An Introduction to Model Building Techniques in Stata

Online 1 day (12th May 2021 - 12th May 2021) Stata Intermediate, Introductory
Delivered by: Robert Grant
Econometrics, Medical statistics, Regression analysis, Statistics

Course Overview

Presented by Robert Grant

In healthcare, economics or commercial data science, analysts of all backgrounds have to build models for their data. Models help us to understand causal relationships and forecast the future, but as statistician George Box put it, "all models are wrong, but some are useful". How do you know if your models are useful? Or maybe even wrong? This one-day course is an introduction to the techniques used in model building.

This course will be delivered online. Participants should have a computer with Stata installed, and should be familiar with writing in the do-file editor up to the level of linear regression and two-way graphics. Some, but not all, of the topics will require version 16. Each topic will be introduced with a short lecture and demonstration, then there will be a solo exercise for you to test out some supplied code and practice writing your own, and then a discussion and Q&A.

This course focusses on data made up of independent observations. Time series, spatial models and panel data are addressed in our Time Series Analysis and Forecasting course.

This course will be taught by Robert Grant, a medical statistician, trainer and coach who has constructed models to predict everything from Arctic rocks to exam marks, from golf scores to recovery from stroke.

Course Timetable

Morning Session Afternoon Session Q&A with Instructor
10am-12pm 2pm-4pm 4pm-4:30pm
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Agenda

In healthcare, economics or commercial data science, analysts of all backgrounds have to build models for their data. Models help us to understand causal relationships and forecast the future, but as statistician George Box put it, "all models are wrong, but some are useful". How do you know if your models are useful? Or maybe even wrong? This one-day course is an introduction to the techniques used in model building covering these topics:

  • supervised or unsupervised learning?
  • exploratory data analysis and visualisation
  • regularisation, LASSO and penalized likelihood
  • stepwise methods
  • soft skills to understand the deliverables and the data sources, and run user testing
  • robust models and justifying assumptions
  • cross-validation
  • monitoring data drift
  • fractional polynomials
  • local-linear regression and LOESS
  • model comparison, information criteria and entropy
  • signpost the other approaches that apply to more structured data like time series, spatial data, panel data, or multimedia

This course will be delivered online. Participants should have a computer with Stata installed, and should be familiar with writing in the do-file editor up to the level of linear regression and two-way graphics. Some, but not all, of the topics will require version 16. Each topic will be introduced with a short lecture and demonstration, then there will be a solo exercise for you to test out some supplied code and practice writing your own, and then a discussion and Q&A.

This course focusses on data made up of independent observations. Time series, spatial models and panel data are addressed in our Time Series Analysis and Forecasting course.

This course will be taught by Robert Grant, a medical statistician, trainer and coach who has constructed models to predict everything from Arctic rocks to exam marks, from golf scores to recovery from stroke.

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers prior to the start of the course.
  • Payment of course fees required prior to the course start date.
  • Registration closes 1 calendar day prior to the start of the course.
    • 100% fee returned for cancellations made more than 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of attendees is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    1-Day Pass (12/05/2021 - 12/05/2021)

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