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.

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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

In the regression equation
 
$y = a + bx$ , 

$b$ is the _________: 

In the regression equation
<math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>a</mi><mo>+</mo><mi>b</mi><mi>x</mi></mrow><annotation encoding="application/x-tex">y = a + bx</annotation></semantics></math>y=a+bx ,
<math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi></mrow><annotation encoding="application/x-tex">b</annotation></semantics></math>b is the _________:

**(Select all that apply.)** In linear regression, which of the following methods do we use to find the line of best fit for our data?

(Select all that apply.) In linear regression, which of the following methods do we use to find the line of best fit for our data?

The correlation coefficient of $0.2$ represents a ________ between variables.

The correlation coefficient of <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0.2</mn></mrow><annotation encoding="application/x-tex">0.2</annotation></semantics></math>0.2 represents a ________ between variables.

**(Select all that apply.)** The graphs below show two fitted regression lines (A and B) for randomly generated data. Which of the following statements is true about the sum of the residuals of A and B?

<img 
src="/udata/1mpVW4pyzqZ/Screenshot 2022-04-11 at 2.50.02 PM.png"
width="400"
align="centre"
/>

**Note:**
* The scale is the same for both axes in both graphs.
The independent variable is on the $x-axis$, and the dependent variable is on the $y-axis$.

(Select all that apply.) The graphs below show two fitted regression lines (A and B) for randomly generated data. Which of the following statements is true about the sum of the residuals of A and B?
<img 
src="/udata/1mpVW4pyzqZ/Screenshot 2022-04-11 at 2.50.02 PM.png"
width="400"
align="centre"
/>
Note:
<ul>
<li>The scale is the same for both axes in both graphs.
The independent variable is on the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mo>−</mo><mi>a</mi><mi>x</mi><mi>i</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">x-axis</annotation></semantics></math>x−axis, and the dependent variable is on the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>−</mo><mi>a</mi><mi>x</mi><mi>i</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">y-axis</annotation></semantics></math>y−axis.</li>
</ul>

Test your knowledge of linear regression.

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: Regression