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	Plots

One Factor Designs: Plots

The following plots are available for one factor designs with standard response data. For information about all the different plots that can be displayed in a design folio, see Design Folio Plots.

• Level Plots

Level plots allow you to compare the visually evaluate the effects of different factor levels on the selected response.

• • The Comparison Chart shows the standardized difference for each paired comparison of factor levels.

  • 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 Level Mean plot shows the mean output at each level of the factor. The center point of each level line is the calculated mean and the end points represent the high and low confidence bounds on the mean based on the alpha (risk) value specified on the Analysis Settings page of the control panel.

  • The Box Plot shows the output at each level of the factor. The top and bottom points at each level represent the highest and lowest responses. The points within the box represent the responses at the 25th, 50th and 75th percentile.

  • The Mean PDFs plot shows the pdf of the mean response at each level of the factor.

• Effect Plots

Effects 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.

• • 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.

  • The Residual Histogram* is used to demonstrate whether the residual is normally distributed by dividing the residuals into equally spaced groups and plotting the frequency of the groups.

• Diagnostic Plots

• • The Box-Cox Transformation* plot can help determine, for the currently selected response and model, what transformation, if any, should be applied. The plot shows the sum of squares of the residuals plotted against lambda. The value of lambda at the minimum point of this curve is considered the "best value" of lambda, and indicates the appropriate transformation, which is also noted by default in the lower title of the plot.