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LinkedIn Learning

Introduction to Stata 15

via LinkedIn Learning

Overview

Syllabus

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

Taught by

Franz Buscha

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