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Topics

The slides in this page are organised in three chapters that cover basic topics in statistical modelling using R. The course is focused on the practical aspects of modelling and not only on the theory behind and covers the following topics:

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  • Simple linear regression.

  • One-Way ANOVA.

  • Simple logistic regression.

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All examples are illustrated using the R software.  Useful R functions include:

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  • lm()

  • aov()

  • glm()

 

External datasets for illustration are included in the data repositories.

 

The course in slides format

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We provide two (similar) courses that were developed by the >eR-BioStat team and by Julie Vu and Dave Harrington(https://www.openintro.org/book/biostat/), respectively. Both courses cover the same topics and we provide the course files that were used to produce the slides.  

 

 

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To download the Power Point files for the slides:

 

Click on the button Slides (PP) and then on the button download in GitHub (upper right side).

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Simple Linear Regression
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This course covers the topic of simple linear regression using the R function lm(). Topics (all presented at a basic level) covered in the course include:

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  • Introduction and model formulation.

  • Fitting a simple linear regression model using the lm() function in R.

  • Model diagnostic.

  • Model diagnostic in R.

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External datasets are available in the data repository.

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One-Way ANOVA
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This  course covers the topic of one way ANOVA models using the R function aov(). Topics (all presented at a basic level) covered in the course include:

  • The one-way ANOVA model.

  • Sources of Variability.

  • One-way ANOVA using R: the aov() function.

  • Model formulation and hypotheses testing.

  • Analysis of the pharmaceutical experiment.

  • Model diagnostic in R: normal probability plot.

  • Multiple testing.

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External datasets are available in the data repository.

Recommended reading for the courses in Simple regression and One-Way anova are Chapter 6 and Section 5.5 in the book "Introductory statistics for the life and biomedical sciences", respectively,  available online here:

Simple Logistic 
Regression
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​This course covers the topic of simple logistic regression using the R function glm(). Topics (all presented at a basic level) covered in the course include:

  • Introduction and example tour.

  • Fitting a simple linear logistic regression model using the glm() function in R.

  • Model formulation.

  • Interpretation of the model parameters.

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External datasets are available in the data repository.

Recommended reading for the course in Logistic regression is  Section 9.5 in the book "OpenIntro Statistics"  available online here:

This part of the course is based on unit 6 in Vu & Harrington course and it 
covers the following topics:

 

  • Examining scatterplots.

  •  Least squares regression. 

  • Interpreting a linear model.

  • Statistical inference in regression.

This  part of the course is based on unit 5 in Vu & Harrington course and it covers the following topics:

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  • Ideas behind One-Way ANOVA..

  • Assumptions for ANOVA.

  • Normal probability plots (Q-Q plots).

  • Pairwise comparisons.

  • ANOVA model in R using the aov () function

This  part of the course is based on unit 9 in Vu & Harrington course  and it covers the following topics:

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  • Odds and probabilities.

  • Introduction to logistic regression.

  • Simple logistic regression.

  • Logistic versus linear regression.

  • Inference for simple logistic regression.

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