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

They test all possible combinations of factor levels.

**(Select all that apply.)** What is true about fully factorial experiments?

(Select all that apply.) What is true about fully factorial experiments?

What are the factors in a factorial design?

What are the factors in a factorial design?

The design has 12 independent variables. 

Interactions are present or will be found.

The design has two independent variables, three dependent variables, and two organismic variables.

The design has three independent variables: two levels of A, three levels of B, and two levels of C.

What information is given in the factorial design notation, 2 $\times$ 3 $\times$ 2?

What information is given in the factorial design notation, 2 <math><semantics><mrow><mo>×</mo></mrow><annotation encoding="application/x-tex">\times</annotation></semantics></math>× 3 <math><semantics><mrow><mo>×</mo></mrow><annotation encoding="application/x-tex">\times</annotation></semantics></math>× 2?

Which of the following R functions is used to select variables and observations from a given data frame?

Which of the following R functions is used to select variables and observations from a given data frame?

What is the appropriate statistical test for a factorial design?

What is the appropriate statistical test for a factorial design?

A function in R that can be used to compare a pair of models is:

A function in R that can be used to compare a pair of models is:

In a factorial ANOVA table, we can visualize the interactions using the ________ function.

In a factorial ANOVA table, we can visualize the interactions using the ________ function.

Which of the following can be found in the first column of an ANOVA summary table?

Which of the following can be found in the first column of an ANOVA summary table?

Test your knowledge of factorial designs.

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: Factorial Designs