# Introduction to Quantitative Analysis Using STATA

## Course Overview

This two day programme provides a comprehensive introduction to STATA and enables participants to practice application of the issues covered. As a result, delegates will understand (i) the theory behind particular quantitative (mainly econometric) techniques and (ii) how to physically code these into STATA and understand its outputs.

Two days

£745 + VAT

### Summary Summary

This two day programme provides a comprehensive introduction to STATA and enables participants to practice application of the issues covered. As a result, delegates will understand (i) the theory behind particular quantitative (mainly econometric) techniques and (ii) how to physically code these into STATA and understand its outputs.

Central London

### Course Details Info

 1. Introduction: Don’t skip the descriptives 2. STATA Introduction: Setting up STATA, loading data, browsing and modifying data, .do files, .ado files and .log files ·      Customising STATA ·      Using help ·      Updating STATA ·      Using the log function and .do files ·      STATA journal & Google   Break at appropriate point 3. STATA Data Management: Working with data in STATA: Organising and working with data ·      Taking a first look and the basics ·      Data management ·      Data transformations Lunch 4. STATA Descriptives Statistics: Getting to know the data. Finding problems beforehand. Survey data and weights ·      Descriptive statistics ·      Visual inspection of data (graphing) ·      Using weights 5. Lecture: Introducing the two-variable regression model and Ordinary Least Squares Break at appropriate point 6. STATA Regression: OLS in STATA with diagnostics. Common problems in OLS ·      OLS and interpretation ·      Diagnostics ·      Functional Form (log-linear models) ·      Heteroskedasticity and multicollinearity ·      Clustering
 7. Introduction: Omitted variable bias and Instrumental Variable (IV) methods 8. STATA IV Estimation: Omitted variable bias and Instrumental Variable methods ·      Simulating omitted variable bias ·      Using IV methods ·      Diagnostics   Break at appropriate point 9. Lecture: Categorical choice methods Lunch 10. SATA Categorical Choice Models: Binary outcome models, Ordered choice models and Multinomial models ·      Logit / Probit ·      Interpretation of coefficients ·      Diagnostics ·      Ordered logit / probit and Multinomial logit ·      Parallel regression assumption and IIA   Break at appropriate point 11. Individual Requests: Individuals are requested to bring details of their own projects to the sessions, and this final section will be explicitly given to provide advice and guidance