Statistical Analysis with Stata and R

• An understanding of data analysis

Audience

• Data Analysts

Stata is a general-purpose software package written in C. R is a programming language and software environment for statistical computing. Using Stata and R, users can analyze large data sets for use cases such as economics, sociology, biomedicine, etc.

This instructor-led, live training (online or onsite) is aimed at data analysts who wish to use Stata and R to analyze big data for statistical analysis.

By the end of this training, participants will be able to:

• Create statistic models for predicting key interest variables and events.
• Generate descriptive visualizations, summary tables, frequencies, and more.
• Manage and structure large databases to preapare for data analysis.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

Stata and Big Data

• What is Stata?
• Stata syntax and commands

R Programming

• What is R?
• R syntax and structure

Preparing the Development Environment

• Installing and configuring Stata
• Installing and configuring R libraries and frameworks

R and Stata

• Reading and writing to Stata with R

Databases and Data in Stata

• Opening and clearing databases
• Compressing databases
• Importing and exporting databases
• Viewing, describing, and summarizing raw data
• Using tabulations and tables
• Implementing variables for data manipulation

Descriptive Analysis and Predictive Analysis

• Working with distributional analysis
• Working with Monte Carlo simulations
• Working with count data analysis
• Working with survival analysis

Hypothesis Testing

• Testing and comparing means

Graphing in Stata

• Using plots, charts, and graphs
• Working with statistical analysis in graphing
• Styling and combining graphs

Regression Models with R

• Using bivariate correlation and regression
• Working with OLS regression, logits, and probits
• Using interactive effects in regression models

Summary and Conclusion