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.

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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

What is the value of lambda (`λ`) in Box-Cox transformation when data (`y`) is squared?

What is the value of lambda (<code>λ</code>) in Box-Cox transformation when data (<code>y</code>) is squared?

In R, what value do we need to input for the family parameter in the `glm()` function?

In R, what value do we need to input for the family parameter in the <code>glm()</code> function?

In a generalized linear model, which link function belongs to the gamma family?

In a generalized linear model, which link function belongs to the gamma family?

They allow curvature in the relationship between the variables.

How are generalized linear models different from linear regression models?

How are generalized linear models different from linear regression models?

Square-root transformation produces a linear relationship, but the variance still increases with the mean.

Log transformation of the data produces constant variance and introduces curvature in the opposite direction.

The problem can be solved using polynomial regression only.

Why do data transformations, such as log and square-root, not work for data with curvilinear relationships?

Why do data transformations, such as log and square-root, not work for data with curvilinear relationships?

Test your knowledge off generalized linear models.

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

Quiz: Generalized Linear Models