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R coefficient

I’ve faced very interesting question in my statistic test:

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What does R coefficient define? (it’s from my final test on SPSS research methods)

1 comment to R coefficient

  • Afroze

    The coefficient of determination R2 is used in the context of statistical models and it is very important to SPSS researcher. The main purpose of this kind of model is prediction of future outcomes on the basis of other related information. R2 is simply the square of the sample correlation coefficient, which value lies between ‘0 and 1’. The computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. The coefficient of determination is a measure of the strength of the relationship between the predicted variable and model of the predictors in a regression model.

    Variables in the model:

    1. Model – SPSS research allows you to specify multiple models in a single regression command. This tells you the number of the model being reported.

    2. Variables Entered – SPSS allows you to enter variables into a regression in blocks, and it allows stepwise regression. Hence, you need to know which variables were entered into the current regression.

    If you did not block your independent variables or use stepwise regression, this column should list all of the independent variables that you specified.

    3. Method – This column tells you the method that SPSS used to run the regression. “Enter” means that each independent variable was entered in usual fashion. If you did a stepwise regression, the entry in this column would tell you that.

    4. R – R is the square root of R-Squared and is the correlation between the observed and predicted values of dependent variable.

    5. R-Square – This is the proportion of variance in the dependent variable which can be explained by the independent variables

    6. Adjusted R-square – This is an adjustment of the R-squared that penalizes the addition of extraneous predictors to the model.

    Adjusted R-squared is computed using the formula 1 – ((1 – Rsq)((N -1) /( N – k – 1)) where k is the number of predictors.

    7. Std. Error of the Estimate – This is also referred to as the root mean squared error. It is the standard deviation of the error term and the square root of the Mean Square for the Residuals in the ANOVA.

    I hope I can be able to clarify your issues. You can also get more from SPSS help and other SPSS researchers.

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