{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}


STATA_Tutorial_for_Assignment_1+S09 - API-202 A Spring 2009...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
API-202 A Spring 2009 TUTORIAL FOR STATA This tutorial will help you prepare for Part 2 of Assignment 1, and also for using Stata throughout this course. You do not need to submit any output from the tutorial . Please note that this tutorial is a complement to and not a substitute for one of the Stata sessions offered by the teaching fellows. If you have not signed up for a Stata session in the lab, do so as soon as possible! Hands-on practice is the best way to learn Stata. Start up Stata (double click on the Stata icon), and follow through the steps below. Following these steps you will learn how to run some useful commands in Stata, as well as how to produce a log file showing your commands and output. 1. Preliminaries First, load the data. The command is “use <filename>”. Stata datasets have the extension “.dta”, but you don’t have to type the extension. At this point you do not need to worry about how we get data from Excel to Stata since we will provide data in Stata format for you. You need to tell Stata in which directory your files are. For the purposes of this exercise, we will assume that you will use "m:\api-202 ” as your directory and that you have saved the data set gender2009.dta in this directory. Type the following command on the command line and then press enter. use "m:\api-202\gender2009.dta" Unlike Excel, you do not see the individual data. In Stata, you think about the data as variables and observations, not as individual cells. To see a list of variables in the data set , type “describe”. describe Contains data from gender2009.dta obs: 950 vars: 6 26 Jan 2009 21:18 size: 15,200 (98.5% of memory free) -------------------------------------------------------------------------------------- -------------------------------------- storage display value variable name type format label variable label -------------------------------------------------------------------------------------- -------------------------------------- age byte %9.0g age in years salary float %9.0f yearly salary hours byte %9.0g usual weekly hours worked weeks byte %9.0g weeks worked last year educ byte %9.0g years of education gender float %9.0g mf gender, =1 if male, =0 if female -------------------------------------------------------------------------------------- -------------------------------------- Sorted by: The dataset has six variables, named age, salary, hours, weeks, educ, and gender. The other columns describe the formatting of the variables. Don’t worry about those for now.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2. Descriptive Statistics To get a summary of the data, we use the “summarize <varname>” command. summarize salary Variable | Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- salary | 950 23834.72 21200.48 30 169999 Note that we can abbreviate “summarize” as “sum” (and in general can abbreviate most commands by their first few letters).
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}