Introduction to Stata 15

Introduction

• Why you should use Stata
• Prerequisites
• How this course is taught

1. Getting Started

• An overview of the interface
• Customizing your preferences
• Using help effectively
• Command syntax
• What are .do and .ado files?
• Log files
• Importing data

2. Exploring Data

• Viewing raw data
• Describing and summarizing
• Tabulating and tables
• Missing values
• Distributional analysis (numerical)
• Weights
• Exploring data: Challenge
• Exploring data: Solution

3. Manipulating Data

• Recoding an existing variable
• Generating a new variable
• Naming and labeling variables
• Extended generate
• Indicator variables
• Keeping and dropping variables
• Saving data
• Merging and appending
• String variables
• Local macros and looping
• Manipulating data: Challenge
• Manipulating data: Solution

4. Graphing in Stata

• Introduction to graph commands
• Bar graphs and dot charts
• Distributional analysis (graphical)
• Pie charts
• Scatterplots and fitted lines
• Contour plots
• Geographic maps
• Graphing in Stata: Challenge
• Graphing in Stata: Solution

5. Basic Inferential Statistics

• Statistics for two categorical variables
• Tests for one or two means
• Bivariate correlation and regression
• Analysis of variance
• Basic inferential statistics: Challenge
• Basic inferential statistics: Solution

6. Ordinary Least Squares (OLS) Regression

• OLS regression and interpretation
• Categorical explanatory variables in OLS
• OLS regression diagnostics
• Exploring functional form in OLS regression
• OLS hypothesis testing
• Presenting OLS regression estimates
• Ordinary least squares regression: Challenge
• Ordinary least squares regression: Solution

7. Binary Outcome Models (Logit and Probit)

• The linear probability, logit, and probit models
• Diagnostics
• Interpretation of coefficients and margins
• Binary outcome models: Challenge
• Binary outcome models: Solution

8. Categorical Choice Models

• Ordered logit and ordered probit
• Multinomial logit
• Categorical choice models: Challenge
• Categorical choice models: Solution

Conclusion

• Next steps