Confounding often defeats attempts to show one variable causes changes in another variable. Confounding means that:    a) the effects of two or more variables are mixed up, so we cannot say which is causing the response.            b) we don’t know which is the response variable and which is the explanatory variable.            c) we would get widely varied results if we repeated the study many times.            d) we have scheduled too many treatments for the number of subjects we have.

Q: data: 10 female endurance athletes ages: 13.6, 13.9, 14.0, 14.2, 14.9, 15.0, 150, 15.1,15.4, 16.4…

A: 13.6,13.9,14.0,14.2,14.9,15.0,15.0,15.1,15.4,16.4sample size(n)=10

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A:

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Q: (d) n = 5, x = 4, p = 0.2 p(x) =

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A: We have given thatP(A) = 0.3 P( B) = 0.2P(A and B) = 0.05

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A:

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A: Here, , , and .

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A: Data are X: 2,6,15,3,12,10,11,8,5,9xx2246361522539121441010011121864525981∑x=81∑x2 =809

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A: xx2131691112115225183241625614196981864

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A: The given data is as follows:313.6, 151.6, 598.5, 222.7, 293.9, 272.6, 198.5, 382.0, 188.7

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Q: what about d

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