# On Demand Workshop: Assumptions of Linear Models

### Did you know that there is no assumption in linear regression or ANOVA that the dependent variable follow a normal distribution overall? Really.

The assumption is about the error term.

In fact, a non-normal dependent variable can still meets the assumptions of the model.

I have talked with many researchers who are checking the wrong assumptions. Or they’re checking the right assumptions, but with over-conservative tests that find every data set fails to meet assumptions.

It’s not surprising. Read any two statistics textbooks and they’ll have a different list of assumptions. And list different ways to test them.

Why would they do that?

Because while some of the assumptions are explicitly stated, others are implicit in the way the model is conceptualized.

In this workshop, we will investigate each of the assumptions of linear models so they make sense. You’ll see they really aren’t just a list of arbitrary rules.

Then we’ll show how to check each one using plots, rules of thumb, and tests. We’ll discuss which tests are too sensitive and when, and how to tell if a plot is “close enough”.

And finally, we’ll discuss, in detail, what your options are for dealing with problems and when and how to do them.

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**In this Workshop, you will learn:**

**In this Workshop, you will learn:**

### Module 1: The Assumptions and checking them

You will get an accurate understanding of each of the assumptions, both implicit and explicit. In just two hours, you will learn:

- A brief review of the General Linear Model, in terms of regression and ANOVA, and how it directly and indirectly leads to the assumptions
- what each assumption
*really*means and why each assumption is important to maintain the integrity of the model and the accuracy of the p-values - how to check each assumption, some with tests, and some with plots
- how to construct and read residual plots–how “robust” of a departure from normality or constant variance is acceptable
- how to construct and read normal probability plots, which are much more accurate than histograms for diagnosing normality
- common distributions and situations in which assumptions will likely fail, including count and categorical outcomes, and trucated and censored outcome data

### Module 2: Your Strategies and Options If Assumptions Fail

- what to do if one or more assumptions are not met, including transformations, weighted least squares, and modifying the model
- All about transformations–the different types, why and how to do them, when to do them, on which variables to do them, and what they mean
- Box-Cox transformations for determining whether and which transformation will best suit your data
- A detailed, step-by-step example of how to run a model and check all the assumptions on a
*real*data set (not a perfect textbook example)

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**But can I keep up?**

**But can I keep up?**

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

**You do need to spend some time on it.** Each module has about two hours of instructional videos, plus exercises and supplemental materials. Plan to spend time on those.

**This workshop is for you if you:**

- Will
**need to implement**either linear regression or ANOVA soon, and want to be sure you’re checking assumptions correctly - Have conducted a linear model in the past and
**are confused by the different things you’ve heard**about its assumptions and how to check them - Are
**about to defend your dissertation**and want to be sure you thoroughly understand your analyses well enough to defend your decisions to your committee - Would like to
**learn as much as you can**about regression and ANOVA, so that you can apply it, and learn its extensions,**with confidence and real understanding** - Are comfortable with what the assumptions are, but would like to see, for once, how to really
**interpret the graphs or run transformations with confidence**that you’re doing it correctly

**Prerequisites:**

- The focus here is not on the software. This workshop will be useful to anyone using any software. That said, you should be familiar with running an ANOVA or linear regression in some statistical software. The data sets are available in SPSS, SAS, Stata, R, text, and excel format so you can import it into the statistical software of your choice.
- You will get the most out of the workshop if you have had at least two statistics classes and some experience in data analysis.

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**The workshop is a**n On Demand Workshop

**The workshop is a**

On Demand Workshops are the ultimate in convenience. Everything is housed on a private workshop website where you can get all the workshop materials when you need it, at your own speed, any time of day or night.

You will get a one-year membership in this workshop website, so feel free to come back again and again.

The workshop videos stream from the site, so as a member, you always have access to the most updated version.

The workshop materials are broken into modules, each on a separate page, so they’re easy to keep track of and you won’t get overwhelmed with too much information at once.

#### Each module contains:

- A set of streaming training videos. Watch them whenever it’s convenient for you, as quickly or slowly as you need.
- Supplemental
**handouts,**including the presentation slides, syntax files to recreate the examples, and handy checklists and/or worksheets - Data files in a variety of formats from real research studies, so you can see how to deal with the challenges inherent in real data
**Exercises to practice**what you’ve learned, with answers to check your work.- SPSS, SAS, Stata and R code to replicate all the workshop examples and to complete all the exercises. Save and modify these for use in your own data analysis.
- A place to type in questions for each module.

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### The Instructor:

Karen Grace-Martin is a statistical trainer and consultant and an expert on linear models, data analysis, and SPSS.

Karen has guided and trained researchers through their statistical analysis for over 15 years. Her focus is on helping statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

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### Comments from past participants in this workshop:

*“The examples were easy to understand and including demonstrations in the presentations were very helpful. Having the data sets to play with ourselves in addition to the specific homework exercises really helped to make this a major learning experience. Karen’s slides are so clear and well organized, and she keeps just the right pace. I did not realize online courses could be so effective. Karen is an extremely effective teacher and maximizes use of technology to make this an extremely valuable learning experience.”*

-Prue Plummer-D’Amato, PhD

*“Karen gave a very clear and detailed explanation implicit vs. explicit assumptions. I particularly liked the reviews of residual plots. She verified most of what I thought I knew – but I came away with a better idea of when to be concerned enough to apply remedial measures.”*

-Anonymous

**Bonuses**

To help fill in your understanding of General Linear Models, you’ll get videos of these webinars:

- A Review of Logarithms for the Data Analyst
- The 11 Steps to Performing any Statistical Model
- The First 3 Steps in Running any Statistical Model: Define and Design
- Getting Started with SPSS Syntax

** Investment: $97**

### Guarantee

We really, truly believe you’ll find this workshop helpful and your satisfaction is guaranteed. If you participate in the full workshop and find you are not satisfied for any reason, we will give you a full refund. Just notify us within 90 days of purchase.