A statistics instructor wonders whether significant differences exist in her students’ final exam scores in her three different sections. She randomly selects the scores from 10 students in each section. A portion of the data is shown in the accompanying table. Assume exam scores are normally distributed.
Section 1 | Section 2 | Section 3 |
60 | 75 | 86 |
94 | 64 | 68 |
56 | 73 | 65 |
57 | 71 | 77 |
60 | 83 | 91 |
86 | 67 | 79 |
66 | 76 | 94 |
85 | 70 | 56 |
82 | 80 | 50 |
63 | 72 | 86 |
Section | Score |
Section 1 | 60 |
Section 1 | 94 |
Section 1 | 56 |
Section 1 | 57 |
Section 1 | 60 |
Section 1 | 86 |
Section 1 | 66 |
Section 1 | 85 |
Section 1 | 82 |
Section 1 | 63 |
Section 2 | 75 |
Section 2 | 64 |
Section 2 | 73 |
Section 2 | 71 |
Section 2 | 83 |
Section 2 | 67 |
Section 2 | 76 |
Section 2 | 70 |
Section 2 | 80 |
Section 2 | 72 |
Section 3 | 86 |
Section 3 | 68 |
Section 3 | 65 |
Section 3 | 77 |
Section 3 | 91 |
Section 3 | 79 |
Section 3 | 94 |
Section 3 | 56 |
Section 3 | 50 |
Section 3 | 86 |
a. Construct an ANOVA table. (Round "Sum Sq" and "Mean Sq" to 1 decimal place, "F value" to 3, and "p-value" to 3 decimal places. Before fitting your model, type options(scipen=10) and options(digits=10) into your R console.)
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