Linear Models in R

Linear Models in R: Regression, ANOVA, and Extensions

Learn how to run, visualize, interpret, and test linear regressions, ANOVAs, ANCOVAs, and related models in R

regression-r

To become proficient in any statistical software, you need a strong foundation in running linear models.

They’re the foundation of most of the statistical tests and models that you need in data analysis.

If you get this stuff, it’s much easier to learn many other more complicated models.

In this nine hour workshop, you will gain a strong understanding of how to run a variety of linear models in R.

You will learn the structure, defaults, and options in R’s lm() command and when each one is appropriate.

You will learn complementary commands that will help you expand, visualize, and test the model and run follow up tests.

You will learn extensions of linear models like interactions and quadratic models that will help you build better fitting models.

And you’ll also be introduced to related models, like regression trees and Generalized Additive Models, to use when linear models don’t work.

We will cover the various topics at a relaxed pace, and you will have many opportunities to ask questions.

The example data sets are taken from education and the physical sciences, but the ideas and syntax are applicable for performing linear analyses in every discipline, including business, the bio-medical sciences, psychology and the social sciences.

After taking this workshop, you will be able to recognize when a linear model is appropriate, and then how to implement and interpret a linear regression model in R, and use your model as a predictive tool.

 

In this Workshop, you will learn:

Simple Linear Regressions and Correlations in R

  • Calculating Pearson’s correlations between two or more variables
  • The syntax and options of the lm() command
  • Interpreting R’s regression output
  • How R stores regression coefficients, predicted values and residuals

Investigating and Testing Linear Regression Models

  • Graphing regression (best fit) lines and depicting the residuals
  • Testing the assumptions, working with outliers, and saving standardized residuals
  • Using the gam() function to check linearity
  • Testing for Normality of Residuals
  • Confidence Intervals and Prediction Intervals for Linear Regression

Fitting Multiple Linear Regression Models

  • Investigating your model with the car() package
  • Adding predictors to the model
  • Making predictions from the model
  • Checking multicollinearity
  • Model assumptions and assessment for models with multiple predictors

Extending the Multiple Linear Regression Model

  • Fitting and graphing curved models using the lm() command
  • Interactions in linear regression
  • Fitting nonparametric CART regression models using the tree() command
  • Robust Regression using the rlm() command
  • A brief introduction to Generalized Linear Models for count data

ANOVA models

  • One Way ANOVA
  • Two Way ANOVA with and without interactions
  • Comparing and Graphing Group Means
  • Checking assumptions

ANCOVA models

  • Analysis of Covariance in lm()
  • How to interpret ANCOVA results
  • Adding and interpreting interactions in an ANCOVA model
  • Graphing data from an ANCOVA
  • Adjusted Means using lsmeans()

 

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But can I keep up?

The workshop is pitched at a level that should make it of interest to both students and professionals.

You need prior experience in R if you want to be able to use what you’ve learned. The material should be accessible to those somewhat new to R. You should be able to open a data set, understand data frames, and know how to work with variables in R.

You do need to spend some time each week. You will learn concepts and get some clarity if you don’t practice what you’ve learned on your own. But you won’t entirely get it.

This is a workshop where you want to get your hands dirty with some data. Please expect to spend 2-3 hours per week just doing the exercises.

This workshop is for you if you:

  • Have run linear regressions, ANOVAs, or ANCOVAs in another software package and want to learn how to do it in R.
  • Wish to use linear regression, and want to expand your knowledge of statistical modelling generally.
  • Have been using R, but would like to develop your R skills.
  • Have the time to really invest in learning. It will require about 4-6 hours per week all together.

It is not for you if you:

  • Are a complete beginner in statistics.
  • Have never seen R before.
  • Have no time to practice what you’ve learned.
  • Are an experienced R user and are looking for advanced techniques.

Prerequisites:

  • You will get the most out of the workshop if you have had at a minimum of one statistics class. You should have a good understanding of concepts like hypothesis testing, p-values, and correlation. 
  • You will need to have R installed on your computer.
  • You should have some experience with R. You should have at least a basic understanding of how R handles objects, data, and how to input commands and read output.  We’ll assume you’ve never run a linear model in R before, but that you are comfortable with the R environment.

 

The Instructor:

 

David LillisDavid Lillis has taught R to many researchers and statisticians.

His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R.

David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.

The workshop is offered as an interactive, live online workshop + Membership Site

 Attend the workshop live via webinar. You will hear the instructor and see exactly what is happening on his screen. Ask questions through phone, microphone, or chat.

During the webinar sessions, the instructor will present concepts and explain the meaning of the techniques in that module, demonstrate how to implement those techniques in R on different examples, and answer your questions.

You also get video screenshot recordings of each workshop session made available within 48 hours. So you can review the material right away, a year later, or while doing the exercises. Or if you need to miss one (or all!) of the live sessions, you can still participate on your own schedule.

You also have a membership to the workshop site. The site is our home base for the workshop. It’s where you’ll find everything you need to support your learning of each module:

Text data files. These are real data files from real research projects. You get full access to use the data to try everything on your own.

Handouts with all the R code to run and explore all of the examples yourself. You won’t learn it unless you try it. So you’ll get my code so you can follow along.

Exercises. (Yes, homework!). You really need to practice this stuff. Get your hands dirty. So we’re giving you the data to try it on your own with new models to try out. But don’t worry–you won’t be on your own stuck on some coding error that won’t work. You’ll get the code to do the exercises and the answers in case you get stuck.

But best of all, you can submit questions. So if there’s something you thought was clear during the workshop, but isn’t now that you’re reviewing the explanatory material, just type in your question. David will answer quick questions right there on the website.

Bonus videos from some webinars on relevant topics to help jog your memory or clear up misunderstandings. Included are:

  • The First 3 Steps to Statistical Modeling: How to Clarify the Research Question, the Design, and the Variables
  • Correlated Predictors in Multiple Regression
  • Dummy Coding and Effect Coding in Regression Models

You’ll have access for a year to this workshop membership site. So as we add resources, re-record sessions, and answer more questions each time we offer the workshop, you have access to the updated material.

 

What Our Students Say About Our Workshops:

“The other issue I wished to say was a very big thank you to you for teaching this course for us. Despite my time pressures during its weeks, I have learnt a lot about R from you by doing this course, and you have very definitely made this software feel not as overwhelming as it looked to me at first sight! There is much I will return to in the near future within the videos and handouts that yourself and Analysis Factor have made available in our course fee. I have also appreciated your sharing of resources and the R code that you had developed yourself for various purposes.

Please keep me in touch with later R courses that you run through Analysis Factor. Despite the difficult timeslot they might come up at our end of the world (for both Australia and New Zealand!), the video facility proves very useful, although I have tried to ‘attend’ them in real-time if I could…. You have been most kind in your commitment to your course participants in everything within the course, including in helping us work through and resolve the package installation problems some of us had at the beginning. That was also good learning in itself!”

Jo, South Australia

“David was clear, knowledgeable, and responsive. Great handouts!”

– Anonymous

“For me, it was a good introduction to R, using simpler (well understood) statistical techniques that did not detract from the introduction to R.”

– Martin Watts, PhD

It was great to see someone using R ‘live’ to see how it works.

– Cate Bailey

The explanation was very clear, the topics were useful.

– Jose Hernandez

“Great format, great presenter.”

– Tom Bohon

“I am a new R user; It just gave me another motivation why I need to learn it further. Great work.”

– Essam Alshreafi

“Learned more efficient ways to parse data and evaluate the contents of a given data set. Very nice stuff! Overall, I thought this webinar was very well done. It was informative and moved at a great pace.”

– anon

“More on R from David would be great. His tips were wonderful.”

– Gary Kitchen

Thanks for this, thought it was great and what a magnificent way to teach people stuff… keep up the good work!

– Joop van Eerbeek

“It was a very thorough set of workshops, a good pace, and it should prove to be an asset to my future data analysis projects (assuming that I dedicate some time to reviewing the concepts presented in the series).”

– David Scott

“I would recommend this course because it provides a proper introduction to R, paving the way for a self directed learning in R.”

– Mohammed Ahmed

“The course was very well structured and David Lillis maintained a comfortable pace. The pre-class materials were welcome, as were the post-class materials. They allowed us to run through the lesson in advance so that we could developed some experience with the methods. Knowing that we’d have post-class documents allowed me to relax during the class (I didn’t have to worry about taking extensive notes). Also, appreciated were the bonus materials. Any thing in advance of the class that either adds insight or prepare for the lesson is great.”

– Anonymous

The Details:

The live workshop sessions will be on the following Thursdays from 1pm-2:30pm EDT:

  • May 7
  • May 14
  • May 21
  • May 28
  • June 4
  • June 11

The live Question & Answer sessions will be on the following Wednesdays from 1pm-2:00pm EDT:

  • May 13
  • May 27
  • June 10
  • June 24

Enrollment:

Investment: $297


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Refund Policy: Your registration fee is fully refundable up to 72 hours in advance. Because enrollment is limited, no refunds will be granted after the program begins.

Guarantee

As with all our programs, your satisfaction is guaranteed. If you participate fully in this workshop–watch, read, and try out everything included–and find you are not satisfied for any reason, we will give you a full refund, no questions asked.

Just notify us within 90 days of purchasing the program.