An article about the Califormia lattery gave the following information on the age distribution of adults in California: 35% are between 18 and 34 years old, 51% are between 35 and 64 years old, and 14% are 65 years old or alder. The artide also gave Information on the age distribution of those who purchase lattery tickets. The following table is consistent with the values given in the article. Suppose that the data resulted from a random sample of 200 lattery ticket purchasers. Based on these sample data, is it reasonable to condude that ane ar more af these three age groups buys a disproportionate share af lottery tickets? Use a chi-square goodness-of-fit test with a- 0.05. (Round your answer to two decimal places.) Age of Purchaser Frequency 18-34 35-64 65 and over 45 100 55 P-value interval Op< 0.001 O 0.001 sp 0.01 O 0.01 sp< 0.05 O 0.05 sp< 0.10 Op20.10 The data Sekci strong evidence to conclude that one or more of the three age groups buys a disproportionate share of lottery tickets.
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!
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