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The Essential Guide to Data Analytics with Stata
Learning and applying new statistical techniques can be daunting experience.
This is especially true once one engages with “real life” data sets that do not allow for easy “click-and-go” analysis, but require a deeper level of understanding of programme coding, data manipulation, output interpretation, output formatting and selecting the right kind of analytical methodology.
In this course you will receive a comprehensive introduction to Stata and its various uses in modern data analysis. You will learn to understand the many options that Stata gives you in manipulating, exploring, visualizing and modelling complex types of data. By the end of the course you will feel confident in your ability to engage with Stata and handle complex data analytics. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques without resorting to deep statistical theory or equations.
This course will focus on providing an overview of data analytics using Stata.
No prior engagement with is Stata needed. Some prior statistics knowledge will help but is not necessary.
The course is aimed at anyone interested in data analytics using Stata.
Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.
Some basic quantitative/statistical knowledge will be required; this is not an introduction to statistics course but rather the application and interpretation of such using Stata.
Topics covered include:
Getting started with Stata
Viewing and exploring data
Manipulating data
Visualising data
Correlation and ANOVA
Regression including diagnostics (Ordinary Least Squares)
Regression model building
Hypothesis testing
Binary outcome models (Logit and Probit)
Fractional response models (Fractional Logit and Beta Regression)
Categorical choice models (Ordered Logit and Multinomial Logit)
Simulation techniques (Random Numbers and Simulation)
Count data models (Poisson and Negative Binomial Regression)
Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)
Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)
Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)
Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)
Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-ControlStudies)
Power analysis (Sample Size, Power Size andEffect Size)
Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)
