Polynomial Regression, R, and ggplot

Name: Polynomial Regression, R, and ggplot
Rating: 4.6 (1353 reviews)

Learn how to write and graph functions in R and how to fit polynomials to data sets.

Created byCharles Redmond

Last updated 5/2015

English

What you'll learn

Write functions in R.
Graph functions with ggplot.
Fit polynomials to data sets.
Fit smoothing splines to data sets.

Course content

2 sections • 13 lectures • 1h 5m total length

Introduction1:43
In this lecture, I introduce myself and the course, and I give you a brief overview of the topics we will cover.
A Review of Functions2:30
In this lecture, I will review for you the definition of a function from your early mathematics classes. I will focus exclusively on polynomials.
Creating Functions in R3:54
After this lecture, you will be able to write your own functions in R. I will focus exclusively on functions with just one parameter, and even then I will focus just on polynomials.
Plotting Functions with ggplot7:53
After this lecture, you will be able to plot your own functions with ggplot.
Polynomials2:17
After this lecture you will understand what a polynomial is.

Reading in Our Data2:46
After this lecture, you will be able to read in a data set provided by me.
Fitting a Line5:49
In this lecture, I review how to fit a least-squares line to the points and how to plot the line.
Plotting Points along the Least-Squares Line3:33
In this lecture, I review how to plot points along the least-squares line. The points have the same x-coordinates as the data points, but their y-coordinates are determined by the least-squares line.
Visualizing the Residuals6:54
In this lecture, I review how to visualize the residuals associated with the least-squares line. Recall that the residuals are the difference (in absolute value) between the y-coordinates of the data points and the y-coordinates of the corresponding points along the least-squares line.
Best Fitting Polynomial of Degree 27:24
After this lecture, you will be able to fit a polynomial of degree two to a set of points using the lm method. We will continue to graph the modeling function, to plot points along it, and to draw in the residuals.
Best Fitting Polynomial of Degree 35:56
After this lecture, you will be able to fit a polynomial of degree three to a set of points using the lm method. We will continue to graph the modeling function, to plot points along it, and to draw in the residuals.
Smoothing Splines10:52
After this lecture, you will be able to fit a smoothing spline to a set of points. You will be able to adjust your spline by indicating various values for degrees of freedom.
Course wrap-up3:31
In this lecture, I warn about the dangers of over-fitting.

Requirements

Students will need to have R and RStudio installed on their own computers.
It will be best if students have the background one would get by viewing my course "R, ggplot, and Simple Linear Regression".

Description

This course is a sequel to my course "R, ggplot, and Simple Linear Regression". Here we take on polynomial regression and learn how to fit polynomials to data sets. Along the way, we will learn how to write our own functions in R and how to graph them with ggplot. At the conclusion of the course, we will learn how to fit a smoothing spline to data sets.

At a relaxed pace, it should take about a week to complete the course. You will need to have R and RStudio installed, and it would be best if you have a background in R and ggplot equivalent to what you would get if you viewed my first course mentioned above.

Who this course is for:

This course is for those looking to learn more about R.
This course is for those looking to learn more about ggplot.
This course is for those looking to understand polynomial regression.

Polynomial Regression, R, and ggplot

What you'll learn

Explore related topics

Course content

Functions in R5 lectures • 18min

Polynomial Regression8 lectures • 47min

Requirements

Description

Who this course is for: