# What is a general linear model?

#### noetsi

##### No cake for spunky
Not sure of the terminology here although lots of dummy variables were used as predictors. This comes from a disertation I am reading.

"To explore the relationship between personal characteristics and employment outcome rates, a multiple regression was used. Since many of the personal characteristic variables were coded categorically, a general linear model was used to run the multiple regressions."

Is this just another way of saying multiple linear regression with dummy predictors?

#### katxt

##### Active Member
A simple view is a combination of regression (continuous predictors) and anova (categorical predictors) although the internal workings use just what you have said - regression with dummy predictors for the categories.

#### noetsi

##### No cake for spunky
Statistics terminology drives me crazy. Different people use differ terms for the same thing.

#### hlsmith

##### Less is more. Stay pure. Stay poor.
Given just this snippet, I would say they just used multiple linear regression more than once based on the "s".

#### fed2

##### Active Member
Statistics terminology drives me crazy. Different people use differ terms for the same thing.
I feel like there should be some sort of convention for naming stats procedures, like IUPAC does for chemicals.

I think multiple regression is really just a subset of general linear models, but software often gives different procedures for the two, which is where this language is coming from.

#### noetsi

##### No cake for spunky
Given just this snippet, I would say they just used multiple linear regression more than once based on the "s".
There is not really any more details. They said the same thing over and over.

#### Dason

A general linear model is like a model but it's also linear so it's like a linear model but it's also like general so it's like a general linear model. You know?

But just because it's a general linear model doesn't mean the predictions will fall on a line. You know?

As if.

Totally.

Rolling with my homies.

Sorry - we put on Clueless a while ago.

#### noetsi

##### No cake for spunky
lol dason its too early to be drunk

I think what raised my concern is I have been reading about models like general additive models recently and I had not heard linear regression called general linear models before. I wondered if they had different assumptions.

#### Dason

lol dason its too early to be drunk
Lies. If you're gonna drink then drink early and end early. Drink lots of water after. You avoid the hangover that way. It's not like anybody should be going to bars now.

Plus it sounds like you've never watched Clueless. You're clueless about Clueless.

I think what raised my concern is I have been reading about models like general additive models recently and I had not heard linear regression called general linear models before. I wondered if they had different assumptions.
General additive models are different but if all anybody says about a model is that it's a "linear regression" then it almost surely falls under the umbrella of general linear model.

#### noetsi

##### No cake for spunky
I watched clueless once. It seemed pretty clueless. I am not sure what the point was, unless having lots of attractive young people is the point.

Although I don't drink I know there are drinks, vodka is one I think, that do not generate traditional hangovers. They hurt you other ways. I have never figured out the value of getting drunk, even assuming I could afford to do so (and of course my faith teaches not to get drunk).

I hope you have a peaceful holidays Dason.

#### Dason

I watched clueless once. It seemed pretty clueless. I am not sure what the point was, unless having lots of attractive young people is the point.
I guess you just don't like the classics

#### noetsi

##### No cake for spunky
Was clueless related to Shakespeare? I read most of his plays long ago or probably synopsis of them.

#### AlexNillson89

##### New Member
This video helped me a lot. Maybe it'll be informative for you, too