If your graduate statistical training was anything like mine, you learned ANOVA in one class and Linear Regression in another. My professors would often say things like “ANOVA is just a special case of Regression,” but give vague answers when pressed.

It was not until I started consulting that I realized how closely related ANOVA and regression are. They’re not only related, they’re **the same thing**. Not a quarter and a nickel–different sides of the same coin.

So here is a very simple example that shows why. When someone showed me this, a light bulb went on, even though I already knew both ANOVA and mulitple linear regression quite well (and already had my masters in statistics!). I believe that understanding this little concept has been key to my understanding the general linear model as a whole–its applications are far reaching.

Use a model with a single categorical independent variable, employment category, with 3 categories: managerial, clerical, and custodial. The dependent variable is Previous Experience in months. (This data set is employment.sav, and it is one of the data sets that comes free with SPSS).

We can run this as either an ANOVA or a regression. In the ANOVA, the categorical variable is effect coded, which means that each category’s mean is compared to the grand mean. In the regression, the categorical variable is dummy coded**, which means that each category’s intercept is compared to the reference group’s intercept. Since the intercept is defined as the mean value when all other predictors = 0, and there are no other predictors, the three intercepts are just means.

In both analyses, Job Category has an F=69.192, with a p < .001. Highly significant.

In the ANOVA, we find the means of the three groups are:

Clerical: 85.039

Custodial: 298.111

Manager: 77.619

In the Regression, we find these coefficients:

Intercept: 77.619

Clerical: 7.420

Custodial: 220.492

The intercept is simply the mean of the reference group, Managers. The coefficients for the other two groups are the differences in the mean between the reference group and the other groups.

You’ll notice, for example, that the regression coefficient for Clerical, is the difference between the mean for Clerical, 85.039, and the Intercept, or mean for Manager (85.039 – 77.619 = 7.420). The same works for Custodial.

So an ANOVA reports each mean and a p-value that says at least two are significantly different. A regression reports only one mean(as an intercept), and the differences between that one and all other means, but the p-values evaluate those specific comparisons.

It’s all the same model, the same information, but presented in different ways. Understand what the model tells you in each way, and you are empowered.

I suggest you try this little exercise with any data set, then add in a second categorical variable, first without, then with an interaction. Go through the means and the regression coefficients and see how they add up.

**The dummy coding creates two 1/0 variables: Clerical = 1 for the clerical category, 0 otherwise; Custodial = 1 for the custodial category, 0 otherwise. Observations in the Managerial category have a 0 value on both of these variables, and this is known as the reference group.

{ 24 comments… read them below or add one }

Great post; thanks for sharing!

Would it ever be the case that the significance tests of the regression coefficients would come out non-significant when the overall F-test did come out significant? What if, for example, you had a factor with three levels, A, B, and C, with means 3, 5, and 4. If C is the reference level, could it be the case in the regression model that neither the coefficient comparing A to C nor the coefficient comparing B to C would be significantly different from 0, but that the F-statistic would be significant due to the difference between A and B?

Yes. They’re testing slightly different things, as you’ve noticed,. and you’ve hit the difference exactly.

As a lowly Bachelor of engineering, here is why you are wrong:

If we ignore rank-deficient cases, then linear regressions only have a non-zero residual if there are more data points than parameters (overdetermined). In contrast, the residual is zero if there are the same number of data points as parameters (critically determined). In this latter, degenerate case, the “regression” always produces a perfect fit.

So, what happens when we apply ANOVA and regression to grouped data?

- With ANOVA, variance is partitioned into between-level and within-level.

- With critically determined linear regression:

— The model can account for all of the between-level variance, because the number of *unique* predictors (i.e. number of levels) equals the number of parameters. Put another way, the model can pass through all group means.

— The residual will be the within-level variance.

- With overdetermined linear regression:

— The model will only account for some of the between-level variance.

— The residual will be the within-level variance, *plus* the remaining between-level variance.

The regression you described is critically determined, because the number of (unique) input levels and parameters are both 2. The input levels are, e.g. Clerical and Non-Clerical; and the parameters are intercept and slope because you are fitting a line. We expect that this regression and ANOVA will partition your variance in the exact same way, and because they arrive at the same partition, they will have the same mean for the groups as you have observed.

However, if ever you were to have a category with more levels (say, “Year”), then we are instead looking at an overdetermined linear regression. A line (or even a parabola) will now fail to model the entirety of the between-level variance, and the model estimates won’t correspond to any of the ANOVA means.

In summary, we can only say that ANOVA produces equivalent results to linear regressions that are critically determined. You cannot claim that ANOVA is the same as linear regression. Not only is this claim wrong, it is wrong in a subtle enough way that it will condemn readers to many headaches before (and if) they ever claw back to the truth.

I hope the stats book you published doesn’t spread the same misinformation. People learn from your writings and trust you as an expert on the subject matter, so please understand that I am just trying to help.

Hi Hongyuan,

Thanks for your trying to help. Sincerely.

Based on your arguments, it sounds like you believe that I’m suggesting that every value of a continuous predictor (X) in regression could be treated as a different grouping variable in ANOVA. And you are absolutely correct that that approach would lead to a big, ugly mess. If I’m misreading your concerns, please let me know.

That’s not what I meant at all.

The key phrases are “Use a model with a single categorical independent variable” and “In the regression, the categorical variable is dummy coded**.” The asterisks lead to a footnote at the bottom that shows that in the regression model, there are only two IVs, each of which have two values: 1 and 0. If you’d like to read up more on dummy coding, here is a good description: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm.

This works because even if there are two values, Clerical (1) and Non-Clerical (0), there is *more than one observation with each of those values.* You’re right, if the n=2, it wouldn’t work either. And I never did mention my sample size, but it was 474. This is a data set that one would typically run as a one-way anova. But if you dummy code, it works just as well in a linear regression and the values of the F test will be identical. Try it.

Hi Karen,

Thank you for your response. What I believed you meant was that one can consider ANOVA and regression as the same concept, and still be fine. I don’t think that statement can be considered correct because they generally produce profoundly different results.

The actual sample size is irrelevant. Nowhere in my original post did I mention anything about sample size needing to be 2. The fact that you had 2 *levels*, or groups (0 and 1) implies that your F-test results and group means will be identical between slope-intercept regression and ANOVA. We don’t even need to crunch the numbers to see why this is the case.

Conversely, had there been 3 levels, the results of slope-intercept regression wouldn’t be the same as ANOVA at all. (But regression with a parabola, having 3 parameters, would still be identical to ANOVA, etc.)

Hongyuan

Thanks for this post. I looked for the SPSS file entitled ‘employment.sav’ but it was not there. I did find one called ‘Employee data.sav’ though which I believe is the same data you used (n=474). Just thought I would highlight this because if anyone is playing with this data they may not be able to find it. I am using SPSS 20, so perhaps there have been changes to names…

I also just have a quick question. When dummy coding the reference category stays at zero along with one of the categories. In this case there were two dummy variables and Managers were coded zero along with another category on each dummy variable.

I was just wondering how the regression analysis knows that the reference category is indeed the reference category and treats it as a constant and differentiates it from the other category which is also coded as zero?

I got the same results of you so I don’t think there was an issue with my dummy coding. I followed the recommendations of Field (2009).

Hi Sam,

It works because there are only two dummy variables for the three variables. Only individuals in the dummy category have zeros on BOTH these variables. I find the way to really show this is to walk you through the equations, which I clearly can’t do here (it takes me a good 1/2 hour to walk someone through it). But I did a webinar on it, if you’re really interested in seeing how dummy coding works: Dummy Coding and Effect Coding in Regression Models. We also go through this in detail, with and without interactions in my Interpreting (Even Tricky) Regression Coefficients workshop.

Interesting! One question though: When one does Anova, one is usually advised not not do basic t-tests between two individual categories, but instead use post-hoc tests that adjust alpha levels to multiple comparisions. When one does a t-test on a regression coefficient to see if it is significantly different from zero, does this issue not arise? I am wondering, because from your example I understand that an individual coefficient is just the difference between the respective group and the comparision group.thanks in advance for any pointers where my logic is wrong.

Hi John,

That’s a great question. There are two reasons. One is not really about doing t-tests in the ANOVA, but doing all pairwise t-tests. The regression situation is identical to a set of orthogonal contrasts in ANOVA. Because they’re independent of each other, they don’t influence each other. If you kept changing the reference group in the regression in order to do more comparisons, then you’d need to do some adjustment.

The other is the idea of post-hoc comparisons vs. planned comparisons. Whenever comparisons are made post-hoc (you’re just looking to see which means are different), you need the adjustment. That’s different from choosing which contrasts you want to make.

Hi Karen, I just read your post there and found it incredibly interesting. It did, however, cause me to worry very much so about my current thesis, and the analysis I should use for it.

I am looking to see if employees’ organisational history (such as voluntary/involuntary turnover) and their contract type (part or full-time) impacts on their organisational commitment and empowerment.

So I have 2 Dependent Variables (Commitment Questionnaire and Empowerment Questionnaire, both Likert-scales)

And I have 3 Independent Variables (Occupational history (3 levels), contract type (2 levels) and I want to use gender as well, to see this impacts on the difference).

I have attempted a Two-Way ANOVA, using only 2 IV (Occupational history and contract type) with both of the DV, but both times my Levene’s score was significant, and I could not find an answer as to what to do if this is the case? does this mean I cannot use an ANOVA, as the assumptions are not met? And for that reason would I be better to use a regression with 3 IV?

Hi Conor,

First of all Levene’s test is not very reliable. (Geoffrey Keppel’s ANOVA book discusses this). I would suggest testing the heterogeneity of variance assumption a few different ways.

Even so, you’ll get exactly the same results if you run it in regression instead of ANOVA. They’re mathematically equivalent.

Hello, thanks for the post. i dont know if you are still active in replying to comments but i thought i might try as Im currently stuck in Master Thesis analysis.

After reading this I tend to do a regression but maybe you can give me a tip.

I have a categorical IV and 5 continuous Dvs and 5 control variables that are continuous as well. I have to check for moderationd and mediation as well.

In the beginning i thought i run some Anovas or a Manova to check if there a differences between the groups for the DV’s. But now im struggeling as i dont know how to integrate the control variables.

Is it correct that i have to run a multiple regression and recode my IV into a dummy ?

Id be so thankful and happy to receive your answer.

Thanks!

Hi Lara,

I do still answer questions when I can, but sometimes go through periods where I can’t. I do always answer questions right away in the Data Analysis Brown Bag or in Quick Question consulting, so if you ever need a quick answer, either will get you one.

It depends which software you’re using, but all should allow you to include those covariates in a MANOVA. I suppose technically at that point it would be called MANCOVA, but you’re software doesn’t care what you’re calling it.

It depends on what software you’re

How would you explain the difference between a 2-way ANOVA when both ways are categorical, but one is defined by a measurable variable (e.g., temperature at which a strain of fruit fly is raised), and a linear regression with a dummy for the other variable? In particular, the ANOVA has an interaction component, while the regression doesn’t.

Hi Peter,

Sure, but those are only the defaults. You don’t have to include an interaction in an ANOVA and you always can include an interaction in a linear model.

But these defaults reflect the way people usually use ANOVA and Regression, so sometimes the software makes changing the defaults difficult. There are always ways around it though, and there are no theoretical reasons not to.

Karen

Hi Karen. As a FORMER 6 Sigma Quality Black Belt it has been a while since I have done an in depth study but I recently ran a simple 2 to the 2 DOE that resulted in an R squared of 84.78% but when I ran a regression analysis (both on Minitab 15) I only get an R squared of 62.6%. Can you help me understand why there would be that big a difference? Thanks!

Hi Dean,

There shouldn’t be. It must be differences in defaults between the two procedures. For example, one is assuming the variables are continuous and the other categorical. Or one defaults to a main-effects only model whereas the other includes and interaction.

Hi Karen, no, niether of those scenarios. The data is somewhat non-normal. Could that account for it or should they be the EXACT R Squared regardless? Thanks again!

Hi, me again. I take it back, my regression equation does not include interactions (that is, my equation only shows a constant and the main effects) , I ASSUMED that MINITAB automatically accounted for that. I can’t find an option to include interactions (maybe is there but I don’t see it, are you familiar with MINITAB and if so how to include interactions in the regression analysis?).

Hi Dean,

I have used Minitab, but I don’t currently have it and don’t remember the defaults. However, in most statistical software, the only way to include an interaction in a linear regression procedure is to create an interaction variable.

So literally, if you want an interaction term for X*Z, create a new variable that is the product of X and Z.

Then add it to your linear regression.

Karen

Hi guys,

Reading this post I was curious about any ad hoc tests that could be employed to test differences between every pair of treatments, just like the common post hoc comparison for ANOVA. As Karen has already highlighted post-hoc comparisons is different from planned comparisons, so I think any kind of contrasts would not be appropriate. So, does this post-hoc for dummy variables regression exists?

Eduardo

This really helps to clarify things. Thanks!