Design Matrix: Restrictions & Definition - Mike

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    Design Matrix
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Design matrices are mathematical entities used in statistical modeling, particularly in ANOVA and regression analysis, and cannot be composed entirely of zeros. Their structure varies based on the specific problem being addressed, such as whether it is univariate or multivariate. For example, in linear regression, the design matrix typically includes a column of ones for the intercept and columns for the predictor variables. It's crucial to avoid nearly linearly dependent observation vectors, as they can lead to unpredictable results. Correlated variables can be addressed using techniques like Principal Components to ensure proper regression analysis.
mikeph
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Hi,
Are there any restrictions on what a design matrix can be?

I have no background in this area, I'm just wondering from the wikipedia article, is it a mathematical entity or simply a name? Can it be all zeros, diagonal, or anything I want?

Thanks
Mike
 
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What type of problem are you considering? Design matrices can have different forms depending on whether you are doing a type of ANOVA or regression, whether the problem is multivariate or univariate.
The only easy answer to give is the one for "can it be all zeros?" No, it can't.

As one simple example: if you want to do a linear regression through the points (2,5), (3,4), (5,12), (7,13),
the "design matrix" that would be used to develop the fit with matrix methods has its first column all 1s and the second column the x-values of these points.
 
Hey MikeyW.

One thing you need to look out for is when you have observation vectors that are nearly linearly dependent. When this occurs you get all kinds of crazy behavior.

Most packages will pick this up and you can use software to find out the vectors that have this property.

Also if you have variables that are largely correlated, then you can use something like Principal Components to un-correlate them and use for a regression.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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