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Stability StudyCoefficients |
The coefficients table shows the coefficients (Coef) for each term in the final model and for the constant. The table also includes the standard error (SE Coef), the t-value, and the p-value for each coefficient.
If the VIF values are all close to 1, this indicates that the predictors are not correlated. VIF values greater than 5 suggest that the regression coefficients are poorly estimated.
Example Output |
Coefficients
Term Coef SE Coef T-Value P-Value VIF Constant 100.085 0.143 701.82 0.000 Month -0.13633 0.00769 -17.74 0.000 1.07 Batch 1 -0.232 0.292 -0.80 0.432 3.85 2 0.068 0.292 0.23 0.818 3.85 3 0.394 0.275 1.43 0.162 3.41 4 -0.317 0.292 -1.08 0.287 3.85 5 0.088 0.275 0.32 0.752 * Month*Batch 1 0.0454 0.0164 2.76 0.010 4.52 2 -0.0241 0.0164 -1.47 0.152 4.52 3 -0.0267 0.0136 -1.96 0.060 3.65 4 0.0014 0.0164 0.08 0.935 4.52 5 0.0040 0.0136 0.30 0.769 * |
Interpretation |
The final model for the pill data includes three terms: Month, Batch, and the Month by Batch interaction (Month*Batch). Thus the relationship between the response (Drug%) and time for each batch is described by 4 coefficients.
The coefficients for Constant and Batch together give you the fitted value for each batch at time zero (also called the intercept). For example, the fitted value for Drug% for Batch 1 at time zero is equal to 100.085 - 0.232, or 99.853.
The coefficients for Month and the Month by Batch interaction together give you the slope for the linear relationship between Month and Drug%. For example, the slope for Batch 1 is equal to -0.13633 + 0.0454, or -0.09093. This value indicates that over a period of 1 month, Drug% decreases by 0.09093.
Because the final model includes the batch factor, the intercept and slope for each batch are also shown in the regression equation table.