Some Stata Commands

Last modified: January 2, 2006 9:51AM

General Plotting Commands

  1. Plot a histogram of a variable:
    histogram vname
  2. Plot a histogram of a variable using frequencies:
    histogram vname, freq
    histogram vname, bin(xx) norm
    where xx is the number of bins.
  3. Plot a boxplot of a variable:
    graph box vname
  4. Plot side-by-side box plots for one variable (vone) by categories of another variable vtwo. (vtwo should be categorical)):
    graph box vone, over(vtwo)
  5. A scatter plot of two variables:
    scatter vone vtwo
  6. A matrix of scatter plots for three variables:
    graph matrix vone vtwo vthree
  7. A scatter plot of two variables with the values of a third variable used in place of points on the graph (vthree might contain numerical values or indicate categories, such as male ("m") and female ("f")):
    scatter vone vtwo, symbol([vthree])
  8. Normal quantile plot:
    qnorm vname

General commands

  1. To compute means and standard deviations of all variables:
    summarize
    or, using an abbreviation,
    summ
  2. To compute means and standard deviations of select variables:
    summarize vone vtwo vthree
  3. Another way to compute means and standard deviations that allows the by option:
    tabstat vone vtwo, statistics(mean, sd) by(vthree)
  4. To get more numerical summaries for one variable:
    summ vone, detail
  5. See help tabstat to see the numerical summaries available. For example:
    tabstat vone, statistics(min, q, max, iqr, mean, sd)
  6. Correlation between two variables:
    correlate vone vtwo
  7. To see all values (all variables and all observations, not recommended for large data sets):
    list
    Hit the space bar to see the next page after "-more-" or type "q" to "break" (stop/interrupt the listing).
  8. To list the first 10 values for two variables:
    list vone vtwo in 1/10
  9. To list the last 10 values for two variables:
    list vone vtwo in -10/l
    (The end of this command is "minus 10" / "lowercase letter L".)
  10. Tabulate categorical variable vname:
    tabulate vname
    or, using an abbreviation,
    tab vname
  11. Cross tabulate two categorical variables:
    tab vone vtwo
  12. Cross tabulate two variables, include one or more of the options to produce column, row or cell percents and to suppress printing of frequencies:
    tab vone vtwo, column row cell
    tab vone vtwo, column row cell nofreq

Generating new variables

  1. General.
    1. Generate index of cases 1,2, ...,n (this may be useful if you sort the data, then want to restore the data to the original form without reloading the data):
      generate case= _n
      or, using an abbreviation,
      gen case=_n
    2. Multiply values in vx by b and add a, store results in vy:
      gen vy = a + b * vx
    3. Generate a variable with values 0 unless vtwo is greater than c, then make the value 1:
      gen vone=0
      replace vone=1 if vtwo>c
  2. Random numbers.
    1. Set numbers of observations to n:
      set obs n
    2. Set random number seed to XXXX, default is 1000:
      set seed XXXX
    3. Generate n uniform random variables (equal chance of all outcomes between 0 and 1):
      gen vname=uniform()
    4. Generate n uniform random variables (equal chance of all outcomes between a and b):
      gen vname=a + (b - a)*uniform()
    5. Generate n discrete uniform random variables (equal chance of all outcomes between 1 and 6)
      gen vname=1 + int(6*uniform())
      (These commands simulate rolling a six-sided die.)
    6. Generate normal data with mean 0 and standard deviation 1:
      gen vname= invnorm(uniform())
    7. Generate normal data with mean mu and standard deviation sigma:
      gen vname= mu + sigma * invnorm(uniform())

Regression

  1. Compute simple regression line (vy is response, vx is explanatory variable):
    regress vy vx
  2. Compute predictions, create new variable yhat:
    predict yhat
  3. Produce scatter plot with regression line added:
    graph twoway lfit vy vx || scatter vy vx
  4. Compute residuals, create new variable residuals:
    predict residuals, resid
  5. Produce a residual plot with horizontal line at 0:
    graph residuals, yline(0)
  6. Identify points with largest and smallest residuals:
    sort residuals
    list in 1/5
    list in -5/l
    (The last command is "minus 5" / "lowercase letter L".)
  7. Compute multiple regression equation (vy is response, vthree, vtwo, and vvthree are explanatory variables):
    regress vy vone vtwo vthree

Important Notes on the "stem" command

In some versions of Stata, there is a potential glitch with Stata's stem command for stem-and-leaf plots. The stem function seems to permanently reorder the data so that they are sorted according to the variable that the stem-and-leaf plot was plotted for. The best way to avoid this problem is to avoid doing any stem-and-leaf plots (do histograms instead). However, if you really want to do a stem-and-leaf plot you should always create a variable containing the original observation numbers (called index, for example). A command to do so is:
generate index = _n

If you do this, then you can re-sort the data after the stem-and-leaf plot according to the index variable:
sort index.
Then, the data are back in the original order.

Summary of These and Other Commands

Here is a list of the commands demonstrated above and some other commands that you may find useful (this is by no means an exhaustive list of all Stata commands):

anova general ANOVA, ANCOVA, or regression
by repeat operation for categories of a variable
ci confidence intervals for means
clear clears previous dataset out of memory
correlate correlation between variables
describe briefly describes the data (# of obs, variable names, etc.)
diagplot distribution diagnostic plots
drop eliminate variables from memory
edit better alternative to input for Macs
exit leave Stata
generate creates new variables (e.g., generate years = last - first)
graph general graphing command (this command has many options)
help online help
histogram create a histogram graphic
if lets you select a subset of observations (e.g., list if radius >= 3000)
infile read non-Stata-format dataset (ASCII or text file)
input type in raw data
insheet read non-Stata-format spreadsheet with variable names on first line
list lists the whole dataset in memory (you can also list only certain variables)
log save or print Stata ouput (except graphs)
lookup keyword search of commands, often precursor to help
oneway oneway analysis of variance
pcorr partial correlation coefficients
plot text-mode (crude) scatterplots
predict calculated predicted values (y-hat), residuals (ordinary, standardized and studentized), leverages, Cook's distance, standard error of predicted individual y, standard error of predicted mean y, standard error of residual from regression
qnorm create a normal quantile plot
regress regression
replace lets you change individual values of a variable
save saves data and labels in a Stata-format dataset
scatter create a scatter plot of two numerical variables
set set Stata system parameters (e.g., obs and seed)
sebarr standard error-bar chart
sort sorts observations from smallest to largest
stem stem and leaf display
summarize produces summary statistics (# obs, mean, sd, min, max) (has a detail option)
tabstat produces summary statistics of your choice
tabulate produces counts/frequencies for categorical data
test conducts various hypothesis tests (refers back to most recent model fit (e.g., regress or anova ) (see help function for info and examples))
ttest one and two-sample t-tests
use retrieve previously saved Stata dataset