Mahesh Godavarti
2

Here is a good answer.


What percent of the values in a normal distribution are considered "outliers" using this definition? Since the tail area for the lowest 25% has a z= -0.67 and the z for the upper 25% is z = 0.67, the interquartile range is 1.34. 1.5*1.34=2.01.
practice problems - Inspire
inspire.stat.ucla.edu/unit_05/solutions.php
Qalaxia QA Bot
1

I found an answer from www.quora.com

What is the percent of the values in the standard Normal distribution ...


Well that is to say... if the data is Normally distributed... you could look up the Y values ... You could also expect a percentage of outliers if you would use a control ...


For more information, see What is the percent of the values in the standard Normal distribution ...

Qalaxia QA Bot
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I found an answer from stats.stackexchange.com

clustering - What is considered a "normal" quantity of outliers - Cross ...


If you expect a normal distribution of your data points, for example, then ... to base your definition of an outlier on the proportion of outliers that ...


For more information, see clustering - What is considered a "normal" quantity of outliers - Cross ...

Qalaxia QA Bot
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I found an answer from www.quora.com

How do outliers affect normal distribution in statistics? - Quora


... a distribution doesn't technically have outliers (the data set does) and their ... for a skewed distribution treated essentially as a Normal distribution but that may  ...


For more information, see How do outliers affect normal distribution in statistics? - Quora

Qalaxia QA Bot
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I found an answer from stats.stackexchange.com

normal distribution - Detecting outliers in percentages - Cross ...


It does catch the outliers but I know that percentages are not normally distributed. Each individual data point is 1/0 (Bernoulli) but I could not find ...


For more information, see normal distribution - Detecting outliers in percentages - Cross ...

Qalaxia QA Bot
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I found an answer from stackoverflow.com

Detect and exclude outliers in Pandas data frame - Stack Overflow


DataFrame({'Data':np.random.normal(size=200)}) # example dataset of normally distributed data. df[np.abs(df. .... and trimboth() to cut the outliers out in a single row, according to the ranking and an introduced percentage of removed values.


For more information, see Detect and exclude outliers in Pandas data frame - Stack Overflow

Qalaxia Knowlege Bot
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I found an answer from en.wikipedia.org

Outlier - Wikipedia


... considered that the underlying distribution of the data is not ... to converge as the sample size increases, and outliers ...


For more information, see Outlier - Wikipedia

Qalaxia Knowlege Bot
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I found an answer from en.wikipedia.org

68–95–99.7 rule - Wikipedia


In statistics, the 68–95–99.7 rule, also known as the empirical rule, is a shorthand used to remember the percentage of values ... In the empirical sciences the so- called three-sigma rule of thumb expresses a conventional heuristic that nearly all values are taken .... From the rules for normally distributed data for a daily event: ...


For more information, see 68–95–99.7 rule - Wikipedia

Qalaxia Knowlege Bot
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I found an answer from statweb.stanford.edu

Homework 4 Solutions


Nov 13, 2008 ... equally spaced as that would not be a normal probability plot. ... supports the assumption that the errors are independently, identically distributed. ... you can see from the plot of the difference of the two types of residuals, the outliers are a little ... If there are data points with x-values close to the singularity on.


For more information, see Homework 4 Solutions

Qalaxia Knowlege Bot
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I found an answer from web.stanford.edu

Diagnostics_for_multiple_regression


Call: lm(formula = Time ~ Distance + Climb, data = races.table) Residuals: ... Errors may not be normally distributed or may not have the same variance ..... Cook's distance measures how much the entire regression function changes .... It seems to have taken much longer than it should have so maybe it is an outlier in the ...


For more information, see Diagnostics_for_multiple_regression