use https://www3.nd.edu/~rwilliam/statafiles/reg01.dta, clear
correlate income educ jobexp race, means
regress income educ jobexp race
regress income educ jobexp race, level(99)
set level 99
regress income educ jobexp race
regress income educ jobexp race, beta
regress income educ jobexp race
regress, level(99)
regress, beta
vif
collin educ jobexp race if !missing(income)
test educ jobexp
test educ=jobexp
test educ=jobexp, coef
nestreg: regress income (race) (educ jobexp)
pcorr income educ jobexp race
sw, pr(.05): regress income educ jobexp race
sw, pe(.05): regress income educ jobexp race
reg income educ jobexp if race==0
sort race
by race: reg income educ jobexp
bysort race: reg income educ jobexp
* First, input the means, sds, and correlations
matrix input Mean = (.44,.82,6.46,7.12)
matrix input SD = (.5,.46,2.11,1.42)
matrix input Corr = (1.00,-.59,-.46,-.26\-.59,1.00,.53,.33\-.46,.53,1.00,.72\-.26,.33,.72,1.00)
* Now use corr2data to create a pseudo-simulation of the data
corr2data sphrd pared aptd grades, n(10609) means(Mean) corr(Corr) sds(SD)
. * Label the variables
. label variable sphrd "Sephardim"
label variable pared "Parental Education Scale"
label variable aptd "Scholastic Aptitude"
label variable grades "Grade Point Average"
* Confirm that all is well
corr sphrd pared aptd grades, means
use https://www3.nd.edu/~rwilliam/statafiles/reg01.dta, clear
regress income educ jobexp i.race
reg income educ jobexp i.race c.educ#c.educ
reg income educ jobexp i.race i.race#c.educ