Interpret the key results for Predict for Fit Mixed Effects Model

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.

Use the marginal prediction interval when you don't know the actual levels of the random factors. Use the conditional prediction interval when you know the specific combination of the random factor settings.

Prediction for Yield

Mixed Effects Model Information Terms Field Variety
Settings Variable Setting Field 1 Variety 1
Prediction Type Fit SE Fit CI DF 95% CI PI DF 95% PI Conditional 3.885 0.103 15.58 (3.666, 4.104) 15.16 (3.462, 4.309) Marginal 3.480 0.163 4.92 (3.058, 3.902) 4.92 (2.536, 4.424) X X denotes an unusual point relative to predictor levels used to fit the model.
Key Results: Fit, 95% PI

In these results, Minitab uses the conditional equation and the marginal equation obtained from the stored model to calculate the two types of fits. The conditional fit of 3.885 represents the mean yield of planting variety 1 of alfalfa in field 1. The marginal fit of 3.480 is the mean yield of planting variety 1 of alfalfa in a randomly selected field in the future.

The prediction intervals indicate that you can be 95% confident that a single new yield for variety 1 of alfalfa from field 1 will be between 3.462 and 4.309, and a single new yield for variety 1 of alfalfa from a randomly selected field will be between 2.536 and 4.424.

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

By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy