Plasma-glucose levels are used to determine the presence of diabetes. Suppose the mean ln (plasma-glucose) concentration (mg/dL) in 35- to 44-year-olds is 4.86 with standard deviation = 0.54. A study of 100 sedentary people in this age group is planned to test whether they have a higher or lower level of plasma glucose than the general population. 7.10. If the expected difference is 0.20 ln units, then what is the power of such a study if a two-sided test is to be used with α = .05?
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Plasma-glucose levels are used to determine the presence
of diabetes. Suppose the
deviation = 0.54. A study of 100 sedentary people in this age
group is planned to test whether they have a higher or lower
level of plasma glucose than the general population.
7.10. If the expected difference is 0.20 ln units, then what is
the power of such a study if a two-sided test is to be used
with α = .05?
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