Create a column of numbers going from 0 to 30. in binomial distrubution with n=30 and p=0.5. Remember that the variance is the expected value of the squared deviations from the mean. In the next column, compute the product of the probability of each outcome and the squared deviation from the mean. Place the sum of the 31 values in the cell next to the cell in which you have computed the variance as a function of the parameters.
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!
Create a column of numbers going from 0 to 30. in binomial distrubution with n=30 and p=0.5.
- Remember that the variance is the expected value of the squared deviations from the
mean . In the next column, compute the product of the probability of each outcome and the squared deviation from the mean. Place the sum of the 31 values in the cell next to the cell in which you have computed the variance as afunction of the parameters.
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