R Programming Series:
Statistical Modelling in R
From the very beginning, R was designed for statistical modelling. ThiaLearn advanced graphics packages like ggplot2 to create advanced and informative graphics. This training course stresses understanding – not just one off R scripts. s course covers the fundamental modelling techniques. We begin the day by revising hypotheses tests, before moving on to ANVOA tables and regression analysis. The class ends by looking at more sophisticated methods such as clustering and principal components analysis (PCA).
Throughout the day we'll cover:
- Basic hypothesis testing: examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test
- ANOVA tables: One-way and two-way tables
- Simple and multiple linear regression: Including model diagnostics
- Clustering: Hierarchical clustering, k-means
- Principal components analysis: Plotting and scaling data
By the end of the day participants will be able to:
Gain a thorough understanding of popular statistical techniques
Make appropriate assumptions about the structure of data and check the validity of these assumptions in R
Fit regression models in R between a response variable
Apply techniques to your own data using R's common interface to statistical functions
Cluster data using standard clustering techniques
Next course dates:
9:00am - 5.00pm
(1 hour lunch break)
Why stop there? Extend your R Programming knowledge.
Receive a 10% discount when booking 2 or more courses from the R Course Series (excluding "Intro to R")
DISCOUNTS AVAILABLE FOR:
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