Question

CB Industries operates three shifts every day of the week. Each shift includes full-time hourly workers, nonsupervisory salaried employees, and supervisors/ managers. CB Industries would like to know if there is a difference among the shifts in terms of the number of hours of work missed due to employee illness. To control for differences that might exist across employee groups, CB Industries randomly selects one employee from each employee group and shift and records the number of hours missed for one year. The results of the study are shown here: a. Develop the appropriate test to determine whether blocking is effective or not. Conduct the test at the $\alpha=0.05$ level of significance. b. Develop the appropriate test to determine whether there are differences in the average number of hours missed due to illness across the three shifts. Conduct the test at the $\alpha=0.05$ level of significance. c. If it is determined that a difference in the average hours of work missed due to illness is not the same for the three shifts, use the $L S D$ approach to determine which shifts have different means.

   CB Industries operates three shifts every day of the week. Each shift includes full-time hourly workers, nonsupervisory salaried employees, and supervisors/ managers. CB Industries would like to know if there is a difference among the shifts in terms of the number of hours of work missed due to employee illness. To control for differences that might exist across employee groups, CB Industries randomly selects one employee from each employee group and shift and records the number of hours missed for one year. The results of the study are shown here:

a. Develop the appropriate test to determine whether blocking is effective or not. Conduct the test at the $\alpha=0.05$ level of significance.
b. Develop the appropriate test to determine whether there are differences in the average number of hours missed due to illness across the three shifts. Conduct the test at the $\alpha=0.05$ level of significance.
c. If it is determined that a difference in the average hours of work missed due to illness is not the same for the three shifts, use the $L S D$ approach to determine which shifts have different means.
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Business Statistics
Business Statistics
David F. Groebner,… 8th Edition
Chapter 12, Problem 25 ↓

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## Two-Way ANOVA with Blocking  Show more…

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CB Industries operates three shifts every day of the week. Each shift includes full-time hourly workers, nonsupervisory salaried employees, and supervisors/ managers. CB Industries would like to know if there is a difference among the shifts in terms of the number of hours of work missed due to employee illness. To control for differences that might exist across employee groups, CB Industries randomly selects one employee from each employee group and shift and records the number of hours missed for one year. The results of the study are shown here: a. Develop the appropriate test to determine whether blocking is effective or not. Conduct the test at the $\alpha=0.05$ level of significance. b. Develop the appropriate test to determine whether there are differences in the average number of hours missed due to illness across the three shifts. Conduct the test at the $\alpha=0.05$ level of significance. c. If it is determined that a difference in the average hours of work missed due to illness is not the same for the three shifts, use the $L S D$ approach to determine which shifts have different means.
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Key Concepts

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Hypothesis Testing
Hypothesis testing involves formulating a null hypothesis that there is no effect or difference among groups and an alternative hypothesis that there is an effect. Statistical tests, such as those based on ANOVA, are used to determine whether the observed data are sufficiently inconsistent with the null hypothesis, based on a predetermined significance level.
Blocking
Blocking is an experimental design technique used to reduce the influence of confounding variables by grouping similar experimental units together. By forming blocks, researchers can control for variability among subjects that might affect the outcome, thereby isolating the effect of the primary factor under investigation.
Randomized Complete Block Design (RCBD)
A Randomized Complete Block Design is a design method where each block, which consists of homogeneous experimental units, receives all treatments randomly. This approach ensures that comparisons among treatments are made within blocks, thereby accounting for systematic variability, and improves the precision of the experiment.
Analysis of Variance (ANOVA)
ANOVA is a statistical method used for comparing means across multiple groups or treatments to determine if there are statistically significant differences among them. In the context of a blocked design, ANOVA partitions the total variance into variance due to blocks, treatments, and error, allowing for effective hypothesis testing regarding differences among treatment means.
Least Significant Difference (LSD) Test
The Least Significant Difference (LSD) test is a post-hoc analysis method used after finding a significant effect in ANOVA. It compares all pairwise differences between group means to determine which specific means differ significantly, providing detailed insight into where the differences lie among the groups.

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D&G Industries operates three shifts every day of the week. Each shift includes full-time hourly workers, nonsupervisory salaried employees, and supervisors/managers. D&G Industries would like to know if there is a difference among the shifts in terms of the number of hours of work missed due to employee illness. To control for differences that might exist across employee groups, D&G Industries randomly selects one employee from each employee group and shift and records the number of hours missed for one year. The results of the study are shown here: Shift 1 Hourly: 48 Nonsupervisory: 31 Supervisor/managers: 25 Shift 2 Hourly: 54 Nonsupervisory: 36 Supervisor/managers: 33 Shift 3 Hourly: 60 Nonsupervisory: 55 Supervisor/managers: 40 a. Develop the appropriate test to determine whether blocking is effective or not. Conduct the test at the α=0.05 level of significance. b. Develop the appropriate test to determine whether there are differences in the average number of hours missed due to illness across the three shifts. Conduct the test at the α=0.05 level of significance. c. If it is determined that the average hours of work missed due to illness are not the same for the three shifts, use the LSD approach to determine which shifts have different means.

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