Delve into modern statistical analysis with R. Gain insights into data description, linear modeling, advanced techniques, and GLMs. Discover how to effectively interpret and analyze real-world models.

r_img.tar.gz

rscript

r_stat

R is a widely used programming language for statistical computing. It features robust libraries that cover everything from basic data analytics to visualization functions for presentation. R is used by professional data scientists, researchers, and marketers.

This course is a comprehensive introduction to modern statistical analysis using R, particularly for researchers in life and environmental sciences. You’ll review R and cover its functionality with its various packages. You'll review essential statistical functions including data description, linear modeling, regression analysis, and more. You’ll also cover advanced analytical techniques looking at analysis of variance, factorial designs, and non-normal data distributions. You’ll also discuss generalized linear models, including binomial GLMs and GLMs for binary data. 

By the end of this course, you’ll be prepared to apply statistical methods to real-world problems and interpret and analyze various models in an effective contemporary manner.

Performing Modern Statistical Analysis with R

## ANOVA with explanatory variables
Two-way ANOVA includes two explanatory variables. Usually, these are the treatments of interest, but since the plants in Darwin’s maize experiment were paired, we can stick with the same example by simply adding the factor for the plant pair to the linear model:


# ANOVA with explanatory variables
Two-way ANOVA includes two explanatory variables. Usually, these are the treatments of interest, but since the plants in Darwin’s maize experiment were paired, we can stick with the same example by simply adding the factor for the plant pair to the linear model:


Let’s look at the two-way ANOVA in this lesson.

Introduction

Description

Estimation

Linear Models

Regression

Prediction

Testing

Intervals

Analysis of Variance

Factorial Designs

Analysis of Covariance

Linear Model Complexities

Generalized Linear Models

GLMs for Count Data

Binomial GLMs

GLMs for Binary Data

Conclusion

Appendix

Two-way ANOVA

ANOVA with explanatory variables