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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

**(True or False.)** Although ANOVA tables are mostly used in analyses with categorical explanatory variables, these tables can be used in analyses that include continuous explanatory variables.

(True or False.) Although ANOVA tables are mostly used in analyses with categorical explanatory variables, these tables can be used in analyses that include continuous explanatory variables.

The general strategy of ANOVA is to quantify the overall variability in the dataset. This variability is captured using which of the following methods?

The general strategy of ANOVA is to quantify the overall variability in the dataset. This variability is captured using which of the following methods?

We can calculate the signal-to-noise ratio by dividing the treatment variance by the residual error variance to produce a(n) ________.

We can calculate the signal-to-noise ratio by dividing the treatment variance by the residual error variance to produce a(n) ________.

The larger the sample, the lower the p-value due to increased statistical power. This makes it easy to use p-values to compare the strength of results from different analyses when the sample sizes vary.

The larger the sample, the lower the p-value due to increased statistical power. This makes it easy to use p-values to compare the strength of results from different analyses when the sample sizes vary.

Test your knowledge of the analysis of variance.

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: Analysis of Variance