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The following plots are available for one factor reliability designs. For information about all the different plots that can be displayed in a design folio, see Design Folio Plots. For general information on working with plots, see ReliaSoft Plot Utilities.
Level Plots
Level plots allow you to compare the visually evaluate the effects of different factor levels on the selected response.
The Response vs. Level plot shows the observed output, or response, as well as the calculated mean output, at each level of the factor.
The Life Characteristic plot shows the calculated life characteristic at each factor level. The top and bottom tick marks on the vertical lines mark the two-sided confidence bounds on the life characteristic. The confidence level for the bounds is determined by the risk level specified on the Analysis Settings page of the Data tab control panel (e.g., if the risk level is 0.1, then 90% two-sided bounds would be shown).
The value of the characteristic life depends on the selected distribution.
Eta is used for the Weibull distribution, and it is equal to the time at which unreliability = 63.2%.
Ln-Mean is used for the lognormal distribution, and it is equal to the time at which unreliability = 50%.
MTTF (i.e., the mean time to failure) is used for the exponential distribution.
The Comparison Chart shows the standardized difference for each paired comparison of factor levels.
Effect Plots
Effect plots allow you to visually evaluate the effects of the factor on the selected response.
The Scatter Plot shows the observed values of the currently selected response plotted against the levels of the factor.
Residual Plots
Residuals are the differences between the observed response values and the response values predicted by the model at each combination of factor values. Residual plots help to determine the validity of the model for the currently selected response. When applicable, a residual plot allows the user to select the type of residual to be used:
Regular Residual is the difference between the observed Y and the predicted Y.
Standardized Residual is the regular residual divided by the constant standard deviation.
Studentized Residual is the regular residual divided by an estimate of its standard deviation.
External Studentized Residual is the regular residual divided by an estimate of its standard deviation, where the observation in question is omitted from the estimation.
The plots are described next.
The Residual Probability* plot is the normal probability plot of the residuals. If all points fall on the line, the model fits the data well (i.e., the residuals follow a normal distribution). Some scatter is to be expected, but noticeable patterns may indicate that a transformation should be used for further analysis. Two additional measures of how well the normal distribution fits the data are provided by default in the lower title of this plot. Smaller values for the Anderson-Darling test indicate a better fit. Smaller p values indicate a worse fit.
The Residual vs. Fitted* plot shows the residuals plotted against the fitted, or predicted, values of the selected response. If the points are randomly distributed around the "0" line in the plot, the model fits the data well. If a pattern or trend is apparent, it can mean either that the model does not provide a good fit or that Y is not normally distributed, in which case a transformation should be used for further analysis. Points outside the critical value lines, which are calculated based on the specified alpha (risk) value, may be outliers and should be examined to determine the cause of their variation.
The Residual vs. Order* plot shows the residuals plotted against the order of runs used in the design. If the points are randomly distributed in the plot, it means that the test sequence of the experiment has no effect. If a pattern or trend is apparent, this indicates that a time-related variable may be affecting the experiment and should be addressed by randomization and/or blocking. Points outside the critical value lines, which are calculated based on the specified alpha (risk) value, may be outliers and should be examined to determine the cause of their variation.
* These plots are available only when there is error in the design, indicated by a positive value for sum of squares for Residual in the ANOVA table of the analysis results.
© 1992-2018. HBM Prenscia Inc. ALL RIGHTS RESERVED.
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