The following table shows age distribution and location of a random sample of 166 buffalo in a national park. Age Lamar District Nez Perce District Firehole District Row Total Calf 15 13 13 41 Yearling 10 12 11 33 Adult 35 31 26 92 Column Total 60 56 50 166 (A) Find the value of the chi-square statistic for the sample. (Round the expected frequencies to at least three decimal places. Round the test statistic to three decimal places.)
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!
The following table shows age distribution and location of a random sample of 166 buffalo in a national park.
| Age | Lamar District | Nez Perce District | Firehole District | Row Total |
| Calf | 15 | 13 | 13 | 41 |
| Yearling | 10 | 12 | 11 | 33 |
| Adult | 35 | 31 | 26 | 92 |
| Column Total | 60 | 56 | 50 | 166 |
(A) Find the value of the chi-square statistic for the sample. (Round the expected frequencies to at least three decimal places. Round the test statistic to three decimal places.)
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