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Topics

The course is orgenized in 4 chapters:

  • A quick start.

  • Basic programing in R.

  • First steps in statistical modeling in R.

  • Selected topics in modeling.

R functions that are used for illustrations include:

  • rnorm()

  • mean()

  • var()

  • median()

  • range()

  • min()

  • max()

  • cor()

  • t.test()

  • hist(x)

  • plot()

  • title()

  • boxplot()

  • aov(),lm(),glm()

  • lm()

  • quantile()

 

The course in slides format

The slides of the course are available online in both PowerPoint and PDF formats.

Quick start

If you are new to R and never used it before, this part of the course will give you a quick overview what to expect from the software. It is a very intuitive part, R knowledge is not required but we assume a basic knowledge in statistics (you need to know what a two sample t-test is…).  Topics that we cover in this chapter include:

  • Sampling from a normal distribution.

  • Working with data: the cars data.

  • Two sample t-test.

  • Basic plots.

Basic programming in R 

In this chapter we discus basic topics in R programming from a user point of view. This part is developed to give you the basic skills that you need for an advanced usage of R.  The topics that we cover in this chapter include:

 

  • Basic programming in R: objects in R

  • Reading external datasets

  • Programming in R: a for loop

  • Programming in R: user functions

  • Application of a for loop: bootstrap.

Modeling 1

The first chapter about statistical modelling presents, at a very basic level, the topics of

  • Simple linear regression.

  • One-way ANOVA.

  • Logistic regression.

The R functions lm(). glm() and aov() are used to fit the models. We assume that you studies a basic course in statistics and you familiar with the three models above.

Modeling 2

The second chapter about statistical modeling presents the topics of

  • Two-way ANOVA.

  • Advance topics about linear regression.

R program for the course

R program that contains the code to produce all the results discussed in the course.

Datasets
External datasets that we use in the course.
Slides for the course in PP

For the slides of the course in PowerPoint format, click on the blue butten below.

 

To download the slides: click on the butten below and then click on "download" butten in the Githab page (at the right side of the screen).

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