Thomas wants to compare the mean concentration of carbon monoxide (CO) on residential versus commercial streets, since these differ in terms of car traffic. In each of three neighborhoods of Montréal (named A, B, and C below), he randomly chooses four locations for each type of street, for a total of 24 observations (2 street types x 3 neighborhoods x 4 locations). At each location, he measures CO concentration in the air over a period of 10 hours (8:00 AM-6:00 PM), and obtains the following data (in ppm/h). Question: Test whether or not the difference between residential and commercial streets in mean atmospheric CO concentration is the same among the three neighborhoods, and whether or not CO concentration in the air is the same, on average, for the two types of streets. Note that Neighborhood is considered a random block factor in the ANOVA. Use significant level= 0.05.
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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