Data Analysis and Regression with Stata

  • Date: 9th - 11th Jul 2021
  • time: 9 AM to 4 PM
  • Language : English
  • Venue: Africa College of Theology (ACT)
    New Life Bible Church, NLBC
    KK15 rd, KICUKIRO
    Kigali, Rwanda.
    Room: 3

Course Description

The aim of this course is to help participants increase their practical skills in using Stata for data analysis and regression.  This course does not teach data analysis and regression (in a theoretical sense), per se, but focuses on how to practically perform data and regression analyses using Stata.  Therefore, participants are expected to have prior theoretical knowledge of statistical methods covering topics such as descriptive and inferential statistics, with the latter including topics such as simple and multiple regression. Prior knowledge of Stata is also needed, though not mandatory. The course will cover several aspects related with importation of data, transformation of data, formulation, estimation and interpretation of a simple or multiple linear model, checking for outliers & influential data points and how to overcome these, performing the necessary diagnostic tests (normality, heteroskedasticity, linearity, model specification, multicollinearity and independence). In addition, the course shall explore regressions with categorical predictors, robust regression methods, constrained linear regressions, regressions with censored/truncated data, regressions with measurement error and multiple equation regression models (i.e. seemingly unrelated regressions and multivariate regression).

Course Outcomes

At the end of this short course, participants will be able to:
  • *  Conduct preliminary data checks (mainly using graphics/plots) for outliers and influential data points and devise solutions;
  • *  Transform data;
  • *  Specify, estimate and interpret a simple linear and/or multiple regression model;
  • *  Diagnostic tests: test the satisfaction of the standard OLS assumptions;
  • *  Specify, estimate and interpret models with categorical predictors and their possible interactions;
  • *  Explore some of the possible solutions for dealing with cases where OLS assumptions are violated: e.g. using robust regression methods

About Instructor

Admin bar avatar Karangwa Mathias

Target Audience

  •   Professionals dealing with research and economic analysis.
  •   Undergraduate and graduate students
  •   Researchers
  •   Data scientists

Course Requirements

  •   Knowledge of statistical methods
  •   Prior knowledge of Stata but not mandatory
  •   Bring a laptop

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