Performing Modern Statistical Analysis with R

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.

Intermediate

111 Lessons

29h 55min

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

229 Playgrounds

15 Quizzes

This course includes

229 Playgrounds

15 Quizzes

Course Overview

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 statist...Show More

What You'll Learn

The ability to perform basic and advanced statistical analysis with R

Working knowledge of various R packages and commands

An understanding of the contemporary framework for interpreting statistical models in R

The ability to test model prediction using diagnostic plots in R

An understanding of retrieving, cleaning, analyzing, and visualizing data with R

A deep understanding of generalized linear models for data with non-normal distributions

What You'll Learn

The ability to perform basic and advanced statistical analysis with R

Course Content

Introduction

Get familiar with linear models, GLMs application in R, and their crucial role.

About the Course

Getting Started

Motivation for Statistical Analysis

The R Programming Language and its Packages

Description

Unpack the core of pollination data analysis, summary statistics, group comparisons, and reproducibility with R.

The Reproducibility Crisis and R Scripts

Quiz: Description

Summary

Estimation

Work your way through statistical estimation, variability, normal distribution, and confidence intervals in R.

Estimation with the Pollination Data

Standard Deviations and Standard Errors

The Normal Distribution and the Central Limit Theorem

Quiz: Estimation

Summary

Linear Models

Enhance your skills in using R for linear model analysis, confidence intervals, and statistical plotting.

Introduction to Linear Model Analysis

Standard Error of the Difference

Confidence Intervals and Darwin’s Question

Releveling to Calculate Mean and Standard Error

Normality and Equal Variance

Challenge: Summary Statistics and Plotting in R

Solution: Summary Statistics and Plotting in R

Quiz: Linear Models

Summary

Regression

Map out the steps for using linear regression in R for predictive analysis.

Introduction to Linear Regression

The Janka Hardness Data

Correlation and Linear Regression in R

Assumptions in Linear Regression

Quiz: Regression

Summary

Prediction

6 Lessons

Focus on making accurate predictions in R using regression, intervals, and hands-on examples.

Testing

7 Lessons

Piece together the parts of t-tests, critical values, and hypothesis testing using R.

Intervals

10 Lessons

Learn how to use estimates and intervals for effective statistical analysis with R.

Analysis of Variance

6 Lessons

Unpack the core of ANOVA tables, hypothesis testing, and two-way ANOVA for data analysis.

10.

Factorial Designs

8 Lessons

Examine factorial designs to analyze interactions and additive effects using R's ANOVA tests.

11.

Analysis of Covariance

6 Lessons

Enhance your skills in interpreting and applying ANCOVA to agricultural data.

12.

Linear Model Complexities

9 Lessons

Dig deeper into handling ANOVA complexities, unbalanced designs, main effects, and linear regression.

13.

Generalized Linear Models

7 Lessons

Utilize GLMs for flexible regression, addressing mean and variance independently with R's tools.

14.

GLMs for Count Data

4 Lessons

Master the steps to analyze count data with Poisson and quasi-maximum likelihood GLMs.

15.

Binomial GLMs

5 Lessons

Learn how to use binomial GLMs and logistic models for mortality rate analysis.

16.

GLMs for Binary Data

5 Lessons

Get started with analyzing binary data using binomial GLMs for insightful interpretations.

17.

Conclusion

5 Lessons

Examine key GLM analyses, enhance reproducibility, and consider future learning in statistical analysis.

18.

Appendix

2 Lessons

Grasp the fundamentals of installing and using R and RStudio for reproducible research.

Course Author

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Anthony Walker

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Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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