In regression analysis, P-value is 0.05, and R sq adj is 23%, what is the inference you will draw?
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In regression analysis, P-value and R-squared adjusted (R sq adj) are used to determine the goodness of fit of a model.
- The P-value is a measure of statistical significance, and it represents the probability that the results of a hypothesis test are due to chance. Typically, if the P-value is less than 0.05, it is considered statistically significant, indicating that there is a strong relationship between the independent variable(s) and the dependent variable.
- R sq adj, also known as the coefficient of determination, is a measure of how well the model fits the data. It represents the proportion of the variance in the dependent variable that is explained by the independent variable(s). R sq adj values range from 0 to 100%, with higher values indicating a better fit.
- In this case, the P-value is 0.05, which is less than 0.05 and considered statistically significant. This indicates that there is a strong relationship between the independent variable(s) and the dependent variable. The R sq adj is 23%, which is a relatively low value and indicates that the model does not fit the data very well.
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