r_img.tar.gz

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

A function in R that provides the point estimates together with the standard error or a confidence or prediction interval is the ________ function.

A function in R that provides the point estimates together with the standard error or a confidence or prediction interval is the ________ function.

In the **R** programming language, we can calculate the estimates of the intercept and slope in linear regression by using the ________ function.

In the R programming language, we can calculate the estimates of the intercept and slope in linear regression by using the ________ function.

They reflect the degree of scatter around the regression line as quantified by the residual variance.

They depict our confidence in the regression estimate.

They depict our confidence in the prediction.

How are **prediction intervals** different from confidence intervals?

How are prediction intervals different from confidence intervals?

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: Prediction