(a) Perform a One-way ANOVA test, and find the resulting ANOVA table and resulting P-value. (Don't worry about comparing P to an a value.) Hint: the F ratio you find is one that appears in Table 9, so you should not have to bracket the P value. Show all pertinent work in computing the values in your table-don't just find and use a plug One-way ANOVA tool that computes all of the values for you.

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Chapter1: Starting With Matlab
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5. The following problem illustrates that One-way ANOVA (for comparing means of two or more
samples) is a generalization of the t-test (for comparing means of two samples). In both tests,
we compare the variation between the samples to the variation within the samples. In particular,
although we normally use One-way ANOVA for comparing three or more samples (since for
two samples we could simply use the t-test), we could actually use it for comparing two samples.
So here is the problem. Consider the following data from two samples. We are testing the
hypothesis that the two corresponding populations have the same mean Ho: H1 = H2. As you
compute the various values below, DO NOT ROUND the values that you compute.
n
10
10
25
19.05
S
10
10
s2
100
100
(a) Perform a One-way ANOVA test, and find the resulting ANOVA table and resulting P-value.
(Don't worry about comparing P to an a value.) Hint: the F ratio you find is one that appears
in Table 9, so you should not have to bracket the P value. Show all pertinent work in computing
the values in your table don't just find and use a plug One-way ANOVA tool that computes
all of the values for you.
(b) Now perform a t-test and find the resulting P-value. (Again, don't worry about comparing it
to an a value.) Hint: since you are using the same data as in (a), you should end up finding
the same P-value that you found in (a). Use df = 18.
Transcribed Image Text:5. The following problem illustrates that One-way ANOVA (for comparing means of two or more samples) is a generalization of the t-test (for comparing means of two samples). In both tests, we compare the variation between the samples to the variation within the samples. In particular, although we normally use One-way ANOVA for comparing three or more samples (since for two samples we could simply use the t-test), we could actually use it for comparing two samples. So here is the problem. Consider the following data from two samples. We are testing the hypothesis that the two corresponding populations have the same mean Ho: H1 = H2. As you compute the various values below, DO NOT ROUND the values that you compute. n 10 10 25 19.05 S 10 10 s2 100 100 (a) Perform a One-way ANOVA test, and find the resulting ANOVA table and resulting P-value. (Don't worry about comparing P to an a value.) Hint: the F ratio you find is one that appears in Table 9, so you should not have to bracket the P value. Show all pertinent work in computing the values in your table don't just find and use a plug One-way ANOVA tool that computes all of the values for you. (b) Now perform a t-test and find the resulting P-value. (Again, don't worry about comparing it to an a value.) Hint: since you are using the same data as in (a), you should end up finding the same P-value that you found in (a). Use df = 18.
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