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LinkedIn Learning

Advanced and Specialized Statistics with Stata

via LinkedIn Learning

Overview

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Take a deeper dive into Stata, the popular statistics software. Explore advanced and specialized topics, from panel data modeling to interaction effects in regression models.

Syllabus

Introduction
  • Specialized statistics with Stata
  • What you should know
1. More on Data Management
  • Formatting the display of variables
  • Date and time variables
  • Repeating commands by looping over variables
  • Repeating commands by looping over numbers
  • Repeating commands by looping within loops
  • Accessing results saved from Stata commands
  • Challenge: More on data management
  • Solution: More on data management
2. More on Visualization Techniques
  • Changing the look of markers
  • Changing graph colors
  • Graphing by groups
  • Controlling legends
  • Adding text and textboxes
  • Sizing graphs
  • Combining graphs
  • How to use jitter
  • How to draw custom functions
  • Challenge: More on visualization techniques
  • Solution: More on visualization techniques
3. Interaction Effects in Regression Models
  • What is an interaction effect?
  • How to use margins and marginsplot
  • Continuous polynomial interactions
  • Continuous by continuous interactions
  • Categorical by categorical interactions
  • Categorical by linear interactions
  • Challenge: Interaction effects
  • Solution: Interaction effects
4. Panel Data Modeling
  • Setting up panel data
  • Setting up panel data demo
  • Panel data descriptives
  • Panel data descriptives demo
  • Panel data dynamics
  • Panel data dynamics demo
  • Linear panel estimators
  • Linear panel estimators demo
  • Random or fixed effects
  • The Hausman test demo
  • Nonlinear panel data estimators
  • Nonlinear panel data estimators demo
  • Challenge: Panel data modeling
  • Solution: Panel data modeling
5. Random Numbers and Simulation
  • Drawing pseudorandom numbers
  • Data generating process (DGP)
  • Violating estimator assumptions
  • Monte Carlo simulation
  • Challenge: Simulation
  • Solution: Simulation
6. Count Modeling
  • Features of count data
  • Poisson model
  • Negative binomial models
  • Truncated models
  • Zero-inflated models
  • Challenge: Count modeling
  • Solution: Count modeling
7. Survival Analysis
  • What is survival data?
  • Setting up survival data
  • Summary statistics
  • Nonparametric analysis
  • Cox proportional hazards model
  • Diagnostics for Cox models
  • Parametric proportional hazards models
  • Challenge: Survival analysis
  • Solution: Survival analysis
Conclusion
  • Next steps

Taught by

Franz Buscha

Reviews

4.8 rating at LinkedIn Learning based on 111 ratings

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