Introduction to STATA

1
Introduction to STATA

• About STATA
• Basic Operations
• Regression Analysis
• Panel Data Analysis

2
About

• STATA is modern and general command driven
  package for statistical analyses, data management
  and graphics.
• STATA provides commands to analyze panel data
  (cross-sectional time-series, longitudinal,
  repeated-measures, and correlated data),
  cross-sectional data, time-series data,
  survival-time data, cohort study,
• STATA is user friendly.
• STATA has an extraordinary set of reference
  books.
• STATA has internet capabilities (installing new
  features, updating)

3
Getting ready

• Download statadata.zip from Econ 511 website
• Unzip file statadata.zip to U\stata

4
Basic Operations

• Entering Data
• Exploring Data
• Modifying Data
• Managing Data
• Analyzing Data

5
Entering Data

• Insheet Read ASCII (text) data created by a
  spreadsheet (.csv files only)
• Infile Read unformatted ASCII (text) data (space
  delimited files)
• Input Enter data from keyboard
• Describe Describe contents of data in memory or
  on disk
• Compress Compress data in memory
• Save Store the dataset currently in memory on
  disk in Stata data format
• Count Show the number of observations
• List List values of variables
• Clear Clear the entire dataset and everything
  else
• Memory Display a report on memory usage
• Set memory Set the size of memory

6
Example

• cd u\stata
• dir
• insheet using hs0.csv (If file has variable name
  on the first line)
• Save hs
• insheet gender id race ses schtyp prgtype read
  write math science socst using hs0_noname.csv,
  clear(If file doesnt have variable name on the
  first line)
• Count
• Describe
• Compress
• Clear
• use hs, clear (only for files in Stata files, can
  be use over internet)
• Memory
• set memory 5m (maximum 256MB)

7
Exploring data

• Describe Describe a dataset
• List List the contents of a dataset
• Codebook Detailed contents of a dataset
• Log Create a log file
• Summarize Descriptive statistics
• Tabstat Table of descriptive statistics
• Table Create a table of statistics
• Stem Stem-and-leaf plot
• Graph High resolution graphs
• Kdensity Kernal density plot
• Sort Sort observations in a dataset
• Histogram Histogram for continuous and
  categorical variables
• Tabulate One- and two-way frequency tables
• Correlate Correlations
• Pwcorr Pairwise correlations
• Type Display an ASCII file

8
Example

• use hs0, clear
• Describe
• List
• list gender-read
• Codebook
• log using unit1, text replace (open a existing
  log file called unit1 which will save all of the
  commands and the output in a text file and delete
  the contents and places the current log into the
  file
• summarize
• summarize read math science write
• display 9.482 (note variance is the sd (9.48)
  squared)
• summarize write
• detail sum write if readgt60
• sum write if prgtype"academic
• sum write in 1/40
• tabulate prgtype, summarize(read)
• stem write
• graph box write
• log close (close the log file)
• type unit1.log (see what is in the log file)

9
Modifying Data

• label data Apply a label to a data set
• Order Order the variables in a data set
• label variable Apply a label to a variable
• label define Define a set of a labels for the
  levels of a categorical variable
• label values Apply value labels to a variable
• List Lists the observations
• Rename Rename a variable
• Recode Recode the values of a variable
• Notes Apply notes to the data file
• Generate Creates a new variable
• Replace Replaces one value with another value
• Egen Extended generate - has special functions
  that can be used when creating a new variable

10
Example

• Use hs0
• Order id gender
• label variable schtyp "The type of school the
  student attended."
• label define scl 1 public 2 private
• label values schtyp scl
• codebook schtyp
• list schtyp in 1/10
• list schtyp in 1/10, nolabel
• encode prgtype, gen(prog) (create a new numeric
  version of the string variable prgtype)
• label variable prog "The type of program in which
  the student was enrolled."
• codebook prog
• list prog in 1/10
• list prog in 1/10, nolabel

11
Example (cont)

• rename gender female (easier to work with since
  we dont have to deal with 0s and 1s)
• label variable female "The gender of the
  student."
• label define fm 1 female 0 male
• label values female fm
• codebook female
• list female in 1/10, nolabel
• Gen total read write math
• replace total read write socst
• label variable total "The total of the read,
  write and socst."
• list race if race 5
• recode race 5 .
• list race if race .
• generate total read write math
• sum total
• Codebook total
• notes race values of race coded as 5 were
  recoded to be missing
• egen zread std(read) (using special function
  std(.))
• save hs1

12
Managing Data

• Pwd Show current directory (pwdprint working
  directory)
• dir or ls Show files in current directory
• cd Change directory
• keep if Keep observations if condition is met
• Keep Keep variables (dropping others)
• Drop Drop variables (keeping others)
• append using Append a data file to current file
• Merge Merge a data file with current file

13
Example

• We take the hs1 data file and make a separate
  folder called honors and store a copy of our data
  which just has the students with reading scores
  of 60 or higher
• use hs1, clear
• Pwd
• Dir
• Ls
• cd honors
• keep if read gt 60
• Describe
• summarize read
• save hsgoodread, replace
• use hsgoodread, clear
• drop ses
• save hsdropped, replace
• describe
• list in 1/20

14
Analyzing Data

• Ttest t-test
• Regress Regression
• Predict Predicts after model estimation
• Kdensity Kernel density estimates and graphs
• Pnorm Graphs a standardized normal plot
• Qnorm Graphs a quantile plot
• Rvfplot Graphs a residual versus fitted plot
• Rvpplot Graphs a residual versus individual
  predictor plot
• Xi Creates dummy variables during model
  estimation
• Test Test linear hypotheses after model
  estimation
• Oneway One-way analysis of variance
• Anova Analysis of variance
• Logistic Logistic regression
• Logit Logistic regression

15
Example

• use hs1, clear
• ttest write 50 (This is the one-sample t-test,
  testing whether the sample of writing scores was
  drawn from a population with a mean of 50 )
• ttest write read (This is the paired t-test,
  testing whether or not the mean of write equals
  the mean of read)
• ttest write, by(female) (This is the two-sample
  independent t-test with pooled (equal) variances)
  
• ttest write, by(female) unequal (This is the
  two-sample independent t-test with separate
  (unequal) variances)
• oneway write prog
• anova write prog (Both of these commands perform
  a one-way analysis of variance (ANOVA)
• anova write prog female progfemale (the anova
  command is used to perform a two-way analysis of
  variance (ANOVA).)
• anova write prog female progfemale read,
  cont(read) (the anova command performs an
  analysis of covariance (ANCOVA))

16
Example (cont)

• regress write read female (Plain vanilla OLS
  regression)
• regress write read female, robust (we run the
  regression with robust standard errors. This is
  very useful when there is heterogeneity of
  variance. This option does not affect the
  estimates of the regression coefficients.)
• predict p (The predict command calculates
  predictions, residuals, influence statistics, and
  the like after an estimation command. The default
  shown here is to calculate the predicted scores)
• predict r, resid (When using the resid option the
  predict command calculates the residual)
• pnorm r ( produces a normal probability plot and
  it is another method of testing whether the
  residuals from the regression are normally
  distributed)
• Rvfplot (generates a plot of the residual versus
  the fitted values it is used after regress or
  anova)
• rvpplot read (produces a plot of the residual
  versus a specified predictor and it is also used
  after regress or anova.

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Example (cont)

• xi regress write read i.prog (The xi prefix is
  used to dummy code categorical variables such as
  prog. The predictor prog has three levels and
  requires two dummy-coded variables)
• test _Iprog_2 _Iprog_3 (The test command is used
  to test the collective effect of the two
  dummy-coded variables in other words, it tests
  the main effect of prog)
• xi regress write i.progread (create dummy
  variables for prog and for the interaction of
  prog and read)
• test _IproXread_2 _IproXread_3 (tests the overall
  interaction)
• test _Iprog_2 _Iprog_3 (tests the main effect of
  prog)
• gen honcomp write gt 60 (create a dichotomous
  variable called honcomp (honors composition) to
  use as our dependent variable)
• tab honcomp
• The logistic command defaults to producing the
  output in odds ratios but can display the
  coefficients if the coef option is used. The
  exact same results can be obtained by using the
  logit command, which produces coefficients as the
  default but will display the odds ratio if the or
  option is used
• logit honcomp read female
• logit honcomp read female, or

18
Logistic Regression

• Classical Regression vs Logistic Regression
• All of the previous regression examples have used
  continuous dependent variables.
• Logistic regression is used when the dependent
  variable is binary or dichotomous.
• Different Assumptions
• The population means of the dependent variables
  at each level of the independent variable are not
  on a straight line, i.e., no linearity.
• The variance of the errors are not constant,
  i.e., no homogeneity of variance.
• The errors are not normally distributed, i.e., no
  normaility.
• Logistic Regression Assumptions
• The model is correctly specified, i.e.,
• the true conditional probabilities are a logistic
  function of the indpendent variables,
• no important variables are omitted,
• no extraneous variables are included, and
• the independent variables are measured without
  error.
• The cases are independent.
• The independent variables are not linear
  combinations of each other. Perfect
  multicolinearity makes estimation impossible,
  while strong multicolinearity makes estimates
  imprecise.

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Logistic Regression - 2

• Logit
• Use admission into a graduate program in which
  70 of the males and 30 of the females are
  admitted -
• Let P equal the probability of being admitted.
• Let Q 1 - P equal the probability of not being
  admitted.
• Let the odds of a male admitted be odds(M) P/Q
  P/1-P .7/.3 2.3333
• Let the odds of a female admitted be odds(F)
  P/Q P/1-P .3/.7 .42857
• Let the odds ration, OR odds(M)/odds(F)
  2.3333/.42857 5.44
• The odds if being admitted to the program are
  about 5.44 times greater for males then for
  females.
• Let logit(P) log(odds) ln(P/Q) ln (P/1 - P)
  
• This results in the logistic regression equation
  logit(P) a bX.
• In effect, this represents a transformation of
  the dependent variable such that the resulting
  logistic regression equation better meets the
  assumptions of linearity, normality and
  homogeneity of variance
• Interpreting logit coefficients
• Logistic slope coefficients can be interpreted as
  the effect of a unit of change in the X variable
  on the predicted logits with the other variables
  in the model held constant. That is, how a one
  unit change in X effects the log of the odds when
  the other variables in the model held constant.
• Interpreting Odds Ratios
• Odds ratios in logistic regression can be
  interpreted as the effect of a one unit of change
  in X in the predicted odds ratio with the other
  variables in the model held constant

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Logistic Regression 3

• Sample data set
• input apt gender admit
• 8 1 1
• 7 1 0
• 5 1 1
• 3 1 0
• 3 1 0
• 5 1 1
• 7 1 1
• 8 1 1
• 5 1 1
• 5 1 1
• 4 0 0
• 7 0 1
• 3 0 1
• 2 0 0
• 4 0 0
• 2 0 0
• 3 0 0

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Logistic Regression 4

• Example 1 Categorical Independent Variable
• logit admit gender
• logistic admit gender
• Example 2 Continuous Independent Variable
• logit admit apt
• logistic admit apt
• Example 3 Categorical Continuous Independent
  Variables
• logit admit gender apt
• logistic admit gender apt
• Example 4 Honors Composition using HSB Dataset
• Use hsb2, clear
• generate honors (writegt60) (create dichotomous
  response variable)
• tabulate ses, generate(ses) (create dummy coding
  for ses)
• logit honors female ses1 ses2 read math
• test ses1 ses2
• logistic honors female ses1 ses2 read math
• lfit (goodness-of-fit test)
• lstat

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Do file

• Do-files are created with the do-file editor or
  any other text editor. Any command which can be
  executed from the command line can be placed in a
  do-file
• To open a do file editor Window Do-file Editor
  or Ctrl 8
• set more off
• use hsb2, clear
• generate lang read write
• label variable lang "language score"
• tabulate lang
• tabulate lang female
• tabulate lang prog
• tabulate lang schtyp
• summarize lang, detail
• table female, contents(n lang mean lang sd lang)
• table prog, contents(n lang mean lang sd lang)
• table ses, contents(n lang mean lang sd lang)
• correlate lang math science socst
• regress lang math science female
• set more on

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Do file cont.

• Look at the commands in a do-file that contains
• . type hsbbatch.do
• To run the do-file.
• do hsbbatch
• From do file, choose Tools - Do

24
Panel Data

• Creat the do file as followed
• set matsize 160
• use http//www.ats.ucla.edu/stat/stata/stat130/dep
  ress, clear
• sort group
• by group summarize pre dep1 dep2 dep3 dep4 dep5
  dep6
• corr pre dep1 dep2 dep3 dep4 dep5 dep6
• graph dep1 dep2 dep3 dep4 dep5 dep6, matrix half
• ttest pre, by(group) / check to see if the
  groups differ on the pretest depression score
• /
• hotel dep1 dep2 dep3 dep4 dep5 dep6,
  by(group)/There isn't much of a difference
  between groups on the pretest so let's try a
  Hotelling's T2
• Using Hotelling's T2 we find a significant
  difference between the two groups. The T2 did not
  make use of any of the information concerning the
  pretest but that's okay for the moment especially
  since we know that the pretest differences were
  not significant./
• reshape long dep, i(subj) j(visit)
• regress dep pre group visit
• glm dep pre group visit, fam(gaus) link(iden)
• xtgee dep pre group visit, fam(gaus) link(iden)
  i(subj) t(visit) corr(ind) /The three previous
  analyses provide identical incorrect results.
• The common thread among them is that they all
  assume that the observations within the subjects
  are independent. This seems, on the face of it,
  to be highly unlikely. Scores on the depression
  scale are not likely to be independent from one
  visit to the next.
• Of the three, only xtgee makes the assumption
  concerning the correlations explicit./
• xtcorr / The xtcorr command shows structure of
  the correlation matrix/
• / xt commands are used with cross-sectional
  time-series data /

25
Panel data 2

• /We can analyze these data using compound
  symmetry for the correlational structure.
• This approach can be tried using exchangable for
  the correlation matrix in xtgee /
• xtgee dep pre group visit, fam(gaus) link(iden)
  i(subj) t(visit) corr(exc)
• xtcorr
• /Note in particular the change in the standard
  errors between this analysis and the previous
  one.
• Now let's try a different correlation structure,
  auto regressive with lag one./
• xtgee dep pre group visit, fam(gaus) link(iden)
  i(subj) t(visit) corr(ar1)
• /back up and reconsider the group by visit
  interaction.
• We will try a model with the interaction using
  the ar1 correlations. /
• generate gxv groupvisit
• xtgee dep pre group visit gxv, fam(gaus)
  link(iden) i(subj) t(visit) corr(ar1)
• / The group by visit interaction still is not
  significant even though this may be a better
  approach for testing it.
• So far we have been treating visit as a
  continuous variable.
• Is it possible that our analysis might change if
  we were to treat visit as a categorical variable,
  the way that the anova did?
• Let's try one last analysis using xi to create
  dummy variables on-the-fly. /
• xi xtgee dep pre group i.visit, fam(gaus)
  link(iden) i(subj) corr(ar1)

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Searching for help

• The help command can be used from the
  command line or from the Help window. To use help
  the command must be spelled correctly and the
  full name of the command must be used. help
  contents will list all commands that can be
  accessed using help
• help if
• help anova
• help regress
• The search command searches for information
  in Stata manuals, FAQs, and Stata Technical
  Bulletins (STBs). The search options include
  manual which restricts searches to the Stata
  Manual author when searching for an author by
  name stb which restricts searhes to STBs faq
  which restricts searches to FAQs.The search
  command can be used from either the command line
  or the Help window.
• search if
• search regression
• search ttest, manual
• Each copy of Stata comes with a built-in
  tutorital. Typing tutorial brings up information
  about the tutorials. tutorial regress will bring
  up the tutorial on regression.
• tutorial
• tutorial regress

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End of Session