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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

The logistic transformation maps proportions from ________ into a symmetric S-shaped curve that asymptotes toward plus and minus infinity.

The logistic transformation maps proportions from ________ into a symmetric S-shaped curve that asymptotes toward plus and minus infinity.

Which is an alternative link function is used in binomial GLMs (in addition to the logistic function)?

Which is an alternative link function is used in binomial GLMs (in addition to the logistic function)?

Selecting a single best model doesn’t reflect the model selection uncertainty involved in arriving at that model. What can be done to avoid this uncertainty?

Selecting a single best model doesn’t reflect the model selection uncertainty involved in arriving at that model. What can be done to avoid this uncertainty?

How can we express the binomial count data?

How can we express the binomial count data?

Test your knowledge on binomial 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: Binomial GLMs