0.352 0.584 6.251 7815 9.348 11.345 12.838 0.711 1.064 7.779 9488 11.143 13277 14.860 1.145 1.610 9.236 11.071 12.833 15.006 16.750 1.635 2204 10.645 12.592 14.449 16.812 18.548 2.167 2.833 12.017 14.067 16.013 18475 20.278 2.733 3490 13.362 15.507 17.535 20.090 21.955 3.325 4.168 14.684 16.919 19.023 21.666 23.589 3.940 4.865 15.987 18.307 20.483 23.209 25.188 4.575 5.578 17.275 19.675 21.920 24.725 26.757 5.226 6.304 18.549 21.026 23.337 26.217 28.299 5.892 7.042 19.812 22.362 24.736 27.688 29.819 6.571 7.790 21.064 23.685 26.119 29.141 31.319
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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