Stat 231 Homework 9
This homework requires the use of JMP software. Iowa State students can download it for
free at http://www.stat.iastate.edu/resources-2/software-sasjmpr/statistical-software-jmp/ or
it can be found at various computer labs on campus
1. For the following ANOVA table, which was based on a one-way normal model with
I = 5 groups and n = 20 total observations (4 per group) Source DF Sum of Squares Mean Squares F Test Statistic
(a) Fill in the missing parts of the ANOVA table.
(b) Calculate ^ = sp
(c) Calculate the overall F-test statistic
(d) Calculate (or give a range for) the p-value
(e) Which item on the ANOVA table represents the \between-group variation?"
Which item on the ANOVA table represents the \within-group variation?"
(f) Calculate a 95% con dence interval for the common variance 2 (you do not need to interpret)
2. Problem 25, Devore, Chapter 10. Page 434. You can calculate the overall mean as
Also answer the following questions:
(c) Write out the full ANOVA table for this problem.
(d) Calculate and interpret a 95% (standard) con dence interval for the di erence
in mean total polyunsaturated fats between the CO group and the SMO group.
3. A consumer testing agency tested the lifetimes of ve brands of dot-matrix computer printer ribbons using a single printer. The data is in the le \Printer Ribbon Lifetime Data.JMP"
(a) Is there signi cant evidence that at least one printer ribbon brand has a di erent mean lifetime than the rest? Answer using a hypothesis test. Show the whole seven-step process. You do not need to show calculations - you can pull numbers directly from the ANOVA table in JMP.
(b) Which brands appear to have the same mean printer ribbon lifetimes? Which brands appear to have di erent mean printer ribbon lifetimes? Use JMP to nd Tukey intervals. Give a lines/letters report.
(c) Calculate and interpret a 95% con dence interval for the average of mean rib- bon lifetimes for brands C and E minus the average of mean ribbon lifetimes for brands A and D.
4. The le \2014 Baseball Data.JMP" contains the number of runs allowed for each team and each game of the 2014 major league baseball season.
(a) Is this data a good t for the one-way normal model? Explain why or why not.
(b) Regardless of what you decided in the previous part, is there evidence that some teams tend to give up more runs others? Report the ANOVA test p-value (you do not need to do the full hypothesis test, just give the p-value)
(c) Which teams are the most di erent in terms of the number of runs allowed? Cal- culate Tukey intervals and give the 10 most signi cant comparisons, reporting the corresponding p-value.
(For example - the rst response will be COL-SEA, p-value = 0.0001)
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- Submitted On 04 Apr, 2015 08:49:58