The "fun size" of a Snickers bar is supposed to weigh 20 grams. Because the penalty for selling candy bars under their advertised weight is severe, the manufacturer calibrates the machine so the mean weight is 20.1 grams. The quality-control engineer at M\&M-Mars, the Snickers manufacturer, is concerned about the calibration. He obtains a random sample of 11 candy bars, weighs them, and obtains the data shown in Table 1. Should the machine be shut down and calibrated? Because shutting down the plant is very expensive, he decides to conduct the test at the \(\alpha=0.01\) level of significance.

Approach Assume that the machine is calibrated correctly. So there is no difference between the actual mean weight and the calibrated weight of the candy. We want to know whether the machine is incorrectly calibrated, which would result in a mean weight that is too high or too low. Therefore, this is a two-tailed test.
Before performing the hypothesis test, verify that the data come from a population that is normally distributed with no outliers by constructing a normal probability plot and boxplot. Then proceed to follow Steps 1 through 5.

Transcribed Image Text:

Table 1 19.68 20.66 19.56 19.98 20.65 19.61 20.55 20.36 21.02 21.50 19.74