Introduction to Stata

Course details

Learning and applying new statistical techniques can often be a daunting experience. Software like Stata, an integrated statistical software package, can help. Stata is agile and easy to use, automate, and extend, helping you perform data manipulation, visualization, and modeling for extremely large data sets. In this course, Franz Buscha provides a comprehensive introduction to Stata and its various uses in modern data analysis. Review the various options that Stata gives you in manipulating, exploring, visualizing, and modelling complex types of data. Explore the practical application—and interpretation—of commonly used statistical techniques such as distributional analysis and regression on real-life data. Each lesson demonstrate the strengths of the application and gives you a firm foundation for performing your own quantitative analysis.

Note: This course was recorded using version 15 of Stata, but all coverage is accurate and up to date for versions 15 and 16.

Learning objectives
Stata command syntax
Importing data
Describing and summarizing data
Conducting distributional analysis
Working with variables
Manipulating data
Graphing
Performing inferential statistics
Ordinary least squares regression
Binary outcome models
Categorical choice models

Concepts

0. Introduction

01 - Why you should use Stata
02 - Prerequisites
03 - How this course is taught

1. Getting Started

04 - An overview of the interface
05 - Customizing your preferences
06 - Using help effectively
07 - Command syntax
08 - What are .do and .ado files
09 - Log files
10 - Importing data

2. Exploring Data

11 - Viewing raw data
12 - Describing and summarizing
13 - Tabulating and tables
14 - Missing values
15 - Distributional analysis (numerical)
16 - Weights
17 - Exploring data - Challenge
18 - Exploring data - Solution

3. Manipulating Data

19 - Recoding an existing variable
20 - Generating a new variable
21 - Naming and labeling variables
22 - Extended generate
23 - Indicator variables
24 - Keeping and dropping variables
25 - Saving data
26 - Merging and appending
27 - String variables
28 - Local macros and looping
29 - Manipulating data - Challenge
30 - Manipulating data - Solution

4. Graphing in Stata

31 - Introduction to graph commands
32 - Bar graphs and dot charts
33 - Distributional analysis (graphical)
34 - Pie charts
35 - Scatterplots and fitted lines
36 - Contour plots
37 - Geographic maps
38 - Graphing in Stata - Challenge
39 - Graphing in Stata - Solution

5. Basic Inferential Statistics

40 - Statistics for two categorical variables
41 - Tests for one or two means
42 - Bivariate correlation and regression
43 - Analysis of variance
44 - Basic inferential statistics - Challenge
45 - Basic inferential statistics - Solution

6. Ordinary Least Squares (OLS) Regression

46 - OLS regression and interpretation
47 - Categorical explanatory variables in OLS
48 - OLS regression diagnostics
49 - Exploring functional form in OLS regression
50 - OLS hypothesis testing
51 - Presenting OLS regression estimates
52 - Ordinary least squares regression - Challenge
53 - Ordinary least squares regression - Solution

7. Binary Outcome Models (Logit and Probit)

54 - The linear probability, logit, and probit models
55 - Diagnostics
56 - Interpretation of coefficients and margins
57 - Binary outcome models - Challenge
58 - Binary outcome models - Solution

8. Categorical Choice Models

59 - Ordered logit and ordered probit
60 - Multinomial logit
61 - Categorical choice models - Challenge
62 - Categorical choice models - Solution

Conclusion

63 - Next steps