That I did with x except that instead of using randn, I used Points over so that instead of being centered around 0 they are areĬentered around 80. Written with the Greek letter ) and that just shifts all my The numbers will change but many of theįinally I add mu (which = 80 mu stands for mean, often It’s like a change of units (movingįrom feet to inches).
The standardĭeviation is just a scaling number. The key intuition is what you’ve just seen. We’ll define it more precisely below (it’s not exactly the averageĭistance from the mean, but it can be helpful for a beginner to think of Sigma, but you’ll most often see it written with the Greek letter Spread (roughly, average distance from the mean) has a technical name. Normal distribution, just one that’s spread out more. Point is that multiplying all the data points by 5 still gives me a The number sigma, or amount of spread, is one way in which a normalĭistribution can vary. First, let’s get a littleĬlearer about exactly what kind of clustering around a mean we’re We’ll do some practical examples shortly. This sounds like something that has to be true (“Most people areĪverage”), but as we shall see, that is far from the case. Picture then represents the idea that most things hover around the mean. Mean rapidly reduces the proportion of population with that A-value. The mean value for both higher and lower A-values, moving away from the Point is at 0 that is, the most likely A-value for a student to have is The bell shape comes from this: The highest Population has A-values in the neighborhood of that point. Of a point on the curve reflects roughly what proportion of the Represent A-values, ordered from lowest A-value to highest. Other bell-curved distributions that are not normal distributions. Is descriptive, but it needs to be used with caution, because there are Here the shape of the curve is roughly that of a bell, leading to yetĪnother name for this kind of distribution, the “bell curve”. From scipy import stats import matplotlib.pyplot as plt import pylab import numpy as np % matplotlib inline pylab.
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