Introduction to Stata 15

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Free Online Course: Introduction to Stata 15 provided by LinkedIn Learning is a comprehensive online course, which lasts for 4-5 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from LinkedIn Learning. Introduction to Stata 15 is taught by Franz Buscha.

Overview
  • Learn and apply basic statistical techniques using the popular statistics software Stata.

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