown
## native.countryVietnam .
## native.countryYugoslavia
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25262 on 22791 degrees of freedom
## Residual deviance: 14897 on 22713 degrees of freedom
## AIC: 15055
##
## Number of Fisher Scoring iterations: 13## OR 2.5 %
## (Intercept) 1.367406e-01 2.853895e-02
## age 1.025545e+00 1.021752e+00
## workclassLocal-gov 4.668307e-01 3.611614e-01
## workclassNever-worked 6.344126e-06 NA
## workclassPrivate 5.866221e-01 4.738521e-01
## workclassSelf-emp-inc 7.387369e-01 5.557221e-01
## workclassSelf-emp-not-inc 3.414259e-01 2.651751e-01
## workclassState-gov 4.493353e-01 3.389494e-01
## workclassUnknown 3.329884e-01 2.417195e-01
## workclassWithout-pay 1.317214e-06 NA
## education.num 1.308278e+00 1.281015e+00
## marital.statusMarried-AF-spouse 2.374888e+01 6.840113e+00
## marital.statusMarried-civ-spouse 7.841557e+00 6.734630e+00
## marital.statusMarried-spouse-absent 8.711213e-01 4.836839e-01
## marital.statusNever-married 5.739313e-01 4.745103e-01
## marital.statusSeparated 9.004825e-01 6.242970e-01
## marital.statusWidowed 8.671250e-01 6.063770e-01
## occupationArmed-Forces 4.570181e-01 1.160958e-02
## occupationCraft-repair 1.011989e+00 8.457428e-01
## occupationExec-managerial 2.117098e+00 1.782083e+00
## occupationFarming-fishing 3.320953e-01 2.381227e-01
## occupationHandlers-cleaners 4.618502e-01 3.309290e-01
## occupationMachine-op-inspct 7.039683e-01 5.558316e-01
## occupationOther-service 4.073155e-01 3.090038e-01
## occupationPriv-house-serv 2.034675e-02 3.681913e-04
## occupationProf-specialty 1.760729e+00 1.472976e+00
## occupationProtective-serv 1.662759e+00 1.242338e+00
## occupationSales 1.264779e+00 1.050094e+00
## occupationTech-support 1.751807e+00 1.357146e+00
## occupationTransport-moving 9.149067e-01 7.307158e-01
## occupationUnknown NA NA
## raceAsian-Pac-Islander 1.729284e+00 9.682475e-01
## raceBlack 1.650585e+00 1.017336e+00
## raceOther 1.413734e+00 6.348468e-01
## raceWhite 1.723673e+00 1.092341e+00
## sexMale 1.249463e+00 1.107393e+00
## capital.gain 1.000325e+00 1.000301e+00
## capital.loss 1.000673e+00 1.000588e+00
## hours.per.week 1.029566e+00 1.025731e+00
## native.countryCanada 6.420016e-01 1.447857e-01
## native.countryChina 2.074629e-01 4.426529e-02
## native.countryColumbia 6.189392e-02 5.986529e-03
## native.countryCuba 6.081989e-01 1.290570e-01
## native.countryDominican-Republic 1.546794e-01 6.483276e-03
## native.countryEcuador 2.200609e-01 1.985244e-02
## native.countryEl-Salvador 3.147131e-01 5.368457e-02
## native.countryEngland 6.747904e-01 1.424913e-01
## native.countryFrance 6.660633e-01 1.123244e-01
## native.countryGermany 4.263183e-01 9.454178e-02
## native.countryGreece 9.729443e-02 1.222538e-02
## native.countryGuatemala 5.479543e-01 5.949593e-02
## native.countryHaiti 3.291161e-01 3.890042e-02
## native.countryHoland-Netherlands 3.525530e-06 NA
## native.countryHonduras 1.993603e-01 2.776203e-03
## native.countryHong 3.351117e-01 3.758284e-02
## native.countryHungary 6.871330e-01 6.218197e-02
## native.countryIndia 3.484999e-01 8.006143e-02
## native.countryIran 4.422732e-01 8.597802e-02
## native.countryIreland 4.921918e-01 6.502372e-02
## native.countryItaly 8.740421e-01 1.789071e-01
## native.countryJamaica 4.080095e-01 7.273915e-02
## native.countryJapan 4.856515e-01 9.371229e-02
## native.countryLaos 3.288241e-01 3.173637e-02
## native.countryMexico 3.671494e-01 8.719096e-02
## native.countryNicaragua 3.409710e-01 3.488241e-02
## native.countryOutlying-US(Guam-USVI-etc) 2.650902e-06 1.762732e-133
## native.countryPeru 3.026268e-06 4.825607e-97
## native.countryPhilippines 6.840964e-01 1.675847e-01
## native.countryPoland 3.159931e-01 5.776183e-02
## native.countryPortugal 3.298215e-01 4.056406e-02
## native.countryPuerto-Rico 1.954418e-01 3.467145e-02
## native.countryScotland 4.250488e-01 3.609659e-02
## native.countrySouth 1.614493e-01 3.256424e-02
## native.countryTaiwan 5.706443e-01 1.109506e-01
## native.countryThailand 4.186335e-01 4.597848e-02
## native.countryTrinadad&Tobago 2.196406e-06 7.004910e-115
## native.countryUnited-States 4.906257e-01 1.257936e-01
## native.countryUnknown 3.622792e-01 9.077970e-02
## native.countryVietnam 1.833935e-01 2.427707e-02
## native.countryYugoslavia 3.858983e-01 4.870608e-02
## 97.5 %
## (Intercept) 5.893217e-01
## age 1.029360e+00
## workclassLocal-gov 6.033814e-01
## workclassNever-worked 7.962242e+13
## workclassPrivate 7.268596e-01
## workclassSelf-emp-inc 9.825676e-01
## workclassSelf-emp-not-inc 4.395556e-01
## workclassState-gov 5.952095e-01
## workclassUnknown 4.574429e-01
## workclassWithout-pay 2.325929e+04
## education.num 1.336369e+00
## marital.statusMarried-AF-spouse 7.859075e+01
## marital.statusMarried-civ-spouse 9.160173e+00
## marital.statusMarried-spouse-absent 1.479990e+00
## marital.statusNever-married 6.943312e-01
## marital.statusSeparated 1.272744e+00
## marital.statusWidowed 1.219468e+00
## occupationArmed-Forces 9.551905e+00
## occupationCraft-repair 1.212170e+00
## occupationExec-managerial 2.518529e+00
## occupationFarming-fishing 4.584196e-01
## occupationHandlers-cleaners 6.359025e-01
## occupationMachine-op-inspct 8.893158e-01
## occupationOther-service 5.325241e-01
## occupationPriv-house-serv 3.639838e-01
## occupationProf-specialty 2.107185e+00
## occupationProtective-serv 2.221157e+00
## occupationSales 1.524580e+00
## occupationTech-support 2.258239e+00
## occupationTransport-moving 1.144444e+00
## occupationUnknown NA
## raceAsian-Pac-Islander 3.146761e+00
## raceBlack 2.758622e+00
## raceOther 3.066079e+00
## raceWhite 2.811759e+00
## sexMale 1.410146e+00
## capital.gain 1.000349e+00
## capital.loss 1.000758e+00
## hours.per.week 1.033436e+00
## native.countryCanada 3.135245e+00
## native.countryChina 1.056712e+00
## native.countryColumbia 4.970414e-01
## native.countryCuba 3.113431e+00
## native.countryDominican-Republic 1.515233e+00
## native.countryEcuador 1.950078e+00
## native.countryEl-Salvador 1.895709e+00
## native.countryEngland 3.487006e+00
## native.countryFrance 4.262255e+00
## native.countryGermany 2.106841e+00
## native.countryGreece 7.185053e-01
## native.countryGuatemala 4.124087e+00
## native.countryHaiti 2.463399e+00
## native.countryHoland-Netherlands 3.247391e+72
## native.countryHonduras 6.562503e+00
## native.countryHong 2.794348e+00
## native.countryHungary 6.228152e+00
## native.countryIndia 1.675760e+00
## native.countryIran 2.462427e+00
## native.countryIreland 3.624906e+00
## native.countryItaly 4.628013e+00
## native.countryJamaica 2.369165e+00
## native.countryJapan 2.708583e+00
## native.countryLaos 2.667862e+00
## native.countryMexico 1.714038e+00
## native.countryNicaragua 2.586828e+00
## native.countryOutlying-US(Guam-USVI-etc) 2.496241e-135
## native.countryPeru 1.115578e-93
## native.countryPhilippines 3.111523e+00
## native.countryPoland 1.812451e+00
## native.countryPortugal 2.340398e+00
## native.countryPuerto-Rico 1.124610e+00
## native.countryScotland 4.013712e+00
## native.countrySouth 8.547176e-01
## native.countryTaiwan 3.176536e+00
## native.countryThailand 3.595393e+00
## native.countryTrinadad&Tobago 2.864347e-111
## native.countryUnited-States 2.152964e+00
## native.countryUnknown 1.619625e+00
## native.countryVietnam 1.235619e+00
## native.countryYugoslavia 3.150734e+00## Call:
## multinom(formula = income ~ ., data = train.data)
##
## Coefficients:
## Values Std. Err.
## (Intercept) -1.990476e+00 3.741062e-02
## age 2.522585e-02 1.805548e-03
## workclassLocal-gov -7.617887e-01 6.728674e-02
## workclassNever-worked -6.421000e+00 7.363868e-09
## workclassPrivate -5.333739e-01 3.751599e-02
## workclassSelf-emp-inc -3.028081e-01 4.267138e-02
## workclassSelf-emp-not-inc -1.074634e+00 6.363730e-02
## workclassState-gov -7.999778e-01 5.761283e-02
## workclassUnknown 2.660664e+00 5.210813e-02
## workclassWithout-pay -1.181635e+01 7.598040e-09
## education.num 2.687148e-01 1.048927e-02
## marital.statusMarried-AF-spouse 3.167562e+00 3.479094e-04
## marital.statusMarried-civ-spouse 2.059470e+00 4.357508e-02
## marital.statusMarried-spouse-absent -1.380063e-01 9.866204e-04
## marital.statusNever-married -5.551954e-01 4.914600e-02
## marital.statusSeparated -1.049461e-01 5.169879e-03
## marital.statusWidowed -1.426210e-01 3.732953e-03
## occupationArmed-Forces -7.830792e-01 4.742869e-05
## occupationCraft-repair 1.191417e-02 5.375753e-02
## occupationExec-managerial 7.500436e-01 5.203115e-02
## occupationFarming-fishing -1.102319e+00 1.186218e-02
## occupationHandlers-cleaners -7.725222e-01 4.502546e-03
## occupationMachine-op-inspct -3.510209e-01 6.087704e-02
## occupationOther-service -8.982939e-01 1.617389e-02
## occupationPriv-house-serv -3.896168e+00 5.439959e-05
## occupationProf-specialty 5.657288e-01 6.028698e-02
## occupationProtective-serv 5.084555e-01 2.176999e-02
## occupationSales 2.348879e-01 5.756905e-02
## occupationTech-support 5.606398e-01 2.235963e-02
## occupationTransport-moving -8.894320e-02 7.457912e-02
## occupationUnknown -3.760336e+00 5.210813e-02
## raceAsian-Pac-Islander 5.477177e-01 3.595428e-02
## raceBlack 5.010875e-01 4.147754e-02
## raceOther 3.462153e-01 1.885757e-03
## raceWhite 5.444188e-01 4.111959e-02
## sexMale 2.227212e-01 5.848648e-02
## capital.gain 3.249619e-04 1.215115e-05
## capital.loss 6.728625e-04 4.317885e-05
## hours.per.week 2.913827e-02 1.870142e-03
## native.countryCanada -4.423738e-01 3.889464e-03
## native.countryChina -1.572122e+00 3.197899e-03
## native.countryColumbia -2.781719e+00 3.927319e-04
## native.countryCuba -4.964297e-01 2.568421e-03
## native.countryDominican-Republic -1.865777e+00 2.785644e-04
## native.countryEcuador -1.513036e+00 3.438398e-04
## native.countryEl-Salvador -1.155280e+00 9.987341e-04
## native.countryEngland -3.925799e-01 2.238580e-03
## native.countryFrance -4.056664e-01 9.710779e-04
## native.countryGermany -8.517582e-01 2.921975e-03
## native.countryGreece -2.329389e+00 6.312120e-04
## native.countryGuatemala -6.012669e-01 5.241249e-04
## native.countryHaiti -1.109896e+00 5.677965e-04
## native.countryHoland-Netherlands -9.796950e+00 5.615090e-09
## native.countryHonduras -1.615334e+00 8.183764e-05
## native.countryHong -1.092480e+00 7.092566e-04
## native.countryHungary -3.743229e-01 4.430977e-04
## native.countryIndia -1.053409e+00 4.569756e-03
## native.countryIran -8.149847e-01 1.425976e-03
## native.countryIreland -7.080493e-01 5.786095e-04
## native.countryItaly -1.338101e-01 2.171259e-03
## native.countryJamaica -8.956051e-01 1.124143e-03
## native.countryJapan -7.215826e-01 1.698888e-03
## native.countryLaos -1.111432e+00 7.638878e-04
## native.countryMexico -1.001256e+00 6.777171e-03
## native.countryNicaragua -1.075292e+00 5.709797e-04
## native.countryOutlying-US(Guam-USVI-etc) -1.332311e+01 1.058227e-09
## native.countryPeru -1.413142e+01 7.050063e-10
## native.countryPhilippines -3.789230e-01 9.298607e-03
## native.countryPoland -1.151202e+00 1.313456e-03
## native.countryPortugal -1.108167e+00 6.191172e-04
## native.countryPuerto-Rico -1.631699e+00 1.131000e-03
## native.countryScotland -8.546934e-01 3.517610e-04
## native.countrySouth -1.822858e+00 2.737159e-03
## native.countryTaiwan -5.601379e-01 2.237899e-03
## native.countryThailand -8.699278e-01 7.699713e-04
## native.countryTrinadad&Tobago -1.395656e+01 1.433109e-09
## native.countryUnited-States -7.112772e-01 5.225595e-02
## native.countryUnknown -1.014583e+00 1.729463e-02
## native.countryVietnam -1.695456e+00 1.071346e-03
## native.countryYugoslavia -9.513535e-01 5.182433e-04
##
## Residual Deviance: 14897.15
## AIC: 15055.15The following are the Logistic regression performance measures:
## [1] 85.86345## [1] 14.13655The following are the multinomial Logistic regression performance measures:
## [1] 85.86345## [1] 14.13655