Use of Software

This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course begins with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis.

TStat’s “Maximising the Potential of Stata’s New Python Capabilities” course offers participants an excellent opportunity to acquire the introductory programming skills required to integrate Python’s capability into Stata 18. The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. In the closing session a series of practical applications will be discussed in order to highlight WHEN and HOW one should exploit the connectivity between Python and Stata for one’s research.

The objective of TStat Training’s more advanced course is to provide participants with the programming commands and options required to autonomously develop and modify Stata ADO files. The opening session offers a quick overview of the fundamental concepts and commands (macros, vectors, scalers, looping, branching, temporary objects, foreach, forvalues) intrinsic to successful programming development. Session two moves on to illustrate the most effective way to develop a Stata ADO file, introducing participants to more specific programming concepts (such as arguments, local subroutines and the temporary storing of results) and Stata’s programming commands tokenize, macro shift, marksample and markout “byable” and sortpreserve. In section three participants are introduced to Stata’s inbuilt matrix capabilities, before moving on in the final session to developing their own programs for linear and maximum likelihood estimators.

In common with TStat’s course philosophy, each session is composed of both a theoretical component (in which the programming techniques are fully explained via a series of course specific developed examples), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques under the watchful eye of the course tutor.

At the end of the course, it is expected that participants will be able to independently implement both the techniques learnt and personalize the ADO program templates specifically developed during the course in order to enhance the effectiveness of their research.

This course aims to provide participants with the fundamental Stata programming toolkit in order to facilitate, automate, replicate and personalize both data analysis, management and presentation.

“Migrating to Stata Painlessly!” is a reduced version of our “Up and Running in Stata” course. The course covers everything from the very basics, in order to get one up and running in Stata, through to an overview of the Stata commands available for preliminary data analysis, data management, importing and exporting data formats and the creation of graphs in Stata.

Mata is Stata’s powerful extremely FAST built-in matrix compiled programming language, similar to R, Matlab and GAUSS. One of the main drawbacks in learning Mata however, is that the Reference Manual, whilst extremely detailed, offers little advice on how Mata can be actively implemented.

Up and Running in Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner. The course covers everything from the very basics, in order to get one up and running in Stata, to an overview of the available Stata commands for preliminary data analysis, data management, importing and exporting data formats, merging of databases and the creation of graphs in Stata.