437
For Exercise 18.1, see page 431; for Exercise 18.2, see page 434.
18.3 Probability models A probability model describes a random phenomenon by telling us which of the following?
(a) Whether we are using data based or personal probabilities.
(b) What outcomes are possible and how to assign probabilities to these outcomes.
(c) Whether the probabilities of all outcomes sum to 1 or sum to a number different than 1.
(d) All of the above.
18.4 Probability models. Which of the following is true of any legitimate probability model?
(a) The probabilities of the individual outcomes must be numbers between 0 and 1, and they must sum to no more than 1.
(b) The probabilities of the individual outcomes must be numbers between 0 and 1, and they must sum to at least 1.
(c) The probabilities of the individual outcomes must be numbers between 0 and 1, and they must sum to exactly 1.
(d) Probabilities can be computed using the Normal curve.
18.5 Density curves. Which of the following is true of density curves?
(a) Areas under a density curve determine probabilities of outcomes.
(b) The total area under a density curve is 1.
(c) The Normal curve is a density curve.
(d) All of the above are true.
18.6 Probability rules. To find the probability of any event,
(a) add up the probabilities of the outcomes that make up the event.
(b) use the probability of the outcome that best approximates the event.
(c) assign it a random, but plausible, value between 0 and 1.
(d) average together the personal probabilities of several experts.
18.7 Sampling distributions. The sampling distribution of a statistic is
(a) the method of sampling used to obtain the data from which the statistic is computed.
(b) the possible methods of computing a statistic from the data.
(c) the pattern of the data from which the statistic is computed.
(d) the pattern of values of the statistic in many samples from the same population.