Beta coefficients do not reflect what you say. Beta coefficients reflect the

expected increase in the dependent variable when the independent variable

increases by one unit. Raw betas (otherwise known as b coefficients) tell

this for the raw variables, and standardized betas (also known as beta

coefficients in the proper sense) tell this for the standardized or z-score

variables = raw variable minus mean divided standard deviation.

The story about explained variance refers to the squared correlation

coefficient, R square, which equals the proportion of total variance in the

dependent variable explained by the regression model.

At each step of your stepwise regression you will find the corresponding R

square and adjusted R square. Use the adjusted one for more conservative

assessment of the quality of your model.

Hector

-----Mensaje original-----

De: SPSSX(r) Discussion [mailto:

[hidden email]] En nombre de Marco

van de Ven

Enviado el: Saturday, August 05, 2006 5:38 AM

Para:

[hidden email]
Asunto: Beta Coefficient

Dear list,

I have rather simple question regarding the Beta coefficients in my linear

regression analysis, and I hope anyone of you is willing to reply. For as

far as I understand it, Beta coefficients reflect the percentage of

explained variation of the dependent variable by the predictor, but during

one of my recent analyses, the Beta coefficients of all variables in model 3

proposed by the stepwise linear regression procedure, I encountered a case

where the total of Beta coefficients within a model is more than 1, hence

explains more than 100%. Obviously this is nonsense, and therefore my

understanding of the Beta coefficients is apparently incorrect. Could anyone

explain how I should interpret this?

Let me describe the model: It contains three predictors. Predictor one has

Beta = .666, predictor two has Beta = .323, and finally predictor three =

.031. How should I interpret these values in term of the expanatoriness of

this model?

Thanks in advance,

Marco

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