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The model properly fits any curvature in the data.The sample contains an adequate number of observations throughout the entire range of all the predictor values.Examine the fitted line plot to determine whether the following criteria are met: If you fit a quadratic model or a cubic model and the quadratic or cubic terms are not statistically significant, you may want to select a different model.Įvaluate how well the model fits your data and whether the model meets your goals. If the p-value is greater than the significance level, you cannot conclude that there is a statistically significant association between the response variable and the term. P-value > α: The association is not statistically significant P-value ≤ α: The association is statistically significant If the p-value is less than or equal to the significance level, you can conclude that there is a statistically significant association between the response variable and the term. A significance level of 0.05 indicates a 5% risk of concluding that an association exists when there is no actual association. Usually, a significance level (denoted as α or alpha) of 0.05 works well.
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The null hypothesis is that the term's coefficient is equal to zero, which indicates that there is no association between the term and the response. To determine whether the association between the response and each term in the model is statistically significant, compare the p-value for the term to your significance level to assess the null hypothesis.