Course: R for Statistical Analysis


  • 03 January 2014
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Aim of Course:

This is a course to "Learn R via your existing knowledge of basic statistics" and does not treat statistical concepts in depth. After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression.

See related course (right) "R Programming - Introduction 1," for an introduction to programming in R.

Course Program:

WEEK 1: The One-Sample T-Test in R

A manual computation
A data vector
The functions: mean(), sd(), (pqrd)qnorm()
Finding confidence intervals
Finding p-values
Issues with data
Using data stored in data frames (attach()/detach(), with())
Missing values
Cleaning up data
EDA graphs
Densityplot() and qqnorm()
The t.test() function
Confidence intervals
The power of a t test

WEEK 2: The Two-Sample T-Tests, the Chi-Square GOF test in R

Tests with two data vectors x, and y
Two independed samples no equal variance assumption
Two independed samples assuming equal variance
Matched samples
Data stored using a factor to label one of two groups; x ~ f;
Boxplots for displaying more than two samples
The chisq.tests
Goodness of fit
Test of homogeneity or independence

WEEK 3: The Simple Linear Regression Model in R

The basics of the Wilkinson-Rogers notation: y ~ x
* y ~ x linear regression
Scatterplots with regression lines
Reading the output of lm()
Confidence intervals for beta_0, beta_1
Tests on beta_0, beta_1
Identifying points in a plot
Diagnostic plots

WEEK 4: Bootstrapping in R, Permutation Tests

An introduction to boostrapping
The sample() function
A bootstrap sample
Forming several bootstrap samples
Aside for loops vs. matrices and speed
Using the bootstrap
An introduction to permuation tests
A permutation test simulation

Please note this is an online course lasting until 31st January 2014.

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