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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

What does `data` refer to in the linear model's function `lm(formula,data,..)`?

What does <code>data</code> refer to in the linear model’s function <code>lm(formula,data,..)</code>?

The ______ function is used to calculate the confidence intervals for linear-model coefficients.

The ______ function is used to calculate the confidence intervals for linear-model coefficients.

**(Select all that apply.)** Which of the following is true about residual error in the regression line?

(Select all that apply.) Which of the following is true about residual error in the regression line?

Which function can be used to plot a graph of the point estimate?

Which function can be used to plot a graph of the point estimate?

It’s a plotting object such as a point, line, or other shapes.

It’s a method used to make conditioning plots.

It’s a method used to map data to attributes, such as color and size.

What is a `geom` in the `ggplot2` system?

What is a <code>geom</code> in the <code>ggplot2</code> system?

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: Linear Models