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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

To understand the structure of data in relation with the experimental design.

To determine the types of variables in the data.

To determine the number of samples in the data.

Why is it important to explore the dataset before using it in an experiment?

Why is it important to explore the dataset before using it in an experiment?

The `str()` function converts the provided data into `string` in R.

The `str()` function displays the internal structure of an R object.

The `str()` function creates and initializes an array of `strings` in R.

What is the functionality of the `str()` function in R? 

What is the functionality of the <code>str()</code> function in R?

Which measure of central tendency is derived from the most common value?

Which measure of central tendency is derived from the most common value?

A function in R that calculates summary statistics for each group is: 

A function in R that calculates summary statistics for each group is:

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: Description