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 lowest25%has a z= -0.67 and the z for the upper25%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

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** ...

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 ...

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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

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**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 ...

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

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

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

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

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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