Here is the ERRORS data set.

\fWhen conducting tests such as: Hy:pyg pp =0 for a two-sample t-test, we MUST first check population variances. Because we use the pivotal statistic F - which in turn is a ratio of independent chi-sguares the result is a function of the ratio of variances. For this reason, we need to test Hy: o} jo} =1 Please read MS page 420-423. Human Inspection Errors (Taken from M5 B.83) Analyzing human inspection errors. Tests of product quality using human inspectors can lead to serious inspection error problems (Journal of Quality Technology). To evaluate the performance of inspectors in a new company, a quality manager had a sample of 12 novice inspectors evaluate 200 finished products. The same 200 items were evaluated by 12 experienced inspectors. The guality of each itemwhether defective or non- defectivewas known to the manager. The next table lists the number of inspection errors (classifying a defective item as nondefective or vice versa) made by each inspector. Prior to conducting this experiment, the manager believed the variance in inspection errors was lower for experienced inspectors than for novice inspectors. First some preliminary summary information: Please use the ERRORS data > error ExX group_by{Inspector} 3% summarise{var = var(Errcrs}, n = n{)) # A tibble: 2 x 3 Inspector wvar n