library(wooldridge)
library(rmarkdown)
data('engin')
paged_table(engin)This is a nice change of pace from wage data sets for the United States. These data are for engineers in Thailand, and represents a more homogeneous group than data sets that consist of people across a variety of occupations. Plus, the starting salary is also provided in the data set, so factors affecting wage growth – and not just wage levels at a given point in time – can be studied. This is a good data set for a common term project that tests basic understanding of multiple regression and the interpretation of models with a logarithm for a dependent variable.A data.frame with 403 observations on 17 variables:
male: =1 if male
educ: highest grade completed
wage: monthly salary, Thai baht
swage: starting wage
exper: years on current job
pexper: previous experience
lwage: log(wage)
expersq: exper^2
highgrad: =1 if high school graduate
college: =1 if college graduate
grad: =1 if some graduate school
polytech: =1 if a polytech
highdrop: =1 if no high school degree
lswage: log(swage)
pexpersq: pexper^2
mleeduc: male*educ
mleeduc0: male*(educ - 14)
summary is a generic function used to produce result summaries of the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument.
summary(lm(formula = male ~ educ + wage + swage + exper + pexper + lwage + expersq + highgrad ,data = engin))##
## Call:
## lm(formula = male ~ educ + wage + swage + exper + pexper + lwage +
## expersq + highgrad, data = engin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98404 -0.23997 -0.00984 0.28752 0.86903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.433e+01 2.243e+00 -6.390 4.68e-10 ***
## educ 5.425e-03 1.076e-02 0.504 0.6144
## wage -2.996e-05 5.289e-06 -5.664 2.86e-08 ***
## swage 1.056e-05 5.953e-06 1.774 0.0769 .
## exper -1.716e-01 1.270e-01 -1.351 0.1773
## pexper 6.464e-03 1.950e-03 3.315 0.0010 **
## lwage 1.630e+00 2.211e-01 7.370 1.01e-12 ***
## expersq 5.842e-03 4.686e-03 1.247 0.2132
## highgrad -1.972e-01 4.628e-02 -4.261 2.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3728 on 394 degrees of freedom
## Multiple R-squared: 0.4547, Adjusted R-squared: 0.4436
## F-statistic: 41.07 on 8 and 394 DF, p-value: < 2.2e-16library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazermodel1 <- lm( male ~ educ + wage + swage + exper + pexper + lwage + expersq + highgrad ,data = engin)model1_2 <- lm( male ~ educ + log(wage) + swage + exper + pexper + lwage + expersq + highgrad ,data = engin)stargazer(model1,model1_2, type = "text")##
## ===================================================================
## Dependent variable:
## -----------------------------------------------
## male
## (1) (2)
## -------------------------------------------------------------------
## educ 0.005 0.018
## (0.011) (0.011)
##
## wage -0.00003***
## (0.00001)
##
## log(wage) 0.537***
## (0.112)
##
## swage 0.00001* -0.00000
## (0.00001) (0.00001)
##
## exper -0.172 -0.110
## (0.127) (0.131)
##
## pexper 0.006*** 0.008***
## (0.002) (0.002)
##
## lwage 1.630***
## (0.221)
##
## expersq 0.006 0.004
## (0.005) (0.005)
##
## highgrad -0.197*** -0.236***
## (0.046) (0.048)
##
## Constant -14.333*** -4.400***
## (2.243) (1.452)
##
## -------------------------------------------------------------------
## Observations 403 403
## R2 0.455 0.410
## Adjusted R2 0.444 0.400
## Residual Std. Error 0.373 (df = 394) 0.387 (df = 395)
## F Statistic 41.067*** (df = 8; 394) 39.262*** (df = 7; 395)
## ===================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01lm(scale(wage) ~ scale(educ) + scale(wage) +scale(swage) + scale(exper) + scale(pexper) + scale(lwage) + scale(expersq) ,data = engin)## Warning in model.matrix.default(mt, mf, contrasts): the response appeared on the
## right-hand side and was dropped## Warning in model.matrix.default(mt, mf, contrasts): problem with term 2 in
## model.matrix: no columns are assigned##
## Call:
## lm(formula = scale(wage) ~ scale(educ) + scale(wage) + scale(swage) +
## scale(exper) + scale(pexper) + scale(lwage) + scale(expersq),
## data = engin)
##
## Coefficients:
## (Intercept) scale(educ) scale(swage) scale(exper) scale(pexper)
## -1.753e-15 -7.825e-02 2.107e-01 -2.164e-01 -2.750e-02
## scale(lwage) scale(expersq)
## 8.456e-01 2.140e-01lm(scale(wage) ~ scale(educ) + scale(wage) +scale(swage) + scale(exper) + (pexper) + (lwage) + (expersq) ,data = engin)## Warning in model.matrix.default(mt, mf, contrasts): the response appeared on the
## right-hand side and was dropped## Warning in model.matrix.default(mt, mf, contrasts): problem with term 2 in
## model.matrix: no columns are assigned##
## Call:
## lm(formula = scale(wage) ~ scale(educ) + scale(wage) + scale(swage) +
## scale(exper) + (pexper) + (lwage) + (expersq), data = engin)
##
## Coefficients:
## (Intercept) scale(educ) scale(swage) scale(exper) pexper
## -22.612953 -0.078251 0.210717 -0.216424 -0.002625
## lwage expersq
## 2.104275 0.004500