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r is a measure of the correlation between the observed value and the predicted
value of the criterion variable.
When you have only one predictor variable in your model, then beta is equivalent to
the correlation coefficient (r) between the predictor and the criterion variable.
When you have more than one predictor variable, you cannot compare the
contribution of each predictor variable by simply comparing the correlation
coefficients. The beta (B) regression coefficient is computed to allow you to make such
comparisons and to assess the strength of the relationship between each predictor variable to the criterion variable.
Beta (standardised regression coefficients) --- The beta value is a measure of how strongly each predictor variable influences the criterion (dependent) variable. The beta is measured in units of standard deviation. For example, a beta value of 2.5 indicates that a change of one standard deviation in the predictor variable will result in a change of 2.5 standard deviations in the criterion variable. Thus, the higher the beta value the greater the impact of the predictor variable on the criterion variable.
In multiple regression, to interpret the direction of the relationship between variables, look at the signs (plus or minus) of the B coefficients. If a B coefficient is positive, then the relationship of this variable with the dependent variable is positive (e.g., the greater the IQ the better the grade point average); if the B coefficient is negative then the relationship is negative (e.g., the lower the class size the better the average test scores). Of course, if the B coefficient is equal to 0 then there is no relationship between the variables.
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