Interpret the key results for Analyze Definitive Screening Design

Complete the following steps to analyze a screening design. Key output includes the Pareto chart, p-values, the coefficients, model summary statistics, and the residual plots.

Step 1: Determine which terms contribute the most to the variability in the response

Use a Pareto chart of the effects to compare the relative magnitude and the statistical significance of the effects. The chart displays the absolute values of the standardized effects.

Minitab plots the effects in the decreasing order of their absolute values. The reference line on the chart indicates which effects are statistically significant.

Key Results: Pareto Chart

In these results, terms that are in the model have blue bars. Terms that are not in the model have gray bars. The plot shows that 2 main effects are statistically significant. A quadratic term and an interaction effect are also significant. The main effects that are parts of the interaction and the quadratic term are in the model, even though these effects are not statistically significant.

In addition, you can see that the largest effect is A because it extends the farthest. The effect for the EE quadratic term is the smallest effect on the chart.

Step 2: Determine which terms have statistically significant effects on 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. The null hypothesis is that the term's coefficient is equal to zero, which implies that there is no association between the term and the response. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that an association exists when there is no actual association.
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.
P-value > α: The association is not statistically significant
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. You may want to refit the model without the term.
If there are multiple predictors without a statistically significant association with the response, you can reduce the model by removing terms one at a time. For more information on removing terms from the model, go to Model reduction.
If a coefficient is statistically significant, the interpretation depends on the type of term. The interpretations are as follows:
Factors
If the coefficient for a factor is statistically significant, you can conclude that the coefficient for the factor does not equal 0.
Interactions among factors
If the coefficient for an interaction is statistically significant, you can conclude that the relationship between a factor and the response depends on the other factors in the term.
Quadratic terms
If the coefficient for a quadratic term is statistically significant, you can conclude that the response surface contain curvature.
Covariates
If the coefficient for a covariate is statistically significant, you can conclude that the association between the response and the covariate is statistically significant.
Blocks
If the coefficient for a block is statistically significant, you can conclude that the mean of the response values in that block is different from the overall mean of the response.
The VIF values for the quadratic term and the interaction term are not 1, which shows the presence of multicollinearity. For more information, go to Coefficients table for Analyze Definitive Screening Design and click VIF.

Screening design model: Power versus Train, Burst, Quiet, Center, Sweep

Coded Coefficients Term Coef SE Coef T-Value P-Value VIF Constant 617.1 15.0 41.16 0.000 Train 52.41 6.53 8.02 0.000 1.00 Burst 8.62 6.53 1.32 0.220 1.00 Quiet -39.59 6.53 -6.06 0.000 1.00 Center -2.36 6.53 -0.36 0.727 1.00 Sweep 2.84 6.53 0.44 0.674 1.00 Sweep*Sweep 49.4 16.7 2.95 0.016 1.16 Burst*Center 24.63 7.59 3.25 0.010 1.16
Key Results: P-Value, Coefficients

In these results, the main effects for Train and Quiet are statistically significant at the 0.05 level. You can conclude that changes in these variables are associated with changes in the response variable.

The main effect for Sweep is not statistically significant but the quadratic effect is. You can conclude that changes in this variable are associated with changes in the response variable, but the association is not linear.

The main effects for Burst and Center are not statistically significant, but the interaction effect is. You can conclude that changes in these variables are associated with changes in the response variable, but the effects depend on the other factor.

Step 3: Determine how well the model fits your data

To determine how well the model fits your data, examine the goodness-of-fit statistics in the Model Summary table.

S

Use S to assess how well the model describes the response.

S is measured in the units of the response variable and represents how far the data values fall from the fitted values. The lower the value of S, the better the model describes the response. However, a low S value by itself does not indicate that the model meets the model assumptions. You should check the residual plots to verify the assumptions.

R-sq

The higher the R2 value, the better the model fits your data. R2 is always between 0% and 100%.

R2 always increases when you add additional predictors to a model. For example, the best five-predictor model will always have an R2 that is at least as high as the best four-predictor model. Therefore, R2 is most useful when you compare models of the same size.

R-sq (adj)

Use adjusted R2 when you want to compare models that have different numbers of predictors. R2 always increases when you add a predictor to the model, even when there is no real improvement to the model. The adjusted R2 value incorporates the number of predictors in the model to help you choose the correct model.

R-sq (pred)

Use predicted R2 to determine how well your model predicts the response for new observations. Models that have larger predicted R2 values have better predictive ability.

A predicted R2 that is substantially less than R2 may indicate that the model is over-fit. An over-fit model occurs when you add terms for effects that are not important in the population. The model becomes tailored to the sample data and, therefore, may not be useful for making predictions about the population.

Predicted R2 can also be more useful than adjusted R2 for comparing models because it is calculated with observations that are not included in the model calculation.

Consider the following points when you interpret the R2 values:
  • Small samples do not provide a precise estimate of the strength of the relationship between the response and predictors. If you need R2 to be more precise, you should use a larger sample (typically, 40 or more).
  • R2 is just one measure of how well the model fits the data. Even when a model has a high R2, you should check the residual plots to verify that the model meets the model assumptions.
Key Results: S, R-sq, R-sq (adj), R-sq (pred)

In these results, the model explains 93.68% of the variation. For these data, the R2 value indicates the model provides a good fit to the data. If additional models are fit with different terms, use the adjusted R2 values and the predicted R2 values to compare how well the models fit the data.

Screening design model: Power versus Train, Burst, Quiet, Center, Sweep

Model Summary S R-sq R-sq(adj) R-sq(pred) 24.4482 93.68% 88.77% 76.78%

Step 4: Determine whether your model meets the assumptions of the analysis

Use the residual plots to help you determine whether the model is adequate and meets the assumptions of the analysis. If the assumptions are not met, the model may not fit the data well and you should use caution when you interpret the results.

For more information on how to handle patterns in residual plots, go to Residual plots for Analyze Definitive Screening Design and click the name of the residual plot in the list at the top of the page.

Residuals versus fits plot

The patterns in the following table may indicate that the model does not meet the model assumptions.
Pattern What the pattern may indicate
Fanning or uneven spreading of residuals across fitted values Nonconstant variance
Curvilinear A missing higher-order term
A point that is far away from zero An outlier
A point that is far away from the other points in the x-direction An influential point

Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.

Residuals versus order plot

Use the residuals versus order plot to verify the assumption that the residuals are independent from one another. Independent residuals show no trends or patterns when displayed in time order. Patterns in the points may indicate that residuals near each other may be correlated, and thus, not independent. Ideally, the residuals on the plot should fall randomly around the center line:
If you see a pattern, investigate the cause. The following types of patterns may indicate that the residuals are dependent.
Trend
Shift
Cycle

Normal probability plot of the residuals

Use the normal probability plot of the residuals to verify the assumption that the residuals are normally distributed. The normal probability plot of the residuals should approximately follow a straight line.

The patterns in the following table may indicate that the model does not meet the model assumptions.

Pattern What the pattern may indicate
Not a straight line Nonnormality
A point that is far away from the line An outlier
Changing slope An unidentified variable
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