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We are going over the normal distribution first, because it is a very common and important distribution, and it is frequently used in many data science activities. Future posts will cover other types of probability distributions. However, please keep in mind that data is NOT always normally distributed. All of these and more follow a normal distribution.
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The height of male students, the height of female students, IQ scores, etc. Many natural phenomena can be described very well with this distribution. This distribution is very common in real world processes all around us. These other data values will taper off to lower and lower probabilities equally in both directions the farther they are from the mean value. The further the other values are from the mean the less probable they are. Data values other than the mean will be less probable. this value will have the highest probability).
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When collecting data, we expect to see this value more than any others when our data is normally distributed (i.e. It is a symmetric distribution where most of the observations cluster around a central peak, which we call the mean. I understand! Trust me, it will make more sense as we explain it and use it. Whoa! That’s a tightly packed group of mathematical words. Introduction Figure 1.1: An Ideal Normal Distribution, Photo by: MediumĪ normal distribution ( aka a Gaussian distribution) is a continuous probability distribution for real-valued variables.
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Published by Teena Mary on SeptemSeptember 1, 2020ĭata is the new oil and new gold.
Pdf to cdf pdf#
Normal Distribution: An Introductory Guide to PDF and CDF
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