Week 6 Discussion: Analysis of Variance This week, we are studying Analysis of V

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Week 6 Discussion: Analysis of Variance
This week, we are studying Analysis of Variance, commonly called ANOVA. ANOVA is used the compare the mean of a dependent variable associated with the effect of at least three independent variables. For example, we could study the mean sodium amount in condiments, desserts, and cereal. In this case, the dependent variable is the amount of sodium and the independent variables are condiments, desserts, and cereal. Note: The hypothesis test using ANOVA will tell you if the mean is the same for the samples but does not tell you which is different.
To prepare for this Discussion:
Review the Week 6 Discussion Resources
To do this week’s Discussion, you will need to have data involving at least three qualitative variables. You may want to start over with a new data set, as shown in the example below.
Example: Penicillin is produced by the penicillium fungus, which is grown in a broth whose sugar content must be monitored carefully. Several samples of the broth are taken on successive days and the amount of sugar was measured in milligrams per milliliter. The results are given in the following table. I want to test whether the mean concentration of sugars differs among the 3 days. In this case, the dependent variable is the amount of sugar and the independent variables are the day.
Day 1 Day 2 Day 3
4.8 5.0 5.7
5.2 5.4 5.5
4.0 5.2 5.2
5.0 5.1 5.4
4.8 5.2 5.1
5.1 5.3 5.5
4.9 5.0 5.3
4.8 5.1 5.4
5.0 5.1 5.5
4.9 5.3 5.6
5.2 5.2 5.3
5.0 5.1 5.7
5.1 5.4 5.1
4.8 5.2 5.6
Review the rubric that will be used for grading.
With these thoughts in mind:
By Day 4
Write 1–2 paragraphs that include the following:
Describe your scenario and provide your dataset.
State your null and alternative hypotheses. Be sure to state your chosen level of significance.
Enter your data into the Sample Editor in Statdisk. Choose Analysis, One-Way Analysis of Variance.
Perform the test and state your conclusion. Remember the conclusion statement should be of the following format: Since the p-value of # is more/less than the level of significance of #, the null hypothesis is/is not rejected; therefore, the data supports ( paraphrase the hypothesis supported).

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