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Analyze VariabilityTable of fits and residuals |
Minitab displays a table of fits and residuals. Minitab calculates fitted values for all combinations of factor levels. You can use this table to:
Example Output |
Fits and Diagnostics for All Observations
Original Response
Ratio Obs Std Fit Residual 1 1.4131 1.5985 0.8840 2 1.5567 1.3607 1.1440 3 4.5696 4.2442 1.0767 4 0.4565 0.4971 0.9184 5 1.6529 1.5064 1.0973 6 1.6812 1.8656 0.9011 7 3.2686 3.4137 0.9575 8 0.6144 0.5817 1.0562 9 1.4041 1.5235 0.9216 10 1.4070 1.3115 1.0729 11 3.8516 3.3787 1.1400 12 0.3550 0.4002 0.8872 13 1.6272 1.4547 1.1186 14 1.6472 1.8219 0.9041 15 2.3432 2.7537 0.8509 16 0.5514 0.4745 1.1620
Transformed Response
Std Obs Ln(Std) Ln(Fit) Ln(Resid) Ln(Resid) 1 0.346 0.469 -0.123 -1.15 2 0.443 0.308 0.135 1.25 3 1.519 1.446 0.074 0.69 4 -0.784 -0.699 -0.085 -0.79 5 0.503 0.410 0.093 0.86 6 0.520 0.624 -0.104 -0.97 7 1.184 1.228 -0.043 -0.40 8 -0.487 -0.542 0.055 0.51 9 0.339 0.421 -0.082 -0.76 10 0.341 0.271 0.070 0.65 11 1.348 1.217 0.131 1.22 12 -1.036 -0.916 -0.120 -1.11 13 0.487 0.375 0.112 1.04 14 0.499 0.600 -0.101 -0.94 15 0.851 1.013 -0.161 -1.50 16 -0.595 -0.746 0.150 1.40 |
Interpretation |
In this example, the first row in the worksheet is the factors set at their low levels. For this observation, the natural log of the standard deviation of strength is 0.346 and the fitted value calculated from the regression coefficients is 0.469. All of the standardized residuals for the natural log of the standard deviation are between -2 and 2, so no unusual observations exist in the data.