Interpret the key results for Predict

First, determine the predicted mean of the response. Then, examine the prediction interval to determine a range of likely values for a single future value.

The fitted response value (fit) is the point estimate for the specified variable settings.

The prediction interval (PI) is a range that is likely to contain a single future response for a specified combination of variable settings. If you collect another data point at the same variable settings, the new data point is likely to be within the prediction interval. Narrower prediction intervals indicate a more precise prediction.

The type of fitted response values that Minitab displays depends on the type of response variable in your model. Minitab displays the following types of fitted values:
  • Means for response variables that contain continuous measurements, such as length or weight.
  • Means for response variables that contain counts that follow the Poisson distribution, such as the number of defects per sample.
  • Probabilities for response variables that contain only two possible outcomes, such as pass/fail.
  • Standard deviations for models that are fit using Analyze Variability.

Prediction for Heat Flux

Regression Equation Heat Flux = 389.2 + 2.12 East + 5.318 South - 24.13 North
Settings Variable Setting East 35 South 34 North 16
Prediction Fit SE Fit 95% CI 95% PI 258.242 2.35414 (253.393, 263.090) (239.882, 276.601)
Key Results: Fit, 95% PI

In these results, the fit is 258.242, which is the fitted mean of the heat flux when East is 35, South is 34, and North is 16. At these settings, the 95% prediction interval is 239.882 to 276.601.

Use your knowledge of the process to determine whether the prediction interval falls inside acceptable boundaries.

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