Example of a prediction for a regression modelmain topic interpreting results session command see also
Example of a prediction for a regression modelAs part of a test of solar energy, you measure the total heat flux from homes. In the multiple regression example, you determined that the total heat flux (HeatFlux) can be predicted by the position of the focal points in the east, south, and north directions. Now, you want to use this model to predict HeatFlux for specific values of the predictors East, South, and North.
You do not need to re-analyze that regression model. The worksheet contains the model for the prediction.
1 Open the worksheet SOLARENERGY_MODEL.MTW.
2 Choose Stat > Regression > Regression > Predict.
3 In Response, choose HeatFlux.
4 In the second drop-down list, choose Enter individual values.
5 In the variables table, enter the setting for each variable as shown below.
|
East |
South |
North |
|
36 |
33 |
18 |
6 Click OK.
Session Window Output
Prediction for HeatFlux
Regression Equation
HeatFlux = 389.2 + 2.12 East + 5.318 South - 24.13 North
Variable Setting East 36 South 33 North 18
Fit SE Fit 95% CI 95% PI 206.783 4.14474 (198.247, 215.320) (187.126, 226.441) |
Minitab uses the stored model to calculate that the predicted response value (fit) for the specified predictor values is 206.783.
Additionally, the confidence interval indicates that you can be 95% confident that the mean of the heat flux at these predictor values is between 198.247 and 215.320. The prediction interval indicates that you can be 95% confident that a single new observation will fall between 187.126 and 226.441.
The prediction interval is always wider than the corresponding confidence interval because of the added uncertainty involved in predicting a single response versus the mean response.
This prediction is based on a model equation. Ensure that your model is adequate before interpreting the results.