Online course with on-demand video and live Zoom meetings: Data Exploration, Regression, GLM & GAM with an introduction to R
This online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of multiple video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. All video files are on-demand and can be watched online, as often as you want, at any time of the day, within a 12 month period.
A discussion board allows for daily interaction between instructors and participants. The course also contains multiple live web meetings in which we summarise the exercises (and some of the theory). You are invited to apply the statistical techniques discussed during the course on your own data and if you encounter any problems, you can ask questions on the Discussion Board. The course fee includes a 1-hour face-to-face video chat with the instructors.
A detailed outline of the course is provided below. All exercises consist of a data set, R solution code, and a 20-60 minutes video describing the data, the questions, and a detailed discussion of the R solution code.
Module 1: Data exploration.
- A video with a general introduction.
- A video with a theory presentation on data exploration.
- A video explaining the basics of R (importing data and accessing variables).
- Exercise 1 on data exploration.
- Exercise 2 on data exploration.
Module 2: Bivariate linear regression and multiple linear regression.
- A video explaining bivariate linear regression.
- Exercises 3 and 4 on bivariate linear regression.
- A video explaining how to use categorical covariates in a regression model.
- A video explaining multiple linear regression.
- Exercise 5 on multiple linear regression.
Module 3: Multiple linear regression (continued).
- Exercise 6 on multiple linear regression.
- A video explaining interactions in multiple linear regression models.
- Exercises 7 and 8 on multiple linear regression models with interactions.
Module 4: Generalised linear models.
- A video with a theory presentation of the Poisson GLM for the analysis of count data.
- Exercise 9 on Poisson GLM.
- A video with a theory presentation on negative binomial GLM.
- Exercise 10 on negative binomial GLM.
- A video explaining Bernoulli and binomial GLMs.
- Exercise 11 on Bernoulli GLM.
- Exercise 12 on binomial GLM.
Module 5: Generalised additive models (GAM).
- A video explaining GAM.
- Exercise 13 showing the application of a Gaussian GAM.
- Exercise 14 showing the application of a Poisson GAM.
- Exercise 15 showing the application of a negative binomial GAM.
- Exercise 16 showing the application of a Bernoulli GAM.
- A video showing what we recommend that you write in a paper.
Free 1-hour face-to-face video meeting: The course fee includes a 1-hour face-to-face meeting with one or both instructors. The meeting needs to take place within 12 months after the last live zoom meeting. You can discuss your own data but the statistical topics need to be within the content of the course. The 1-hour needs to be consumed in one session and will take place at a mutually convenient time.
Web meetings: Web meetings are hosted on zoom.us. Click here for recommended internet speed (see the text under 'Recommended bandwidth for Webinar Attendees'). We will record the meetings and make them available on the course website.
Discussion Board: You can use the Discussion Board to ask any questions related to the course material.
Pre-required knowledge: Basic statistics (e.g. mean, variance, normality). No R knowledge is required. You will learn R ‘on the fly’. This is a non-technical course.
Cancellation policy: What if you are not able to participate? Once participants are given access to course exercises with R solution codes, pdf files of certain book chapters, pdf files of PowerPoint or Prezi presentations and video solution files, all course fees are non-refundable and non-transferable to another participant.
Copyright: Sharing the access details of the course website or the pdf files of our course material is prohibited. Video files cannot be downloaded, but they can be watched in the same way as on Netflix.