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Starch Out
Determine whether sand blasting significantly affects starch removal in jeans. Treadmill Exercise
Determine whether stretching and wearing ankle weights affects exercise on treadmills. |
Starch Out: Problem |
Denim manufacturers are concerned with maximizing the comfort of the jeans they make. In the manufacturing process, starch is often built up in the fabric, creating stiff jeans that must be “broken in” by the wearer before they are comfortable. To minimize the break-in time for each pair of pants, the manufacturers often wash the jeans to remove as much starch as possible. In a particular study to evaluate starch removal, data was recorded including the washing method (3 types), size of the load (in pounds), whether or not the fabric was sand blasted prior to washing, and the starch content of the fabric after washing.
Carry out a two-way analysis of variance to examine if the interaction between washing method and whether or not the fabric was sand blasted has a significant effect on the removal of starch.
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Lee Creighton (modified by Paris Faison)
SAS Institute
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Starch Out: Sample Data | |
The Denim data set contains the four columns that are described below. These are the variables in the data set: Name | Type | Description | | Method | char | method used in washing | | Size of Load | num | size in pounds | | Sand Blasted | char | whether or not the fabric was sand blasted before washing | | Starch Content | num | the starch content of the fabric after washing | |
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Source of Data
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Sall, J., Creighton, L., & Lehman, A. (2006). JMP Start Statistics, Third Edition. Cary, NC: SAS Institute Inc. |
Starch Out: Solution |
The value of the F statistic for the interaction effect Method*Sand_blasted_ is 1.324 with a corresponding p-value of 0.271, which is not significant at a level as high as α = 0.10. So we can conclude that the interaction between washing method and sand blasting does not have a significant effect on the removal of starch from jeans. These results were given by the output from the GLM procedure in SAS.
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Treadmill Exercise: Problem |
An exercise physiologist wants to examine whether stretching and wearing ankle weights affect the value of exercise on treadmills. To carry out her study, she recruits subjects who have roughly the same level of physical fitness, and divides them randomly into four groups: with or without ankle weighs, and with or without a stretching period before the exercise. Using the amount of calories burned as the response, carry out a two-way ANOVA to determine whether stretching and wearing ankle weights have significant effects on exercise. |
SAS Institute Inc.
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Treadmill Exercise: Sample Data | |
The Exercise data set contains data about the effects of stretching and wearing ankle weights on treadmill exercise. These are the variables in the data set: Name | Type | Description | | PreStretch | char | stretch group (Stretch, No stretch) | | AnkleWeights | char | weights group (Weights, No weights) | | Energy | num | calories burned | | Speed | num | average speed (in meters per minute) | | Oxygen | num | oxygen consumed (in liters) | |
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Source of Data
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This data is sample data from SAS Institute Inc. |
Treadmill Exercise: Solution |
It appears that the two-way model is significant, based on the p-value of 0.0008. The results of the F-tests for type I and type III sums of squares are equivalent in this case. Based on these, with p-values of 0.0045 and 0.0032, we can say that there is sufficient evidence to claim that stretching and wearing ankle weights both have significant effects on exercise benefit (in terms of burning calories). |
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