Pregunta:
Interpreting regression analysis
Autor: Danny Terro 3902Respuesta:
The Multiple R is the Correlation Coefficient that measures the strength of a linear relationship between two variables. The larger the absolute value, the stronger is the relationship. • 1 means a strong positive relationship • -1 means a strong negative relationship • 0 means no relationship at all • R Square signifies the Coefficient of Determination, which shows the goodness of fit. It shows how well the data fits this regression model. In our example, the value of R square is 0.97, which is an excellent fit. In other words, 97% of the variation in the dependent variable (y-values) is explained by the independent variables (x-values). • Adjusted R Square is the modified version of R square that adjusts for predictors that are not significant to the regression model. • Standard error is also a goodness of fit measure.
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
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Autor
Danny Terro 3902