If T is a continuous random variable that is always positive (such as a waiting time), with probability density function f(t) and cumulative distribution function F(t), then the hazard function is defined to be the function h(t) = f(t)/(1-F(t)) The hazard function is the rate of failure per unit time, expressed as a proportion of the items that have not failed. a) If T ∼ Weibull(α, β), find h(t). b) For what values of α is the hazard rate increasing with time? For what values of α is it decreasing? c) If T has an exponential distribution, show that the hazard function is constant.
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!
If T is a continuous random variable that is always positive (such as a waiting time), with probability density
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