• http://www.indiana.edu/~jslsoc | http://lib.stat.cmu.edu/stata/
• http://www.stata.com/support/ | http://www.stata-journal.com/
• http://www.stata-press.com/ | http://www.stata.com/support/faqs/
• Generalized Linear Latent And Mixed Models (GLLAMMs)
• Psmatch2.ado Leuven and Sianesi) | Pscore.ado (Becker and Ichino)
• Stata Tutorial (Princeton University) | Stata Tutorial (Univ. of North Carolina)
• Stata Resources (UCLA) | Stata Introduction (Robert A. Yaffee of NYU)
• http://sobek.colorado.edu/LAB/STATS/stata_help.html (UNIX)

• Part I. Getting Started
• Part II. Working with Data Sets
• Part III. Ordinary Least Squares (OLS)
• Part IV. Categorical Dependent Variable Models
• Part V. Panel Data Models
• Basic Statistical Analysis | Analysis of Variance | Multivariate Analysis

Basic Commands

• help regress
• display "Hello"
• di di (1-normal(1.96))*2
• di sqrt(3.14)
• describe x1 x2
• des using jeeshim.dbt
• list in 1/10
• list male-income
• list pop* pro? if male==1
• list -2
• summarize male grade
• sum x1-x10 y* post?
• sum income family if male~=.
• sum income if (male==1) & (class >=3)
• tabulate male educate
• tab male grade, chi2 row col
• tabi 12 33 \ 34 53, chi2 exact
• tabi 34 53 23 \ 23 56 34 \ 45 32 21, chi2 all
• tabstat math english, by(male) stats(n mean sum sd var max min skewness)

Management Commands

• codebook male
• label data "Pew Internet and American Life Project"
• label variable male "Gender"
• label variable male; // to remove a variable lable
• label define yn 1 yes 0 no
• label values open yn
• compare math english
• cf math english using indiana.dta
• ci grade if male==0; /* confidence interval */
• count if math >90
• lookfor gnp
• notes var: Need to be verified
• update all
• net search Spost
• net from http://www.indiana.edu/~jslsoc/stata
• net describe spost9_ado
• net spost9_ado
• net get spost9_do
• ssc whatsnew
• ssc describe ...
• ssc install ...

Operators

• +, -, *, /, ^
• >, >=, <, <=, == (equal), ~= (not equal)
• & (and), | (or), ~ (not)
• +=, =+, -=, =-, /=, %=, &=, |=, *=
• in (in if command)
• + (other variables), 1/b (from a through b), . (missing values)
• wild card (*, ?, /, -)
• concatenation (+)

Math Functions

• abs(); sin(); cos(); tan();asin(); acos(); atan()
• ceil(); floor(); int() or trunc(); round()
• exp(); sqrt(); log(); ln(); log10()
• min(); max(); sign(); sum(); mod(x,y); comb(x,k)

Probability Distribution

• binomal(h,k,p) // joint cumulative distribution of bivariate normal
• chi2(df,x) // cumulative chi squared distribution
• chi2tail(n,x) // reverse of chi2()
• F(df1, df2, f) // cumulative F distribution
• Ftail(df1, df2, f) // reverse cumulative (upper-tail) F
• normal(z); normal(1.96) // returns .9750002
• ttail(df, t) // reverse cumulative (upper-tail) T
• uniform() // uniform distributionreverse cumulative (upper-tail) T
• di chi2tail(10,18.31) // returns .04995417, p-value
• di F(5, 10, 3.325) // returns .9499661
• di Ftail(5, 10, 3.325) // returns .05000, the pa-value
• di (1-normal(z))*2 // compute the p-value for the two-tailed test
• di ttail(20, 2.086) // returns .02499818
• di ttail(df, t)*2 // compute the p-value for the two-tailed test

String Functions

• char(n); length(s); trim(s); ltrim(s); rtrim(s)
• string(n); substr(s,begin, length)
• real(s); reverse(s); word(); lower(); upper()

Handling Data Sets

• use "k:\kucc625\open.dta", clear
• use jeeshim.dta, clear nolabel
• use using jeeshim.dta if gender==1, clear nolabel
• save "c:\kucc625\open.dta", replace
• save open.dta, replace nolabel
• saveold "c:\kucc625\jeeshim.dta", replace
• log using "c:\kucc625\open.log", append
• log using open.log, append text
• log on // log off; log close
• logcmd using "c:\kucc625\open_cmd.log", replace
• logcmd on // logcmd off; logcmd close

Import

• infile a b c using jeeshim.txt, clear
• infile str15 name float weight int height using student.txt
• inf id _skip(1) q1-q3 using student.txt, clear
• inf str20 id long (q1-q3) using student.txt, clear
• inf id double (q10-q13) income if income >50000 using student.txt
• inf using student.dct, clear
• infix year 1-4 gnp 5-9 interest 10-13 using macro.txt
• infix using macro.dct in 1/100, clear
• insheet using jeeshim.csv, clear
• insheet a b c d e f g using student.txt, comma clear
• insheet using student.txt, delim("#") clear

Export

• odbc list
• odbc load ID=year gnp interest in 1/500, table("macro") dsn("jeeshim")
• outfile using jeeshim.txt
• outfile x1-x10 using jeeshim.txt, wide replace
• outfile using jeeshim.txt, nolabel noquote replace
• outsheet using jeeshim.xls, nolabel /* tab delimited */
• outsheet using jeeshim.xls, comma replace

Editing

• keep gender grade korean math english if gender=1
• keep id q1-q20
• drop temp1-temp5 if gender=0
• drop temp* pro? if income <5000
• drop in 1/10
• drop if gender==1 in 1/100
• edit
• edit in 10/-5
• edit if gender==0
• edit in 1/100 if income >5000
• edit male class if income >=30000
• mark yn_miss
• markout yn_miss q1-q10 //0 if any one of variable has missing
• isid college // to check for unique identifiers

Recoding

• generate gender; gen gender=male
• gen square=gnp^2
• gen grade=(score <= 90 | attendance==0) if final~=.
• egen avg = rowmean(english math stats)
• egen gnp_bar = mean(gnp), by(country)
• replace gender=0
• replace gender=1 if male==1
• replace male=1 in 3
• recode class 1=0 2=1 *=.
• recode class 1/3=0 4=1 if male==0
• recode grade 1 2 3 5=1 4=2
• recode grade 9999=.
• recode grade min/5=min
• recode grade 6/max=max

Reshaping Data Sets

• set obs 100 // to change the numnber of observations
• sort male grade
• gsort -grade name, gen(rank)
• append using c:\data\class
• app using c:\data\class, keep(id state q1-q10)
• expand 5 in -10/-1 // duplicate observations n-1 times
• merge using school // one-to-one merging
• merge state using school // match merging
• merge state using school university, update replace
• joinby id using secondary, unmatched(master) // unm(both), unm(using)
• move male grade
• order grade male // order variables as listed
• rename male gender; /* from male to gender */
• expand 5 if state=="IN" // duplicate a subset of observations
• collapse a b (sd) c (count) d (max)
• collapse a b (sd) c (count) d (max), by state
• contract gender degree area, freq(count) zero
• reshape long choice, i(id) j(orders)
• stack best1-best3, into(best) clear
• pkshape id row col1-col3, order(abc cab bca) outcome(y) sequence(rows) treat(treat) period(columns)
• compress // all variable
• compress name grade
• xpose, clear varname

Ordinary Least Squares (OLS)

• regress dv iv1 iv2
• regress depend indep1-indep10, noconstant
• regress income school job location if gender==0
• regress income school job location if gender==0, noconstant level(95)
• predict p // xb
• predict r, residual
• fitstat
• quietly fitstat, saving(model1)
• fitstat, using(model1)
• fitstat, dif
• bgodfrey, lag(1 2 3); estat bgodfrey, lag(1 2 3)
• dwstat; estat dwatson, lag(1 2 3)
• stepwise, pr(.2): regress y x1-x10 // backward stepwise regression

• constraint define 1 d1+d2+d3=0 // LSDV2 in STATA
• constraint define 2 g1+g2+g3+g4=0
• cnsregs y x1 x2 d1-d3 g1-g4, constraint(1 2)

Hypothesis Test

• test school /* Wald Test */
• test school location; test school=location
• test job; test school, accumulate
• lrtest, saving(0); /* Likelihood Ratio Test */
• lrtest, saving(1)
• lrtest, using(1) model(0) /* 1 for full model and 0 for nested one*/

Advanced Models

• boxcox //Box-Cox regression model
• eivreg // errors-in-variables regression
• fracpoly // Fractional polynomial regression
• frontier //Stochastic frontier models
• glm // generalized linear model
• intreg //interval regression
• ivreg //instrumental variables (two-stage least squares) regression
• ivreg dv iv1 iv2 (iv3= x1 x2 x3) iv4 iv5
• mfp //multivariable fractional polynomial models
• mvreg //multivariate regression
• newey //Regression with Newey-West standard errors
• nl //nonlinear least-squares estimation
• orthog //Orthogonalize variables and compute orthogonal polynomials
• prais dv1 rhs, rho(tscorr) twostep //Prais-Winsten two-step
• prais dv1 rhs, rho(dw) // iterative two-step
• prais dv1 rhs, rho(dw) corc // Cochrane-Orcutt
• qreg //Quantile (including median) regression
• reg3 //three-stage estimation for systems of simultaneous equations
• reg3 (dv1 x1 x2) (dv2 x1 x3)
• reg3 (dv1 dv2 = x1 x2 x3)
• reg3 (dv1 dv2 = x1 x2 x3) (dv3 x1 x3)
• rocfit //fit ORC model
• rreg //robust regression
• stcox //fit Cox proportional hazards model
• streg //fit parametric survival model
• sureg //Zellner's seemingly unrelated regression
• stepwise //stepwise estimation
• treatreg //treatment-effects model
• treatreg y x1 x2 x3, treat(x4=z1 z2) twostep
• vwls //variance-weighted least squares

• tsset group year // set group and time
• xtreg y x1 x2, re i(year) // random effect model
• xtreg y x1 x2, fe i(group) // random effect model
• xtreg y x1 x2, be i(group) // between effect model
• areg // linear regression with a large dummy-variable set
• xtabond // Arellano-Bond linear, dynamic panel-data estimator
• xtcloglog // Random-effects, population-averaged cloglog models
• xtgee // fit population-averaged panel-data models using GEE
• xtfrontier // stochastic frontier models for panel data
• xtgls // fit panel-data models using GLS
• xthtaylor // Hausman-Taylor estimator for error components models
• xtinreg // random-effects interval data regression models
• xtivreg // Instrumental variables and two-stage least squares
• xtlogit //fixed-effects, random-effects, population-averaged logit
• xtmixed // multilevel mixed-effects linear regression
• xtprobit // random-effects and population averaged probit models
• xttobit // random-effects tobit models
• xtnbreg //fixed-effects, random-effects, and population-averaged NB
• xtpcse // Prais-Winsten models with panel-corrected standard errors
• xtpoisson //fixed-effects, random-effects, population-averaged Poisson
• xtrc // random-coefficients models
• xtregar // fixed-and random-effects linear models with an AR(1)

Binary Logit/Probit

• logit dv iv1 iv2
• logit card income school job if gender==0, nolog nocon
• logistict dv iv1 iv2
• probit dv iv1 iv2
• predict p
• prchange; prchange income, x(school=1 job=1)
• prchange school, x(income=10000) help
• prtab income school, rest(mean)
• prgen income, from(0) to(10000) x(school=1) rest(mean)
• prvalue, rest (mean)
• prvalue, x(income=10000 job=1) rest(mean)

Ordinal and Multinomial

• ologit dv iv1 iv2 // Ordinal
• ologit grade income school job if gender==0, nolog nocon
• oprobit dv iv1 iv2
• omodel logit card income school job // Approximate LR test
• mlogit dv iv1 iv2; /* Nominal */
• mlogit mode income school job, basecategory(1) nolog
• mlogtest, lr
• mlogtest, wald
• mlogtest, hausman base
• mprobit // multinomial probit regression
• clogit dv iv1 iv2, group(var) // Conditional logit
• clogit mode income school job, group(gender) nolog
• nlogit // nested logit regression

Special Logit/Probit

• asmprobit // alternative-specific multinomial probit
• biprobit (dv1=rhs1) (dv2=rhs2) // bivariate probit
• glogit //logit and porbit for grouped data
• heckprob dv rhs, select(rhs2) // probit model with selection
• hetprob // heteroskedastic probit
• ivprobit // probit model with endogenous regressions
• rologit // rank-ordered logistic
• scobit // Skewed logit
• slogit // sterotype logistic
• xtlogit // logit models for panel data
• xtprobit // probit models for panel data

Event Count Data Models

• poisson dv iv1 iv2
• nbreg dv iv1 iv2
• zip dv iv1 iv2 // zero-inflated Poisson Model
• zinb dv iv1 iv2 // zero-inflated NB Model
• ztp dv iv1 iv2 // zero-truncated Poisson Model
• ztnb dv iv1 iv2 // zero-truncated NB Model

Truncated/Censored/Self-selected

• cnreg // Censored-normal regression
• heckman // Heckman selection model
• ivtobit // Tobit model with endogenous regressors
• tobit // Tobit regression
• truncreg // truncated regression
• ztp dv iv1 iv2 // zero-truncated Poisson Model
• ztnb dv iv1 iv2 // zero-truncated NB Model

Related Commands

• bootstrap // bootstrap sampling and estimation
• bsample // Sampling with replacement
• jackknife // Jackknife estimation
• impute // imputation
• permute // Monte Carlo permutation test
• simulate // Monte Carlo simulation
• sampsi // sample size and power determination

T-Test

• ttest grade==10
• ttest grade, by(male)
• ttest grade, by(male), unequal welch
• ttest math=english; ttest math==english, unpaired
• ttesti 100 88.1 5.2 90; /* N mean sd hypothesis */
• ttesti 100 88.1 5.2 200 91 10.2; /* N1 mean1 sd1 N2 mean2 sd2 */
• ttesti 100 88.1 5.2 200 91 10.2, unequal
• mean // estimate means
• total // estimate totals
• ratio // estimate ratios
• proportion // one- and two-sample tests of proportions
• ci // confidence intervals for means, proportions, and counts

ANOVA

• anova score gender
• anova score gender year gender*year
• oneway score gender, tabulate
• loneway //large one-way ANOVA, random effect, and reliability
• sdtest // Variance-comparison test
• Related: .manova; .pkshape; .xtmixed

Correlation Analysis

• correlate gnp interest inflation
• corr gnp interest inflation, covariance
• pcorr x1-x10 // partial correlation coefficients
• pwcorr gnp interest inflation, sig
• pwcorr gnp interest inflation, print(5) // .05 significance level
• pwcorr gnp interest inflation, sig star(.05) // .05 level

Factor Analysis

• factor x1-x30 // by default pcf (principal component factor)
• factor x1-x30, ml // maximum likelihood factor
• factor x1-x30, factors(5)
• rotate, varimax // orthogonal, oblique, quartimax, equamax, parsimax, promax
• pca // principal component analysis

Other Analysis

• alphar // Cronbach's alpha
• ca // correspondence analysis
• canon // Canonical correlation
• cluster // cluster analysis
• mvreg // multivariate regression
• manova // multivariate MANOVA
• mds // multidimensional scaling for two way data
• mdslong
• mdsmat
• biplot

• swilk math english
• sfrancia x1-x10
• ranksum //Equality tests on unmatched data
• signrank math=english // Equality tests on matched data
• runtest // test for random order
• spearman x1-x10
• kwallis score, by(gender)
• ksmirnov math, by(area)
• alpha x1-x10, item
• kappa eval1 eval2
• bitest //Binomial probability test
• prtest // one- and two-sample tests of proportions

Basics

• version; version 7
• memory
• set memory 100m
• macro list // list a macro list
• macro list _x // display contents of a macro x
• macro drop _all

Macros

• global var "dv"; global i=1;
• gen $dv$i = income; gen ${dv$i}=income; // gen dv1=income
• global rhs "x1 x2 x3"; regress y $rhs
• local var "dv"; local i=1;
• gen `dv'`i' = income; gen `dv`i''=income; // gen dv1=income
• local rhs "x1 x2 x3"; regress y `rhs'
• local x=stat[1,1] // from a matrix
• display `x'

Loop

local i=0
foreach var in var1-var10 {
   local i=`i'+1
   sum `var'
   display `i'
}

foreach var of varlist var1-var10 {
   ...
}

local car "Sonata Camry Accord"
foreach lm of local car {
   display "`lm'"
}

global gm "Sonata Camry Accord"
foreach gm of global car {
   display "`gm'"
}

foreach str in "Sonata Camry Accord" {
   display "`str'"
}

forvalues num = 1/100 {
   ...
}

• forvalues num = 1(1)100 {...}
• forvalues num = 100(-1)1 {...}
• forvalues num = 1 2: 100 {...}
• forvalues num = 1 2 to 100 {...}

Matrices

• mata // beginning of mata
• : end // end of mata
• :a=(1, 2 \ 3, 4); :b=(6, 3 \ 4, 9)
• :a; :a[1,1]; :a[2,] :b[,2]
• :c=a+b; :d=a-b
• :e=a*b; :f=a:*b //element by element
• :diag(); :diagonal() // create a diagonal matrix and extract diagonal
• :vec() //convert matrix into column vector
• :trace(); :det(); :rank()
• :cond(); :eigenvalues();
• :cholesky()
• :I(); :e() // identity matrix, unit vectors
• :J(row, col, constant) // matrix of constants

• matrix input Y=(1, 2 \ 3, 4)
• mkmat x1 x2 x3, matrix(X) // convert variables into a matrix
• svmat x, name(x) // convert a matrix into variables (x1, x2, x3)
• matrix COV=get(VCE)
• matrix BETA=get(_b)
• matrix list X
• matrix INVX=inv(X)
• matrix DETX=det(X)

Graphics Basics

• sysuse auto, clear
• graph bar (mean) mpg turn, by(foreign)
• graph bar (mean) mpg turn, over(foreign)
• graph hbar (mean) mpg turn, over(foreign)
• graph hbar (mean) mpg, over(foreign) over(class)
• graph hbar (mean) mpg, over(class) over(foreign)
• graph hbar (mean) mpg, over(class) over(foreign, sort(1) descending)
• graph hbar (sum) mpg turn, over(class) stack
• graph hbar (sum) mpg turn, over(class) by(foreign)
• graph hbar (sum) mpg turn, over(class) by(foreign) stack
• graph dot (mean) mpg, over(class)
• graph dot (mean) mpg, over(class) over(foreign)
• graph matrix mpg price turn, half
• graph pie, over(class)
• graph pie mpg turn trunk, plabel(_all name)

Scatter and Two-way Plotting (Examples)

• scatter mpg weight
• scatter mpg weight, sort
• scatter mpg weight, sort connect(1)
• scatter mpg weight, sort title("MPG versus Weight") subtitle("Year 2006")
• scatter mpg weight, title("MPG versus Weight") caption("Source: STATA Corp. 2006")
• scatter mpg weight, title("MPG versus Weight") xsize(4) ysize(3)
• scatter mpg weight, ytitle("MPG (Mileage)") xtitle("Car Weight")
• scatter mpg weight, title("MPG versus Weight") ylabel(#8) xlabel(0(2000)6000)
• scatter mpg weight, title("MPG versus Weight") ylabel(minmax) xlabel(minmax)
• scatter mpg weight, title("MPG versus Weight") yscale(log) xlabel(#5) // log scales
• scatter mpg weight, sort xline(4000) yline(25)
• scatter mpg weight, title("MPG versus Weight") msymbol(triangle)
• scatter mpg weight || fpfit mpg weight
• twoway fpfitci mpg weight
• twoway fpfitci mpg weight || scatter mpg weight, m(d)
• scatter mpg weight, sort title("MPG versus Weight") m(diamond) by(foreign)
• scatter mpg weight, sort m(t) by(foreign, total row(1))
• twoway fpfitci mpg weight, sort m(t) by(foreign, total row(1))
• twoway fpfitci mpg weight || scatter mpg weight, sort m(t) by(foreign, total row(1))
• scatter mpg turn weight
• scatter mpg turn weight , yline(30) xline(3500)
• scatter mpg trunk turn weight
• scatter mpg weight || scatter trunk weight || scatter turn weight
• scatter mpg weight, sort c(1) || line trunk weight, sort || scatter turn weight
• twoway (line mpg weight, sort c(1)) (dropline trunk weight, sort) (scatter turn weight)

Plotting by Functions

• twoway function y=x^3, range(-5 5) xsize(4) ysize(3) xlabel(#10) xline(0)
• twoway function y=normalden(x), range(-5 5) xsize(4) ysize(2) xlabel(#10) xline(0)
• twoway function y=1/sqrt(2*_pi)*exp(-x^2/2), range(-5 5) xsize(4) ysize(2) xlabel(#10) xline(0)
• twoway function y=normalden(x), range(-4 -1.96) xlabel(#10) xline(0) recast(area) || function y=normalden(x), range(1.96 4) recast(area) || function y=normalden(x), range(-1.96 1.96) lstyle(foreground)
• twoway function t=tden(3, x), range(-5 5) xsize(4) ysize(2) xline(0)
• twoway function t=tden(1, x), range(-5 5) xsize(4) ysize(2) color(blue) lstyle(p1solid) xlabel(-5(1)5) recast(area) || function z=normden(x), range(-5 5) color(maroon) lwidth(thick)
• scatter gear_ratio headroom, xsize(4) || function y=x, range(0 5)
• twoway function c=chi2(1,x), range(0 5) xsize(4) ysize(3) yline(.5)
• twoway function c=Fden(5, 10, x), range(0 5) xsize(4) ysize(3) yline(.3)