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

**(Select all that apply)** Which of the following is a basic type of the t-test?

(Select all that apply) Which of the following is a basic type of the t-test?

A *t-test* compares the difference between the means of _________ sample(s) against a null hypothesis of zero.

A t-test compares the difference between the means of _________ sample(s) against a null hypothesis of zero.

`qt(degrees of freedom, confidence level)`

`critical.value(degrees of freedom,confidence level)`

`critical.value(confidence level,degrees of freedom)`

`qt(confidence level, degrees of freedom)`

Which of the following represents the correct way to estimate the critical value of *t* in R?

Which of the following represents the correct way to estimate the critical value of t in R?

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: Testing