By recognizing that your data set consists of smokers and nonsmokers, you gain tremendous power in...

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By recognizing that your data set consists of smokers and nonsmokers, you gain tremendous power in extending conclusions beyond the data by recognizing that these events are mutually exclusive (or disjoint) outcomes, Furthermore, because cach individual can be considered independent of each other and the probability of each youth smoking is the same, you can represent such a random variable X using a binomial probability distribution. Suppose the following validation study is to be completed using 8 randomly selected youth from the East Boston area, where p represents the probability of selecting a smoker from the sample. Use the probability of randomly selecting a smoker from the full data set: p= the probability of selecting a smoker in this sample. to represent Complete the following table for this binomial probability distribution using the binomial formula to determine the missing values. P(X = 0) P(X = 1) %3D P(X = 2) %3D 2. 3. 6. 8. 0.0331 0.0046 0.0004 0.0000 0.0000 0.0000 P(X) By recognizing that your data set consists of smokers and nonsmokers, you gain tremendous power in extending conclusions beyond the data by recognizing that these events are mutually exclusive (or disjoint) outcomes, Furthermore, because cach individual can be considered independent of each other and the probability of each youth smoking is the same, you can represent such a random variable X using a binomial probability distribution. Suppose the following validation study is to be completed using 8 randomly selected youth from the East Boston area, where p represents the probability of selecting a smoker from the sample. Use the probability of randomly selecting a smoker from the full data set: p= the probability of selecting a smoker in this sample. to represent Complete the following table for this binomial probability distribution using the binomial formula to determine the missing values. P(X = 0) P(X = 1) %3D P(X = 2) %3D 2. 3. 6. 8. 0.0331 0.0046 0.0004 0.0000 0.0000 0.0000 P(X)