Trying to find variance. his equation is Prob(x)*[x-e(x)]^2. Just need an example to get me started. Original question is: A call center recieves an average of 10 calls per hour. Assuming the number of calls received follows the poisson distribution, determine the probability of each of the discrete outcomes below. Then calculate the variance component for each one using the standard formula for the variance of a discrete random variable. At the end, take the sum of both columns. Table: Poisson Distribution Table Calls Density Variance 0 0.00% 1 0.05% 2 0.23% 3 0.76% 4 1.89% 5 3.78% 6 6.31% 7 9.01% 8 11.26% 9 12.51% 10 12.51% 11 11.37% 12 9.48% 13 7.29% 14 5.21% 15 3.47% 16 2.17% 17 1.28% 18 0.71% 19 0.37% 20 0.19% 21 0.09% 22 0.04% 23 0.02% 24 0.01% 25 0.00% 26 0.00% 27 0.00% 28 0.00% 29 0.00% 30 0.00% Sum 100.00%
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!
Trying to find variance. his equation is Prob(x)*[x-e(x)]^2. Just need an example to get me started.
Original question is:
| A call center recieves an average of 10 calls per hour. Assuming the number of calls received follows the poisson distribution, determine the probability of each of the discrete outcomes below. Then calculate the variance component for each one using the standard formula for the variance of a discrete random variable. At the end, take the sum of both columns. |
Table:
| Poisson Distribution Table | ||
| Calls | Density | Variance |
| 0 | 0.00% | |
| 1 | 0.05% | |
| 2 | 0.23% | |
| 3 | 0.76% | |
| 4 | 1.89% | |
| 5 | 3.78% | |
| 6 | 6.31% | |
| 7 | 9.01% | |
| 8 | 11.26% | |
| 9 | 12.51% | |
| 10 | 12.51% | |
| 11 | 11.37% | |
| 12 | 9.48% | |
| 13 | 7.29% | |
| 14 | 5.21% | |
| 15 | 3.47% | |
| 16 | 2.17% | |
| 17 | 1.28% | |
| 18 | 0.71% | |
| 19 | 0.37% | |
| 20 | 0.19% | |
| 21 | 0.09% | |
| 22 | 0.04% | |
| 23 | 0.02% | |
| 24 | 0.01% | |
| 25 | 0.00% | |
| 26 | 0.00% | |
| 27 | 0.00% | |
| 28 | 0.00% | |
| 29 | 0.00% | |
| 30 | 0.00% | |
| Sum | 100.00% |
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