To determine if chocolate milk was as effective as other carbohydrate replacement drinks, nine male cyclists performed an intense workout followed by a drink and a rest period. At the end of the rest period, each cyclist performed an endurance trial where he exercised until exhausted and time to exhaustion was measured. Each cyclist completed the entire regimen on two different days. On one day the drink provided was chocolate milk and on the other day the drink provided was a carbohydrate replacement drink. Data consistent with summary quantities appear in the table below. (Use a statistical computer package to calculate the P-value. Subtract the carbohydrate replacement times from the chocolate milk times. Round your test statistic to two decimal places, your df down to the nearest whole number, and the P-value to three decimal places.) Time to Exhaustion (minutes) t = df = P = Cyclist 1 2 3 4 5 6 7 8 9 Chocolate Milk 48.90 51.96 38.37 53.33 59.11 49.93 59.09 50.42 52.71 Carbohydrate Replacement 53.93 45.69 19.46 31.38 40.09 18.22 7.52 31.60 10.33 Is there sufficient evidence to suggest that the mean time to exhaustion is greater after chocolate milk than after carbohydrate replacement drink? Use a significance level of 0.05. Yes No
To determine if chocolate milk was as effective as other carbohydrate replacement drinks, nine male cyclists performed an intense workout followed by a drink and a rest period. At the end of the rest period, each cyclist performed an endurance trial where he exercised until exhausted and time to exhaustion was measured. Each cyclist completed the entire regimen on two different days. On one day the drink provided was chocolate milk and on the other day the drink provided was a carbohydrate replacement drink. Data consistent with summary quantities appear in the table below. (Use a statistical computer package to calculate the P-value. Subtract the carbohydrate replacement times from the chocolate milk times. Round your test statistic to two decimal places, your df down to the nearest whole number, and the P-value to three decimal places.) Time to Exhaustion (minutes) t = df = P = Cyclist 1 2 3 4 5 6 7 8 9 Chocolate Milk 48.90 51.96 38.37 53.33 59.11 49.93 59.09 50.42 52.71 Carbohydrate Replacement 53.93 45.69 19.46 31.38 40.09 18.22 7.52 31.60 10.33 Is there sufficient evidence to suggest that the mean time to exhaustion is greater after chocolate milk than after carbohydrate replacement drink? Use a significance level of 0.05. Yes No
Algebra: Structure And Method, Book 1
(REV)00th Edition
ISBN:9780395977224
Author:Richard G. Brown, Mary P. Dolciani, Robert H. Sorgenfrey, William L. Cole
Publisher:Richard G. Brown, Mary P. Dolciani, Robert H. Sorgenfrey, William L. Cole
Chapter2: Working With Real Numbers
Section2.3: Rules For Addition
Problem 8P
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Transcribed Image Text:To determine if chocolate milk was as effective as other carbohydrate replacement drinks, nine male cyclists performed an intense workout followed by a drink and a rest period. At the
end of the rest period, each cyclist performed an endurance trial where he exercised until exhausted and time to exhaustion was measured. Each cyclist completed the entire regimen on
two different days. On one day the drink provided was chocolate milk and on the other day the drink provided was a carbohydrate replacement drink.
Data consistent with summary quantities appear in the table below. (Use a statistical computer package to calculate the P-value. Subtract the carbohydrate replacement times from the
chocolate milk times. Round your test statistic to two decimal places, your df down to the nearest whole number, and the P-value to three decimal places.)
Time to Exhaustion (minutes)
t =
df =
P =
Cyclist
1 2 3
4 5
6
7
8 9
Chocolate
Milk
48.90 51.96 38.37 53.33 59.11 49.93 59.09 50.42 52.71
Carbohydrate
Replacement
53.93 45.69 19.46 31.38 40.09 18.22 7.52 31.60 10.33
Is there sufficient evidence to suggest that the mean time to exhaustion is greater after chocolate milk than after carbohydrate replacement drink? Use a significance level of 0.05.
Yes
No
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