List of tables
List of figures
Acknowledgments
1 The first time
1.1 Starting Stata
1.2 Setting up your screen
1.3 Your first analysis
1.3.1 Inputting commands
1.3.2 Loading data
1.3.3 Variables and observations
1.3.4 Looking at data
1.3.5 Interrupting a command and repeating a command
1.3.6 The variable list
1.3.7 The in qualifier
1.3.8 Summary statistics
1.3.9 The if qualifier
1.3.10 Defining missing values
1.3.11 The by prefix
1.3.12 Command options
1.3.13 Frequency tables
1.3.14 Graphs
1.3.15 Getting help
Excursus: Large language models as programming help
1.3.16 Recoding variables
1.3.17 Variable labels and value labels
1.3.18 Linear regression
1.4 Do-files
1.5 Dynamic documents
1.6 Exiting Stata
1.7 Around Stata
1.7.1 Resources and information
1.7.2 Updating Stata
1.7.3 Additional procedures
Stata Journal ado-files
Statistical Software Components Archive ado-files
1.8 Exercises
2 Working with do-files
2.1 From interactive work to working with a do-file
2.1.1 Alternative 1
2.1.2 Alternative 2
2.2 Designing do-files
2.2.1 Comments
2.2.2 Line breaks
2.2.3 Some crucial commands
2.3 Organizing your work
2.4 Exercises
3 The grammar of Stata
3.1 The elements of Stata commands
3.1.1 Stata commands
3.1.2 The variable list
List of variables: Required or optional
Abbreviation rules
Special listings
3.1.3 Options
3.1.4 The in qualifier
3.1.5 The if qualifier
3.1.6 Expressions
Operators
Functions
3.1.7 Lists of numbers
3.1.8 Using filenames
3.2 Repeating similar commands
3.2.1 The by prefix
3.2.2 The foreach loop
The types of foreach lists
Several commands within a foreach loop
3.2.3 The forvalues loop
3.3 Weights
Frequency weights
Analytic weights
Sampling weights
3.4 Exercises
4 General comments on the statistical commands
4.1 Regular statistical commands
4.2 Estimation commands
4.3 Integration of other programming languages in Stata
4.4 Export and import from Python
4.4.1 Finding and using the right Python version
4.4.2 Installing Python modules from within Stata
4.4.3 The Stata Python module
4.4.4 Large language models as programming assistant for Python
4.5 Exercises
5 Creating and changing variables
5.1 The commands generate and replace
5.1.1 Variable names
5.1.2 Some examples
5.1.3 Useful functions
5.2 Setting missing values
5.3 Labels
5.4 Specialized recoding commands
5.4.1 The recode command
5.4.2 The egen command
5.4.3 Changing codes with by, __n, and __N
5.4.4 Subscripts
5.5 Recoding string variables
5.6 Recoding date and time
5.4.1 Dates
5.4.2 Time
5.7 Storage types, or the ghost in the machine
5.8 Exercises
6 Creating and changing graphs
6.1 A primer on graph syntax
6.2 Graph types
6.2.1 Examples
6.2.2 Specialized graphs
6.3 Graph elements
6.3.1 Appearance of data
Choice of marker
Marker colors
Marker size
Lines
6.3.2 Graph and plot regions
Graph size
Plot region
Scaling the axes
6.3.3 Information inside the plot region
Reference lines
Labeling inside the plot region
6.3.4 Information outside the plot region
Labeling the axes
Tick lines
Axis titles
The legend
Graph titles
6.4 Multiple graphs
6.4.1 Overlaying many twoway graphs
6.4.2 Option by()
6.4.3 Combining graphs
6.5 Saving and printing graphs
6.6 Graphs with Python (within Stata)
6.7 Large language models as programming help with graphs
6.8 Exercises
7 Describing and comparing distributions
7.1 Categories: Few or many?
7.2 Variables with few categories
7.2.1 Tables
Frequency tables
More than one frequency table
Comparing distributions
Summary statistics
More than one contingency table
7.2.2 Graphs
Histograms
Bar charts
Pie charts
Dot charts
7.3 Variables with many categories
7.3.1 Frequencies of grouped data
Some remarks on grouping data
Special techniques for grouping data
7.3.2 Describing data using statistics
Important summary statistics
The summarize command
The tabstat command
Comparing distributions using statistics
7.3.3 Graphs
Box plots
Histograms
Kernel density estimation
Quantile plot
Comparing distributions with Q–Q plots
7.4 Exercises
8 Statistical inference
8.1 Random samples and sampling distributions
8.1.1 Random numbers
8.1.2 Creating fictitious datasets
8.1.3 Drawing random samples
8.1.4 The sampling distribution
8.2 Descriptive inference
8.2.1 Standard errors for simple random samples
8.2.2 Standard errors for complex samples
Typical forms of complex samples
Sampling distributions for complex samples
Using Stata’s svy commands
8.2.3 Standard errors with nonresponse
Unit nonresponse and poststratification weights
Item nonresponse and multiple imputation
8.2.4 Uses of standard errors
Confidence intervals
Significance tests
Two-group mean comparison test
8.3 Causal inference
8.3.1 Basic concepts
Data-generating processes
Potential outcomes
Controlling for observables
8.3.2 The effect of third-class tickets
8.4 Predictive inference
8.4.1 Quantifying prediction quality
8.4.2 Find the best split
8.4.3 The best split for all variables
8.4.4 Continue learning
8.4.5 Training and test
8.5 Exercises
9 Introduction to linear regression
9.1 Simple linear regression
9.1.1 The basic principle
9.1.2 Linear regression using Stata
The table of coefficients
The table of ANOVA results
The model fit table
9.2 Multiple regression
9.2.1 Multiple regression using Stata
9.2.2 Derived statistics
Adjusted R2
Standardized regression coefficients
9.2.3 Categorical independent variables
9.2.4 Interaction terms
9.2.5 Regression models using transformed variables
9.2.6 Why do we use multiple regression?
Parsimonious description
Adjustment of the substantive interpretation of regression coefficients
Estimation of causal effects
Prediction
9.3 Regression diagnostics
9.3.1 Violation of E(εi) = 0
Linearity
Influential data points
Omitted variables
9.3.2 Violation of Var(εi) = σ2
9.3.3 Violation of Cov(εi, εj) = 0, i ≠ j
9.4 Reporting regression results
9.4.1 Tables of similar regression models
9.4.2 Plots of coefficients
9.4.3 Profile plots
9.5 Advanced models for causal inference
9.5.1 Causal-effect estimation from observational data
9.5.2 Controlling for unobserved confounders using panel data
Between, within, and difference in differences
From wide to long format
Within and DID using first principles
First-difference model
Two-way fixed-effects models
9.5.3 Instrumental-variable regression
9.6 Exercises
10 Regression models for categorical dependent variables
10.1 The linear probability model
10.2 Basic concepts
10.2.1 Odds, log odds, and odds ratios
10.2.2 Excursion: The maximum likelihood principle
10.3 Logistic regression with Stata
10.3.1 The coefficient table
Sign interpretation
Interpretation with odds ratios
Probability interpretation
Average marginal effects
10.3.2 The iteration block
10.3.3 The model fit block
Classification tables
Pearson chi-squared
10.4 Logistic regression diagnostics
10.4.1 Linearity
10.4.2 Influential cases
10.5 Likelihood-ratio test
10.6 Refined models
10.6.1 Nonlinear relationships
10.6.2 Interaction effects
10.7 Related models
10.7.1 Probit models
10.7.2 Multinomial logistic regression
10.7.3 Models for ordinal data
10.8 Exercises
11 Reading and writing data
11.1 The goal: The data matrix
11.2 Importing machine-readable data
11.2.1 Reading system files from other packages
Reading Excel files
Reading SPSS files
Reading SAS files
Reading other system files
11.2.2 Reading text files
Reading data in spreadsheet format
Reading data in free format
Reading data in fixed format
11.3 Inputting data
11.3.1 Input data using the Data Editor
11.3.2 The input command
11.4 Web scraping
11.5 Combining data
11.5.1 The GSOEP database
11.5.2 The merge command
Merge 1:1 matches with rectangular data
Merge 1:1 matches with nonrectangular data
Merging more than two files
Merging m:1 and 1:m matches
11.5.3 The append command
11.5.4 Combining data with frames
Frames as alternative to merge and append
Frames to collect results
11.6 Saving and exporting data
11.7 Unicode
11.7.1 Code tables
11.7.2 Compatibility issues
11.7.3 Babel
11.8 Exercises
12 Do-files for advanced users and user-written programs
12.1 Two examples of usage
12.2 Four programming tools
12.2.1 Local macros
Calculating with local macros
Combining local macros
Changing local macros
12.2.2 Do-files
12.2.3 Programs
The problem of redefinition
The problem of naming
The problem of error checking
12.2.4 Programs in do-files and ado-files
12.3 User-written Stata commands
12.3.1 Sketch of the syntax
12.3.2 Create a first ado-file
12.3.3 Parsing variable lists
12.3.4 Parsing options
12.3.5 Parsing if and in qualifiers
12.3.6 Generating an unknown number of variables
12.3.7 Default values
12.3.8 Extended macro functions
12.3.9 Avoiding changes in the dataset
12.3.10 Help files
12.4 Exercises
References
