A manufacturing engineer wants to understand the sources of variability in the manufacture of glass jars. The engineer's company manufactures the glass jars at four locations. Four operators measure furnace temperatures in three batches over four shifts from the four locations.

The operators at each plant are different, so the operator factor is nested in the plant factor. While each shift number represents the same part of the workday, the shifts that each operator works at the same plant are different. Thus, shift is nested in operator. Also, the batch of material that the operators use changes each shift. Thus, batch is nested in shift. Because of the nesting pattern, the engineer uses fully nested ANOVA so that the model specification in Minitab is easier.

The ANOVA table indicates that the main effects for plant and shift are statistically significant at the 0.05 significance level. The operator effect is not statistically significant at the 0.05 level. The variance component estimates indicate that the variability attributable to batches, shifts, and plants was 52%, 27%, and 18%, respectively, of the total variability.

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