The Normal Distribution

You have surely seen a normal distribution before as it is the most common one. The statistical term for it is Gaussian distribution, but many people call it the Bell Curve as it is shaped like a bell. It is symmetrical and its mean, median and mode are equal. If you remember the lesson about skewness, you would recognize it has no skew! It is perfectly centered around its mean.

So, it is denoted in this way. N stands for normal, the tilde sign denotes it is a distribution and in brackets we have the mean and the variance of the distribution. On the plane, you can notice that the highest point is located at the mean, because it coincides with the mode. The spread of the graph is determined by the standard deviation. Now, let’s try to understand the normal distribution a little bit better.

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[hermozart]

2% is the rule of error. If it is more than 2% it is not normal. Or not the right population. Or if it is more than 2% in error then, your sample is all wrong. But I think that would be a measure of regression (assuming you can measure and know how to measure change). I think this is a linear behavior model, since 2% is steady as she goes. Normalized, averaged, with standards (assuming you have standards after some point of time and sampling, otherwise you wait and see).