Question

Fitting Logistic Regression model : Dependent Variable (Employed), Independent variables(Age, Race.Ethnicities, Education.Attainment, Gender)

Dataset

Age	Earnings Past 12 Months	Usual Weekly Hours	Female	Married	No High School Degree	High School Degree or GED	Some College	Associates Degree	Bachelors Degree	Masters Degree	Professional Degree	Doctorate	Educational Attainment	Employed	White	Black	American Indian or Native American	Asian	Hawaiian or Pacific Islander	Other Race	Biracial	Hispanic	Race/Ethnicity	Worked 40+ Weeks During Past 12 Months	Worked 35+ Hours in a Typical Week
18	1200	16	0	0	1	0	0	0	0	0	0	0	No High School Degree	1	1	0	0	0	0	0	0	0	White	0	0
53	0		1	0	0	1	0	0	0	0	0	0	High School Degree	0	0	0	0	0	0	0	0	1	Hispanic	0	0
61	0	70	0	1	0	0	1	0	0	0	0	0	Some College	1	1	0	0	0	0	0	0	0	White	1	1
32	350	24	1	0	0	0	1	0	0	0	0	0	Some College	1	1	0	0	0	0	0	0	0	White	0	0
49	0	30	0	0	0	0	0	0	0	1	0	0	Masters Degree	1	1	0	0	0	0	0	0	0	White	1	0

Answer 1

setwdC"c:/users/parekhku/oneDrive - Merck sharp& Dohme, Corp/Desktop" datal-read.esvC Bookl.esw str(datal) data.frame 4 obs of 26 variables: s Age : int 18 53 61 32 : int 1200 0 0 350 : int 16 NA 70 24 Earnings.Past. 12.Months Usual.weekly.Hours Female int 0101 : int 0010 int 1000 : int 01 0 0 : int 0011 : int 0000 : int 0 0 0 0 int 0 000 :int 0 0 0 0 : int 0 0 00 5 Married No. High. School, Degree 5 High, School.Degree. or.GED 5 Some.College Associates. Degree Bachelors. Degree Masters.Degree Professional.Degree Doctorate Educational.Attainnent Employed :Factor w/ 3 levels "High school Degree" 213 3 : int 1011 : int 1011 : int 0 00 0 American. Indn.or.Native.Americannt 00 00 : int 0 000 : int 0 00 0 : int 0 000 int 0 00 0 int 0100 s white Black S Asian Hawai1an.or.Pacific. Islander Other. Race Biracial S Hispanic Race. Ethnicity : Factor w 2 1evels "Hispanic","white": 21 22 5 Worked.40.. Weeks.During. Past.12.Months: int 001 0 $ Worked. 35. . Hours.in.a.Typical.Weelk int 0 01 0

: : Console Terminal Merck Sharp &Dohme s white s Black : int 10 11 : int 0000 American. Indian.or.Native.Americanint 0 000 : int 000 0 : int o000 S Asian Hawai1an.or.Pacific.Islander Other. Race Biracial Hispanic Race. Ethnicity int 000 : int 0000 :int 0100 Factor w 2 1evels "Hispanic", "white": 21 2 2 S Worked.40..Weeks.During.Past. 12.Months: int 0 010 s Worked. 35..Hours.in.a. Typical.weekint 0 0 10 reg-glmCEmployed- Error: unexpected )in "reg-glm(Employed-)" > reg-glm(Employed-Age+Race.Ethnicity Educational.Attainment+Female, data-datal,binonial) sunmary(reg) Call: g1m(formula EmployedAge Race.EthnicityEducational.Attainment+ Female, family= binomial, data data1) Deviance Residuals: 1 0 00 0 Coefficients: 2 not defined because of singularities) Estimate Std. Error z value Pr(lzl) 2.357e+01 2.202e+05 2.496e-11 3.875e-03 4.713e+0 1.005e-05 (Intercept) Race. Ethnicitywhite Educational.AttainmentNo High School Degree 1.426e-09 1.472e+05 Educational.AttainmentSome College Female CDispersion parameter for binomial family taken to be 1) Null deviance: 4.4987e+00 on 3 degrees of freedom Residual deviance: 4.6610e-10 on 0 degrees of freedom AIC: 8
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