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The comprehensive guide to Stata! Continuously updated.
An excellent training about Economics
The Essential Guide to Stata
The Essential Guide to Data Analytics with StataLearning 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 StataViewing and exploring dataManipulating dataVisualising dataCorrelation and ANOVARegression including diagnostics (Ordinary Least Squares)Regression model buildingHypothesis testingBinary outcome models (Logit and Probit)Fractional response modelsCategorical choice models (Ordered Logit and Multinomial Logit)Simulation techniquesCount data modelsSurvival data modelsPanel data analysisDifference-in-Differences modelsPower analysis
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