The level, in grams per millilitre, of a pollutant at different locations in a certain river is denoted by the random variable X, where X has the distribution N(n, 0.0000409). In the past the value of n has been 0.0340. This year the mean level of the pollutant at 50 randomly chosen locations was found to be 0.0325 grams per millilitre. Test, at the 5% significance level, whether the mean level of pollutant has changed
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 level, in grams per millilitre, of a pollutant at different locations in a certain river is denoted by
the random variable X, where X has the distribution N(n, 0.0000409).
In the past the value of n has been 0.0340.
This year the mean level of the pollutant at 50 randomly chosen locations was found to be 0.0325
grams per millilitre.
Test, at the 5% significance level, whether the mean level of pollutant has changed
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