# R Dataset / Package DAAG / nsw74psid1

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dataset-20278.csv | 110.8 KB |

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On this Picostat.com statistics page, you will find information about the nsw74psid1 data set which pertains to Labour Training Evaluation Data. The nsw74psid1 data set is found in the DAAG R package. You can load the nsw74psid1 data set in R by issuing the following command at the console data("nsw74psid1"). This will load the data into a variable called nsw74psid1. If R says the nsw74psid1 data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the nsw74psid1 R data set. The size of this file is about 113,463 bytes. ## Labour Training Evaluation Data## DescriptionThis data frame contains 2675 rows and 10 columns. These data are pertinent to an investigation of the way that earnings changed, between 1974-1975 and 1978, in the absence of training. Data for the experimental treatment group (NSW) were combined with control data results from the Panel Study of Income Dynamics (PSID) study. ## Usagensw74psid1 ## FormatThis data frame contains the following columns: - trt
a numeric vector identifying the study in which the subjects were enrolled (0 = PSID, 1 = NSW). - age
age (in years). - educ
years of education. - black
(0 = not black, 1 = black). - hisp
(0 = not hispanic, 1 = hispanic). - marr
(0 = not married, 1 = married). - nodeg
(0 = completed high school, 1 = dropout). - re74
real earnings in 1974. - re75
real earnings in 1975. - re78
real earnings in 1978.
## Sourcehttp://www.columbia.edu/~rd247/nswdata.html ## ReferencesDehejia, R.H. and Wahba, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053-1062. Lalonde, R. 1986. Evaluating the economic evaluations of training programs. American Economic Review 76: 604-620. ## Examplesprint("Interpretation of Regression Coefficients - Example 6.6") nsw74psid1.lm <- lm(re78~ trt+ (age + educ + re74 + re75) + (black + hisp + marr + nodeg), data = nsw74psid1) summary(nsw74psid1.lm)$coef options(digits=4) sapply(nsw74psid1[, c(2,3,8,9,10)], quantile, prob=c(.25,.5,.75,.95,1)) attach(nsw74psid1) sapply(nsw74psid1[trt==1, c(2,3,8,9,10)], quantile, prob=c(.25,.5,.75,.95,1)) pause()here <- age <= 40 & re74<=5000 & re75 <= 5000 & re78 < 30000 nsw74psidA <- nsw74psid1[here, ] detach(nsw74psid1) table(nsw74psidA$trt) pause()A1.lm <- lm(re78 ~ trt + (age + educ + re74 + re75) + (black + hisp + marr + nodeg), data = nsw74psidA) summary(A1.lm)$coef pause()A2.lm <- lm(re78 ~ trt + (age + educ + re74 + re75) * (black + hisp + marr + nodeg), data = nsw74psidA) anova(A1.lm, A2.lm) -- Dataset imported from https://www.r-project.org. |

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