DEM 7283 - Principal Components Analysis (PCA) Assignment
Paulina Cano
March 5, 2018
All the variables in this dataset were compiled from a sample of the National Survey of College Graduates through the IPUMS-HigherEd site.
For this assignment, we requested a new extract from CPS-IPUMS to include variables related to job satisfaction including: access to health insurance, pension benefits, paid vacation, membership to professional organizations and whether the job is related to the highest degree acquired.
The data used for this analysis consists of a sample from the National Survey of College Graduates gathered through IPUMS-Higher Ed.
library(car)
library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.1. https://CRAN.R-project.org/package=stargazerlibrary(survey)## Loading required package: grid## Loading required package: Matrix## Loading required package: survival##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartlibrary(ggplot2)
library(pander)
library(knitr)
library(haven)cpsipums4<-read_dta("~/Statistics II/Data for project/highered/highered_00009.dta")
names(cpsipums4) #print the column names## [1] "personid" "year" "weight" "sample" "surid" "gender"
## [7] "minrty" "raceth" "bthus" "chtot" "dgrdg" "lfstat"
## [13] "hrswkgr" "jobins" "jobpens" "jobvac" "ocedrlp" "prmbr"
## [19] "salary"Recoding of Variables
#income grouping
cpsipums4$salary4<-ifelse(cpsipums4$salary==9999998:9999999, NA, cpsipums4$salary)#number of children living in the household
cpsipums4$chtot<-recode(cpsipums4$chtot, recodes="00='no children'; 01='one child'; 02='one to three children'; 03='two or more children'; 04='more than 3 children'; 98=NA", as.factor.result=T)
cpsipums4$chtot<-relevel(cpsipums4$chtot, ref = "one child")
table(cpsipums4$chtot)##
## one child two or more children
## 14472 20517#gender
cpsipums4$female<-recode(cpsipums4$gender, recodes=1)
cpsipums4$male<-recode(cpsipums4$gender, recodes=2)
cpsipums4$gender<-recode(cpsipums4$gender, recodes="1='female'; 2='male'", as.factor.result=T)
table(cpsipums4$gender)##
## female male
## 38626 45830cpsipums4$gender<-relevel(cpsipums4$gender, ref = "female")#race/ethnicity
#There are no entries in this data set under "other"
cpsipums4$asian<-recode(cpsipums4$raceth, recodes=1)
cpsipums4$white<-recode(cpsipums4$raceth, recodes=2)
cpsipums4$minorities<-recode(cpsipums4$raceth, recodes=3)
cpsipums4$other<-recode(cpsipums4$raceth, recodes=4)
cpsipums4$raceth<-recode(cpsipums4$raceth, recodes="1='asian'; 2='white'; 3='minorities'", as.factor.result=T)
cpsipums4$raceth<-relevel(cpsipums4$raceth, ref = "white")
table(cpsipums4$raceth)##
## white asian minorities
## 51846 13868 18742 #education level
cpsipums4$dgrdg<-recode(cpsipums4$dgrdg, recodes="1='0bachelors'; 2='1masters'; 3='2doctorate'; 4='3professional'", as.factor.result=T)
table(cpsipums4$dgrdg, cpsipums4$gender)##
## female male
## 0bachelors 18930 25269
## 1masters 16756 16585
## 2doctorate 1103 1611
## 3professional 1837 2365Recoding of Variables to Build Job Satisfaction Index
#Access to Pension (1=yes, 0=no)
cpsipums4$pension<-recode(cpsipums4$jobpens, recodes = "1=1; 0=0; 98=NA")
table(cpsipums4$pension)##
## 0 1
## 19749 51625#participation in professional organizations
cpsipums4$organizations<-recode(cpsipums4$prmbr, recodes = "0000=NA; 96=NA; 99=NA; 98=NA")
table(cpsipums4$organizations)##
## 1 2 3 4 5 6
## 18166 11588 4874 1828 879 620#Access to vacation (1=yes, 0=no)
cpsipums4$vacation<-recode(cpsipums4$jobvac, recodes = "1=1; 0=0; 98=NA")
table(cpsipums4$vacation)##
## 0 1
## 12204 59170#Access to Health Insurance (1=yes, 0=no)
cpsipums4$insurance<-recode(cpsipums4$jobins, recodes = "1=1; 0=0; 98=NA")
table(cpsipums4$insurance)##
## 0 1
## 11497 59877#Job relationship to highest degree (1=it is related, 0=not related)
cpsipums4$related<-recode(cpsipums4$ocedrlp, recodes = "1:2=1; 3=0; 98=NA")
table(cpsipums4$related)##
## 0 1
## 9971 61403Analysis
We will use the prcomp function to do the pca by specifying our variables we want in our index using a formula, the name of the data, we also specify R to z-score the data (center=T removes the mean, scale=T divides by the standard deviation for each variable), and the retx=T will calculate the PC scores for each individual for each component.
Our Scree plot shows an eigenvalue higher than 1. PC1 seems to be the most fitting summary measure explaining 43% of all variation however, membership to professional organizations may not be an effective variable in this index.
Further review of PC1 shows that three components account for at least 50% of the variation.
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncpsi<- cpsipums4 %>%
select(related, organizations, pension, vacation, insurance)%>%
filter(complete.cases(.))
apply(cpsi, MARGIN = 2, var)## related organizations pension vacation insurance
## 0.07612482 1.25082159 0.20024455 0.14648699 0.12913184cpsipums4.pc<-prcomp(~related+pension+vacation+insurance+organizations,data=cpsipums4, center=T, scale=T, retx=T)
#Screeplot
screeplot(cpsipums4.pc, type = "l", main = "Scree Plot")
abline(h=1)
#Request some basic summaries of the PCA analysis option(digits=3)
summary(cpsipums4.pc)## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 1.477 1.0215 0.9621 0.7127 0.58543
## Proportion of Variance 0.436 0.2087 0.1851 0.1016 0.06855
## Cumulative Proportion 0.436 0.6448 0.8299 0.9314 1.00000cpsipums4.pc$rotation## PC1 PC2 PC3 PC4 PC5
## related 0.14994534 -0.63529206 0.75570251 -0.04057262 -0.03446709
## pension 0.53849708 0.02561616 -0.12864179 -0.83184327 0.02920245
## vacation 0.58499510 0.06078000 -0.07791547 0.36747642 -0.71622398
## insurance 0.58371895 -0.01065113 -0.07088716 0.41292102 0.69543510
## organizations -0.06775113 -0.76937648 -0.63345813 0.02911965 -0.03677593#then I plot the first 2 components
hist(cpsipums4.pc$x[,1])
hist(cpsipums4.pc$x[,2])
Next, we calculate the correlation between the first 2 components to show they are orthogonal, i.e. correlation == 0
cor(cpsipums4.pc$x[,1:2])## PC1 PC2
## PC1 1.000000e+00 -7.943575e-15
## PC2 -7.943575e-15 1.000000e+00scores<-data.frame(cpsipums4.pc$x)
scores$name<-rownames(cpsipums4.pc$x)
cpsipums4$name<-rownames(cpsipums4)
cpsipums4<-merge(cpsipums4, scores, by.x="name", by.y="name", all.x=F)
tail(names(cpsipums4), 20)## [1] "ocedrlp" "prmbr" "salary" "salary4"
## [5] "female" "male" "asian" "white"
## [9] "minorities" "other" "pension" "organizations"
## [13] "vacation" "insurance" "related" "PC1"
## [17] "PC2" "PC3" "PC4" "PC5"Here we examine correlation among our job variables
round(cor(cpsipums4[,c("related","pension","vacation","insurance","organizations")], method = "spearman"), 3)## related pension vacation insurance organizations
## related 1.000 0.086 0.097 0.132 0.051
## pension 0.086 1.000 0.535 0.526 -0.039
## vacation 0.097 0.535 1.000 0.655 -0.076
## insurance 0.132 0.526 0.655 1.000 -0.039
## organizations 0.051 -0.039 -0.076 -0.039 1.000The correlations among the original variables and the components under PC1 show that pension, vacation and insurance have a positive relationship to the job satisfaction index followed by the job’s relationship to the highest degree earned. The component of membership to professional organizations has a slight negative relationship.
round(cor(cpsipums4[,c("related","pension","vacation","insurance","organizations", "PC1", "PC2")], method = "spearman"),3)## related pension vacation insurance organizations PC1
## related 1.000 0.086 0.097 0.132 0.051 0.291
## pension 0.086 1.000 0.535 0.526 -0.039 0.741
## vacation 0.097 0.535 1.000 0.655 -0.076 0.660
## insurance 0.132 0.526 0.655 1.000 -0.039 0.626
## organizations 0.051 -0.039 -0.076 -0.039 1.000 -0.432
## PC1 0.291 0.741 0.660 0.626 -0.432 1.000
## PC2 -0.469 0.198 0.189 0.081 -0.841 0.444
## PC2
## related -0.469
## pension 0.198
## vacation 0.189
## insurance 0.081
## organizations -0.841
## PC1 0.444
## PC2 1.000Using the Principal Components
Next, we use the PC’s we just generated in an analysis
#Survey design object
options(survey.lonely.psu = "adjust")
cpsipums4$pc1q<-cpsipums4$PC1
des<-svydesign(ids=~1, strata=~sample, weights=~weight, data=cpsipums4)
#means of the variables in the index
svyby( ~related+pension+vacation+insurance+organizations,~pc1q, des,FUN=svymean , na.rm=T)## pc1q related pension vacation insurance
## -4.25093822316251 -4.2509382 0 0 0 0
## -4.19035967655366 -4.1903597 0 0 0 0
## -4.1297811299448 -4.1297811 0 0 0 0
## -4.06920258333594 -4.0692026 0 0 0 0
## -4.00862403672709 -4.0086240 0 0 0 0
## -3.94804549011823 -3.9480455 0 0 0 0
## -3.7074754155757 -3.7074754 1 0 0 0
## -3.64689686896684 -3.6468969 1 0 0 0
## -3.58631832235798 -3.5863183 1 0 0 0
## -3.52573977574913 -3.5257398 1 0 0 0
## -3.46516122914027 -3.4651612 1 0 0 0
## -3.40458268253142 -3.4045827 1 0 0 0
## -3.04755765999889 -3.0475577 0 1 0 0
## -2.92640056678118 -2.9264006 0 1 0 0
## -2.86582202017232 -2.8658220 0 1 0 0
## -2.80524347356346 -2.8052435 0 1 0 0
## -2.74466492695461 -2.7446649 0 1 0 0
## -2.72248307515592 -2.7224831 0 0 1 0
## -2.6013259819382 -2.6013260 0 0 1 0
## -2.56598156364506 -2.5659816 0 0 0 1
## -2.54074743532935 -2.5407474 0 0 1 0
## -2.5054030170362 -2.5054030 0 0 0 1
## -2.50409485241207 -2.5040949 1 1 0 0
## -2.48016888872049 -2.4801689 0 0 1 0
## -2.44482447042734 -2.4448245 0 0 0 1
## -2.44351630580322 -2.4435163 1 1 0 0
## -2.41959034211163 -2.4195903 0 0 1 0
## -2.38424592381849 -2.3842459 0 0 0 1
## -2.38293775919436 -2.3829378 1 1 0 0
## -2.32366737720963 -2.3236674 0 0 0 1
## -2.32235921258551 -2.3223592 1 1 0 0
## -2.26178066597665 -2.2617807 1 1 0 0
## -2.20120211936779 -2.2012021 1 1 0 0
## -2.1790202675691 -2.1790203 1 0 1 0
## -2.11844172096025 -2.1184417 1 0 1 0
## -2.0830973026671 -2.0830973 1 0 0 1
## -2.05786317435139 -2.0578632 1 0 1 0
## -2.02251875605824 -2.0225188 1 0 0 1
## -1.99728462774253 -1.9972846 1 0 1 0
## -1.96194020944939 -1.9619402 1 0 0 1
## -1.93670608113368 -1.9367061 1 0 1 0
## -1.90136166284053 -1.9013617 1 0 0 1
## -1.87612753452482 -1.8761275 1 0 1 0
## -1.84078311623167 -1.8407831 1 0 0 1
## -1.78020456962282 -1.7802046 1 0 0 1
## -1.45852396538344 -1.4585240 0 1 1 0
## -1.42317954709029 -1.4231795 0 1 0 1
## -1.39794541877458 -1.3979454 0 1 1 0
## -1.33736687216572 -1.3373669 0 1 1 0
## -1.30202245387258 -1.3020225 0 1 0 1
## -1.27678832555687 -1.2767883 0 1 1 0
## -1.24144390726372 -1.2414439 0 1 0 1
## -1.21620977894801 -1.2162098 0 1 1 0
## -1.18086536065486 -1.1808654 0 1 0 1
## -1.12028681404601 -1.1202868 0 1 0 1
## -1.09810496224732 -1.0981050 0 0 1 1
## -1.03752641563846 -1.0375264 0 0 1 1
## -0.976947869029604 -0.9769479 0 0 1 1
## -0.975639704405479 -0.9756397 1 1 1 0
## -0.916369322420748 -0.9163693 0 0 1 1
## -0.915061157796623 -0.9150612 1 1 1 0
## -0.879716739503475 -0.8797167 1 1 0 1
## -0.855790775811892 -0.8557908 0 0 1 1
## -0.854482611187766 -0.8544826 1 1 1 0
## -0.819138192894619 -0.8191382 1 1 0 1
## -0.795212229203035 -0.7952122 0 0 1 1
## -0.79390406457891 -0.7939041 1 1 1 0
## -0.758559646285763 -0.7585596 1 1 0 1
## -0.733325517970054 -0.7333255 1 1 1 0
## -0.697981099676907 -0.6979811 1 1 0 1
## -0.672746971361198 -0.6727470 1 1 1 0
## -0.63740255306805 -0.6374026 1 1 0 1
## -0.576824006459194 -0.5768240 1 1 0 1
## -0.554642154660502 -0.5546422 1 0 1 1
## -0.494063608051645 -0.4940636 1 0 1 1
## -0.433485061442789 -0.4334851 1 0 1 1
## -0.372906514833933 -0.3729065 1 0 1 1
## -0.312327968225077 -0.3123280 1 0 1 1
## -0.251749421616221 -0.2517494 1 0 1 1
## 0.105275600916307 0.1052756 0 1 1 1
## 0.165854147525163 0.1658541 0 1 1 1
## 0.226432694134019 0.2264327 0 1 1 1
## 0.287011240742875 0.2870112 0 1 1 1
## 0.347589787351731 0.3475898 0 1 1 1
## 0.408168333960587 0.4081683 0 1 1 1
## 0.648738408503121 0.6487384 1 1 1 1
## 0.709316955111977 0.7093170 1 1 1 1
## 0.769895501720833 0.7698955 1 1 1 1
## 0.830474048329689 0.8304740 1 1 1 1
## 0.891052594938546 0.8910526 1 1 1 1
## 0.951631141547402 0.9516311 1 1 1 1
## organizations se.related se.pension se.vacation
## -4.25093822316251 6 0.000000e+00 0.000000e+00 0.000000e+00
## -4.19035967655366 5 0.000000e+00 0.000000e+00 0.000000e+00
## -4.1297811299448 4 0.000000e+00 0.000000e+00 0.000000e+00
## -4.06920258333594 3 0.000000e+00 0.000000e+00 0.000000e+00
## -4.00862403672709 2 0.000000e+00 0.000000e+00 0.000000e+00
## -3.94804549011823 1 0.000000e+00 0.000000e+00 0.000000e+00
## -3.7074754155757 6 0.000000e+00 0.000000e+00 0.000000e+00
## -3.64689686896684 5 0.000000e+00 0.000000e+00 0.000000e+00
## -3.58631832235798 4 0.000000e+00 0.000000e+00 0.000000e+00
## -3.52573977574913 3 0.000000e+00 0.000000e+00 0.000000e+00
## -3.46516122914027 2 0.000000e+00 0.000000e+00 0.000000e+00
## -3.40458268253142 1 0.000000e+00 0.000000e+00 0.000000e+00
## -3.04755765999889 6 0.000000e+00 0.000000e+00 0.000000e+00
## -2.92640056678118 4 0.000000e+00 0.000000e+00 0.000000e+00
## -2.86582202017232 3 0.000000e+00 6.159077e-17 0.000000e+00
## -2.80524347356346 2 0.000000e+00 3.631379e-17 0.000000e+00
## -2.74466492695461 1 0.000000e+00 0.000000e+00 0.000000e+00
## -2.72248307515592 6 0.000000e+00 0.000000e+00 0.000000e+00
## -2.6013259819382 4 0.000000e+00 0.000000e+00 0.000000e+00
## -2.56598156364506 5 0.000000e+00 0.000000e+00 0.000000e+00
## -2.54074743532935 3 0.000000e+00 0.000000e+00 0.000000e+00
## -2.5054030170362 4 0.000000e+00 0.000000e+00 0.000000e+00
## -2.50409485241207 6 0.000000e+00 0.000000e+00 0.000000e+00
## -2.48016888872049 2 0.000000e+00 0.000000e+00 0.000000e+00
## -2.44482447042734 3 0.000000e+00 0.000000e+00 0.000000e+00
## -2.44351630580322 5 0.000000e+00 0.000000e+00 0.000000e+00
## -2.41959034211163 1 0.000000e+00 0.000000e+00 0.000000e+00
## -2.38424592381849 2 0.000000e+00 0.000000e+00 0.000000e+00
## -2.38293775919436 4 0.000000e+00 0.000000e+00 0.000000e+00
## -2.32366737720963 1 0.000000e+00 0.000000e+00 0.000000e+00
## -2.32235921258551 3 0.000000e+00 0.000000e+00 0.000000e+00
## -2.26178066597665 2 2.076334e-17 2.076334e-17 0.000000e+00
## -2.20120211936779 1 0.000000e+00 0.000000e+00 0.000000e+00
## -2.1790202675691 6 0.000000e+00 0.000000e+00 0.000000e+00
## -2.11844172096025 5 0.000000e+00 0.000000e+00 0.000000e+00
## -2.0830973026671 6 0.000000e+00 0.000000e+00 0.000000e+00
## -2.05786317435139 4 0.000000e+00 0.000000e+00 0.000000e+00
## -2.02251875605824 5 0.000000e+00 0.000000e+00 0.000000e+00
## -1.99728462774253 3 0.000000e+00 0.000000e+00 0.000000e+00
## -1.96194020944939 4 0.000000e+00 0.000000e+00 0.000000e+00
## -1.93670608113368 2 0.000000e+00 0.000000e+00 0.000000e+00
## -1.90136166284053 3 0.000000e+00 0.000000e+00 0.000000e+00
## -1.87612753452482 1 0.000000e+00 0.000000e+00 0.000000e+00
## -1.84078311623167 2 1.407415e-17 0.000000e+00 0.000000e+00
## -1.78020456962282 1 0.000000e+00 0.000000e+00 0.000000e+00
## -1.45852396538344 5 0.000000e+00 0.000000e+00 0.000000e+00
## -1.42317954709029 6 0.000000e+00 0.000000e+00 0.000000e+00
## -1.39794541877458 4 0.000000e+00 0.000000e+00 0.000000e+00
## -1.33736687216572 3 0.000000e+00 0.000000e+00 0.000000e+00
## -1.30202245387258 4 0.000000e+00 0.000000e+00 0.000000e+00
## -1.27678832555687 2 0.000000e+00 0.000000e+00 0.000000e+00
## -1.24144390726372 3 0.000000e+00 0.000000e+00 0.000000e+00
## -1.21620977894801 1 0.000000e+00 0.000000e+00 0.000000e+00
## -1.18086536065486 2 0.000000e+00 0.000000e+00 0.000000e+00
## -1.12028681404601 1 0.000000e+00 0.000000e+00 0.000000e+00
## -1.09810496224732 6 0.000000e+00 0.000000e+00 0.000000e+00
## -1.03752641563846 5 0.000000e+00 0.000000e+00 5.821941e-17
## -0.976947869029604 4 0.000000e+00 0.000000e+00 0.000000e+00
## -0.975639704405479 6 0.000000e+00 0.000000e+00 0.000000e+00
## -0.916369322420748 3 0.000000e+00 0.000000e+00 0.000000e+00
## -0.915061157796623 5 0.000000e+00 0.000000e+00 0.000000e+00
## -0.879716739503475 6 3.324619e-17 3.324619e-17 0.000000e+00
## -0.855790775811892 2 0.000000e+00 0.000000e+00 0.000000e+00
## -0.854482611187766 4 4.952958e-17 4.952958e-17 4.952958e-17
## -0.819138192894619 5 0.000000e+00 0.000000e+00 0.000000e+00
## -0.795212229203035 1 0.000000e+00 0.000000e+00 1.800281e-17
## -0.79390406457891 3 0.000000e+00 0.000000e+00 0.000000e+00
## -0.758559646285763 4 2.258359e-17 2.258359e-17 0.000000e+00
## -0.733325517970054 2 0.000000e+00 0.000000e+00 0.000000e+00
## -0.697981099676907 3 0.000000e+00 0.000000e+00 0.000000e+00
## -0.672746971361198 1 0.000000e+00 0.000000e+00 0.000000e+00
## -0.63740255306805 2 0.000000e+00 0.000000e+00 0.000000e+00
## -0.576824006459194 1 0.000000e+00 0.000000e+00 0.000000e+00
## -0.554642154660502 6 0.000000e+00 0.000000e+00 0.000000e+00
## -0.494063608051645 5 0.000000e+00 0.000000e+00 0.000000e+00
## -0.433485061442789 4 0.000000e+00 0.000000e+00 0.000000e+00
## -0.372906514833933 3 0.000000e+00 0.000000e+00 0.000000e+00
## -0.312327968225077 2 6.861203e-18 0.000000e+00 6.861203e-18
## -0.251749421616221 1 0.000000e+00 0.000000e+00 0.000000e+00
## 0.105275600916307 6 0.000000e+00 0.000000e+00 0.000000e+00
## 0.165854147525163 5 0.000000e+00 0.000000e+00 0.000000e+00
## 0.226432694134019 4 0.000000e+00 0.000000e+00 0.000000e+00
## 0.287011240742875 3 0.000000e+00 0.000000e+00 0.000000e+00
## 0.347589787351731 2 0.000000e+00 0.000000e+00 0.000000e+00
## 0.408168333960587 1 0.000000e+00 0.000000e+00 0.000000e+00
## 0.648738408503121 6 0.000000e+00 0.000000e+00 0.000000e+00
## 0.709316955111977 5 0.000000e+00 0.000000e+00 0.000000e+00
## 0.769895501720833 4 6.270735e-18 6.270735e-18 6.270735e-18
## 0.830474048329689 3 0.000000e+00 0.000000e+00 0.000000e+00
## 0.891052594938546 2 0.000000e+00 0.000000e+00 0.000000e+00
## 0.951631141547402 1 0.000000e+00 0.000000e+00 0.000000e+00
## se.insurance se.organizations
## -4.25093822316251 0.000000e+00 0.000000e+00
## -4.19035967655366 0.000000e+00 0.000000e+00
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## 0.891052594938546 0.000000e+00 0.000000e+00
## 0.951631141547402 0.000000e+00 0.000000e+00The first analysis will look at variation in my job satisfaction index across gender, education, race/ethnicity level and number of children.
A comparison by gender shows that males are more likely to experience job statisfaction than women as observed in the gender plot.
The education plot shows interestingly that those with a professional degree rank lower in the job satisfaction index while those with a masters degree rank highest.
Our race/ethnicity plot shows that the white participants of our sample rank higher than the asian and other minorities in the index of job satisfaction.
The number of children does not show any visible differences between those that have one child versus those that have two or more.
library(ggplot2)
ggplot(aes(x=gender, y=PC1), data=cpsipums4)+geom_boxplot()
ggplot(aes(x=dgrdg, y=PC1), data=cpsipums4)+geom_boxplot()
ggplot(aes(x=raceth, y=PC1), data=cpsipums4)+geom_boxplot()
ggplot(aes(x=chtot, y=PC1), data=cpsipums4)+geom_boxplot()
fit.9<-svyglm(PC1~gender+dgrdg+raceth+chtot, des, family=gaussian)
summary(fit.9)##
## Call:
## svyglm(formula = PC1 ~ gender + dgrdg + raceth + chtot, des,
## family = gaussian)
##
## Survey design:
## svydesign(ids = ~1, strata = ~sample, weights = ~weight, data = cpsipums4)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.14040 0.05922 -2.371 0.0178 *
## gendermale 0.25822 0.05119 5.044 4.60e-07 ***
## dgrdg1masters 0.08405 0.05427 1.549 0.1214
## dgrdg2doctorate 0.13741 0.08973 1.531 0.1257
## dgrdg3professional -0.50685 0.07370 -6.877 6.34e-12 ***
## racethasian -0.04470 0.08292 -0.539 0.5898
## racethminorities 0.12017 0.06254 1.921 0.0547 .
## chtottwo or more children 0.05318 0.05029 1.057 0.2903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.450064)
##
## Number of Fisher Scoring iterations: 2