HereвЂ™s an example using Python programming. You donвЂ™t have to use 2 though, you can tweak it a little to get a better outlier detection formula for your data. Our approach was to remove the outlier points by eliminating any points that were above (Mean + 2*SD) and any points below (Mean - 2*SD) before plotting the frequencies. Sometimes we would get all valid values and sometimes these erroneous readings would cover as much as 10% of the data points. The challenge was that the number of these outlier values was never fixed. We needed to remove these outlier values because they were making the scales on our graph unrealistic. However, sometimes the devices werenвЂ™t 100% accurate and would give very high or very low values. Each data point contained the electricity usage at a point of time. I applied this rule successfully when I had to clean up data from millions of IoT devices generating heating equipment data. Remove Outliers Using Normal Distribution and S.D. This fact is known as the 68-95-99.7 (empirical) rule, or the 3-sigma rule. Such values follow a normal distribution.ДЄccording to the Wikipedia article on normal distribution, about 68% of values drawn from a normal distribution are within one standard deviation Пѓ away from the mean about 95% of the values lie within two standard deviations and about 99.7% are within three standard deviations.

When we perform analytics, we often come across data that follow a pattern with values rallying around a mean and having almost equal results below and above it e.g. Right now, we only know that the second data set is more вЂњspread outвЂќ than the first one. However, it's not easy to wrap your head around numbers like 3.13 or 14.67.

To be more precise, the standard deviation for the first dataset is 3.13 and for the second set is 14.67. However, the first dataset has values closer to the mean and the second dataset has values more spread out. how much the individual data points are spread out from the mean.Д«oth have the same mean 25. Standard deviation is a metric of variance i.e. By Punit Jajodia, Chief Data Scientist,.

Excel trendline ignore outliers