Glad, it helps you in your job. You can also see the outliers using an imported box plot visual powerbi.microsoft.com/en-us/blog/visual-awesomeness-unlocked-box-and-whisker-plots/ Power bi also integrates Python and if you know package such as numpy, matplot lib, you can get a lot of these done visually too
I would think that you can have the mean and standard deviation as calculated measures and then you do your z-score as a new column on which you can use a slicer
Let me first emphasize that there are serval ways in which you can determine if an observation is an outlier. z-score method only applies to data sets with frequency distributions that are mound shaped and symmetric. The z-score tells you how many standard deviations away a value is from the mean. Any z-score greater than 3 or less than -3 is considered to be an outlier. This rule of thumb is based on the empirical rule. From this rule, it is often seen that almost all of the data (99.7%) would be within three standard deviations from the mean. By calculating the z-score you are standardizing the observation, meaning the standard deviation is now 1. Thus, from the empirical rule you expect that 99.7% of the z-scores to be within -3 and 3. Another method you can use to determine the Interquartile Range (IQR) outlier Fences.
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Thank you so much. I never seen a very simple way of controlling outliers in your dataset as good as this.
Glad it was helpful!
Also please note that z-score method assumes the data sets has a frequency distribution that are mound shaped and symmetric.
Superb
Thank you, It was very helpful, and you explained it excellently!
You are welcome
Thanks. This is what i have been looking for
Glad, it helps you in your job. You can also see the outliers using an imported box plot visual
powerbi.microsoft.com/en-us/blog/visual-awesomeness-unlocked-box-and-whisker-plots/
Power bi also integrates Python and if you know package such as numpy, matplot lib, you can get a lot of these done visually too
Many thanks ! good explanation
Excellent video , thank you!
Thank you - this is really super helpful!
Thanks, very helpful
Is this the same process if I have months as slicers and want to filter outliers based on selected months?
Hello, how about if I want dynamic zscore based on data slicers? Thanks!
I would think that you can have the mean and standard deviation as calculated measures and then you do your z-score as a new column on which you can use a slicer
"no ole db error information found "
i am getting the above error . can you help?
where we can find the resources of this Video?
why did u used -3 and 3? could u explain that pls?
Let me first emphasize that there are serval ways in which you can determine if an observation is an outlier. z-score method only applies to data sets with frequency distributions that are mound shaped and symmetric.
The z-score tells you how many standard deviations away a value is from the mean. Any z-score greater than 3 or less than -3 is considered to be an outlier. This rule of thumb is based on the empirical rule. From this rule, it is often seen that almost all of the data (99.7%) would be within three standard deviations from the mean. By calculating the z-score you are standardizing the observation, meaning the standard deviation is now 1. Thus, from the empirical rule you expect that 99.7% of the z-scores to be within -3 and 3.
Another method you can use to determine the Interquartile Range (IQR) outlier Fences.
@@AnalyzeIt_Nael Amazing explanation, thank you so much!