The following data represent glucose blood levels (mg/100 ml) after a 12-hour fast for a random sample of 70 women (Reference: American Journal of Clinical Nutrition, Vol. 19, pp. 345-351). 45 66 83 71 76 64 59 59 76 82 80 81 85 77 82 90 87 72 79 69 83 71 87 69 81 76 96 83 67 94 101 94 89 94 73 99 93 85 83 80 78 80 85 83 84 74 81 70 65 89 70 80 84 77 65 46 80 70 75 45 101 71 109 73 73 80 72 81 63 74
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 data represent glucose blood levels (mg/100 ml) after a 12-hour fast for a random sample of 70 women (Reference: American Journal of Clinical Nutrition, Vol. 19, pp. 345-351).
| 45 | 66 | 83 | 71 | 76 | 64 | 59 | 59 | 76 | 82 |
| 80 | 81 | 85 | 77 | 82 | 90 | 87 | 72 | 79 | 69 |
| 83 | 71 | 87 | 69 | 81 | 76 | 96 | 83 | 67 | 94 |
| 101 | 94 | 89 | 94 | 73 | 99 | 93 | 85 | 83 | 80 |
| 78 | 80 | 85 | 83 | 84 | 74 | 81 | 70 | 65 | 89 |
| 70 | 80 | 84 | 77 | 65 | 46 | 80 | 70 | 75 | 45 |
| 101 | 71 | 109 | 73 | 73 | 80 | 72 | 81 | 63 | 74 |
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