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Course Outline

Introduction

Stata and Large-Scale Data Management

  • Overview of the Stata statistical software package
  • Command structure and syntax for Stata operations

R Programming Language

  • Overview of the R programming environment
  • Syntax conventions and structural framework in R

Establishment of the Analytical Environment

  • Installation and configuration procedures for Stata
  • Deployment of required R libraries and frameworks for government

Integration of R and Stata

  • Data import and export protocols between R and Stata formats

Database Management and Data Handling in Stata

  • Procedures for loading and clearing data sessions
  • Compression techniques for dataset files
  • Methods for importing and exporting database records
  • Review, description, and summary of raw data elements
  • Application of tabulations and structured tables
  • Deployment of variables for data manipulation tasks

Descriptive and Predictive Analytical Methods

  • Execution of distributional analysis
  • Implementation of Monte Carlo simulation techniques
  • Analysis of count-based data sets
  • Application of survival analysis models

Hypothesis Testing Procedures

  • Statistical testing and comparison of mean values

Data Visualization in Stata

  • Utilization of plots, charts, and graphical outputs
  • Integration of statistical metrics within visualizations
  • Formatting and composition of composite graphs

Regression Modeling in R

  • Application of bivariate correlation and regression techniques
  • Implementation of Ordinary Least Squares (OLS), logistic, and probit models
  • Analysis of interaction effects within regression frameworks

Summary and Concluding Remarks

Requirements

  • Proficiency in data analysis methodologies

Target Audience

  • Data Analysts providing support for government operations
 35 Hours

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