1.1.1 The Stata Interface
When Stata starts up you see five docked windows, initially arranged as shown below:
The window labeled Command is where you type your commands. Stata then shows the results in the larger window immediately above, called appropriately enough Results. Your command is added to a list in the window labeled Review on the left, so you can keep track of the commands you have used. The window labeled Variables, on the top right, lists the variables in your dataset. The Properties window immediately below that, introduced in version 12, displays properties of your variables and dataset.
You can resize or even close some of these windows. Stata remembers its settings the next time it runs. You can also save (and then load) named preference sets using the menu Edit|Preferences. I happen to like the Compact Window Layout. You can also choose the font used in each window; just right click and select font from the context menu; my own favorite, Lucida Console, is now the default in Windows. Finally, it is possible to change the color scheme, selecting from seven preset or three customizable styles. One of the preset schemes is classic, the traditional black background used in earlier versions of Stata.
There are other windows that we will discuss as needed, namely the Graph, Viewer, Variables Manager, Data Editor, and Do file Editor.
Starting with version 8 Stata's graphical user interface (GUI) allows selecting commands and options from a menu and dialog system. However, I strongly recommend using the command language as a way to ensure reproducibility of your results. In fact, I recommend that you type your commands on a separate file, called a do file, as explained in Section 1.2below, but for now we will just type in the command window. The GUI can be helpful when you are starting to learn Stata, particularly because after you point and click on the menus and dialogs, Stata types the corresponding command for you.
1.1.2 Typing Commands
Stata can work as a calculator using the
display command. Try typing the following (excluding the dot at the start of a line, which is how Stata marks the lines you type):. display 2+2 4 . display 2 * ttail(20,2.1) .04861759
Stata commands are case-sensitive,
display is not the same as Display and the latter will not work. Commands can also be abbreviated; the documentation and online help underlines the shortest legal abbreviation of each command and we will do the same here.
The second command shows the use of a built-in function to compute a p-value, in this case twice the probability that a Student's t with 20 d.f. exceeds 2.1. This result would just make the 5% cutoff. To find the two-tailed 5% critical value try
display invttail(20, 0.025). We list a few other functions you can use in Section 2.
If you issue a command and discover that it doesn't work press the Page Up key to recall it (you can cycle through your command history using the Page Up and Page Down keys) and then edit it using the arrow, insert and delete keys, which work exactly as you would expect. For example Arrows advance a character at a time and Ctrl-Arrows advance a word at a time. Shift-Arrows select a character at a time and Shift-Ctrl-Arrows select a word at a time, which you can then delete or replace. A command can be as long as needed (up to some 64k characters); in an interactive session you just keep on typing and the command window will wrap and scroll as needed.
1.1.3 Getting Help
Stata has excellent online help. To obtain help on a command (or function) type
help command_name, which displays the help on a separate window called the Viewer. (You can also type chelp command_name, which shows the help on theResults window; but this is not recommended.) Or just select Help|Command on the menu system. Try help ttail. (Unfortunately, versions 9 and later open a new viewer each time you type help, and before you know it you have dozens of windows cluttering your desktop. To avoid this problem type the help command on the viewer itself, or type , nonew at the end of the help command to instruct it not to open a new window.)
If you don't know the name of the command you need you can search for it. Stata has a
search command with a few options, type help search to learn more. Version 13 searches the web by default. If you are using an earlier version of Stata learn about the findit command. Try search Student's t. This will list all Stata commands and functions related to the t distribution. One of the entries is "density functions", which takes you to a table with a list of probability distribution and density functions, including "Student's t and noncentral Student's t distributions", which takes you to a list of functions, which includes ttail(). Along the way you see that Stata can also compute tail probabilities for the normal, chi-squared and F distributions, among others.
One of the nicest features of Stata is that, starting with version 11, all the documentation is available in PDF files. (In fact it looks as if in version 13 you can no longer get printed manuals.) Moreover, these files are linked from the online help, so you can jump directly to the relevant section of the manual. To learn more about the help system type
help help.1.1.4 Loading a Sample Data File
Stata comes with a few sample data files. You will learn how to read your own data into Stata in Section 2, but for now we will load one of the sample files, namely
lifeexp.dta, which has data on life expectancy and gross national product (GNP) per capita in 1998 for 68 countries. To see a list of the files shipped with Stata type sysuse dir. To load the file we want type sysuse lifeexp (the file extension is optional). To see what's in the file type describe. (This command can be abbreviated to a single letter but I prefer desc.). sysuse lifeexp
(Life expectancy, 1998)
. desc
Contains data from C:\Program Files (x86)\Stata13\ado\base/l/lifeexp.dta
obs: 68 Life expectancy, 1998
vars: 6 26 Mar 2011 09:40
size: 2,652 (_dta has notes)
-------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------------------------
region byte %12.0g region Region
country str28 %28s Country
popgrowth float %9.0g * Avg. annual % growth
lexp byte %9.0g * Life expectancy at birth
gnppc float %9.0g * GNP per capita
safewater byte %9.0g *
* indicated variables have notes
-------------------------------------------------------------------------------------------------
Sorted by:
We see that we have six variables. The dataset has notes that you can see by typing
notes. Four of the variables have annotations that you can see by typing notes varname. You'll learn how to add notes in Section 2.1.1.5 Descriptive Statistics
Let us run simple descriptive statistics for the two variables we are interested in, using the
summarize command followed by the names of the variables (which can be omitted to summarize everything):. summarize lexp gnppc
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
lexp | 68 72.27941 4.715315 54 79
gnppc | 63 8674.857 10634.68 370 39980
We see that live expectancy averages 72.3 years and GNP per capita ranges from $370 to $39,980 with an average of $8,675. We also see that Stata reports only 63 observations on GNP per capita, so we must have some missing values. Let us
list the countries for which we are missing GNP per capita:. list country gnppc if missing(gnppc)
+--------------------------------------+
| country gnppc |
|--------------------------------------|
7. | Bosnia and Herzegovina . |
40. | Turkmenistan . |
44. | Yugoslavia, FR (Serb./Mont.) . |
46. | Cuba . |
56. | Puerto Rico . |
+--------------------------------------+
We see that we have indeed five missing values. This example illustrates a powerful feature of Stata: the action of any command can be restricted to a subset of the data. If we had typed
list country gnppc we would have listed these variables for all 68 countries. Adding the condition if missing(gnppc) restricts the list to cases where gnppc is missing. Note that Stata lists missing values using a dot. We'll learn more about missing values in Section 2.1.1.6 Drawing a Scatterplot
To see how life expectancy varies with GNP per capita we will draw a scatter plot using the
graph command, which has a myriad of subcommands and options, some of which we describe in Section 3.. graph twoway scatter lexp gnppc
The plot shows a curvilinear relationship between GNP per capita and life expectancy. We will see if the relationship can be linearized by taking the log of GNP per capita.
1.1.7 Computing New Variables
We compute a new variable using the
generate command with a new variable name and an arithmetic expression. Choosing good variable names is important. When computing logs I usually just prefix the old variable name with 'log' or 'l', but compound names can easily become cryptic and hard-to-read. Some programmers separate words using an underscore, as in log_gnp_pc, and others prefer the camel-casing convention which capitalizes each word after the first: logGnpPc. I suggest you develop a consistent style and stick to it. Variable labels can also help, as described in Section 2.
To compute natural logs we use the built-in function
log:. gen loggnppc = log(gnppc) (5 missing values generated)
Stata says it has generated five missing values. These correspond to the five countries for which we were missing GNP per capita. Try to confirm this statement using the list command. We will learn more about generating new variables in Section 2.
1.1.8 Simple Linear Regression
We are now ready to run a linear regression of life expectancy on log GNP per capita. We will use the
regress command, which lists the outcome followed by the predictors (here just one, loggnppc). regress lexp loggnppc
Source | SS df MS Number of obs = 63
-------------+------------------------------ F( 1, 61) = 97.09
Model | 873.264865 1 873.264865 Prob > F = 0.0000
Residual | 548.671643 61 8.99461709 R-squared = 0.6141
-------------+------------------------------ Adj R-squared = 0.6078
Total | 1421.93651 62 22.9344598 Root MSE = 2.9991
------------------------------------------------------------------------------
lexp | Coef. Std. Err. t p>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggnppc | 2.768349 .2809566 9.85 0.000 2.206542 3.330157
_cons | 49.41502 2.348494 21.04 0.000 44.71892 54.11113
------------------------------------------------------------------------------
Note that the regression is based on only 63 observations. Stata omits observations that are missing the outcome or one of the predictors. The log of GNP per capita "explains" 61% of the variation in life expectancy in these countries. We also see that a one percent increase in GNP per capita is associated with an increase of 0.0277 years in life expectancy. (To see this point note that if GNP increases by one percent its log increases by 0.01.)
Following a regression (or in fact any estimation command) you can retype the command with no arguments to see the results again. Try typing
reg.1.1.9 Post-Estimation Commands
Stata has a number of post-estimation commands that build on the results of a model fit. A useful command is
predict, which can be used to generate fitted values or residuals following a regression. The command. predict plexp (option xb assumed; fitted values) (5 missing values generated)
generates a new variable,
plexp, that has the life expectancy predicted from our regression equation. No predictions are made for the five countries without GNP per capita. (If life expectancy was missing for a country it would be excluded from the regression, but a prediction would be made for it. This technique can be used to fill-in missing values.)1.1.10 Plotting the Data and a Linear Fit
A common task is to superimpose a regression line on a scatter plot to inspect the quality of the fit. We could do this using the predictions we stored in
plexp, but Stata's graph command knows how to do linear fits on the fly using the lfit plot type, and can superimpose different types of twoway plots, as explained in more detail in Section 3. Try the command. graph twoway (scatter lexp loggnppc) (lfit lexp loggnppc)
In this command each expression in parenthesis is a separate two-way plot to be overlayed in the same graph. The fit looks reasonably good, except for a possible outlier.
1.1.11 Listing Selected Observations
It's hard not to notice the country on the bottom left of the graph, which has much lower life expectancy than one would expect, even given its low GNP per capita. To find which country it is we list the (names of the) countries where life expectancy is less than 55:
. list country lexp plexp if lexp < 55, clean
country lexp plexp
50. Haiti 54 66.06985
We find that the outlier is Haiti, with a life expectancy 12 years less than one would expect given its GNP per capita. (The keyword
clean after the comma is an option which omits the borders on the listing. Many Stata commands have options, and these are always specified after a comma.) If you are curious where the United States is try. list gnppc loggnppc lexp plexp if country == "United States", clean
gnppc loggnppc lexp plexp
58. 29240 10.28329 77 77.88277
Here we restricted the listing to cases where the value of the variable
country was "United States". Note the use of a double equal sign in a logical expression. In Stata x = 2 assigns the value 2 to the variable x, whereas x == 2 checks to see if the value of x is 2.1.1.12 Saving your Work and Exiting Stata
To exit Stata you use the
exit command (or select File|Exit in the menu, or press Alt-F4, as in most Windows programs). If you have been following along this tutorial by typing the commands and try to exit Stata will refuse, saying "no; data in memory would be lost". This happens because we have added a new variable that is not part of the original dataset, and it hasn't been saved. As you can see, Stata is very careful to ensure we don't loose our work.
If you don't care about saving anything you can type
exit, clear, which tells Stata to quit no matter what. Alternatively, you can save the data to disk using the save filename command, and then exit. A cautious programmer will always save a modified file using a new name.
Source: http://data.princeton.edu/stata/
