The most common method of finding outliers with the Interquartile Range (IQR) is to define outliers as values that fall outside below Q1 or above Q3

Can you still use statistical process methods when your data is not normaly distributed?

With Kolmogorov-Smirnov test, Shapiro-Wilk test and Anderson-Darling test, if p-value is smaller than 0,005 :

The interquartile range is the difference in value between the third quartile and first quartile.

Match the correct definition to the word

When the distribution is bimodal (two peaks), usually there are two process: it means that the products are manufacturing in slightly different conditions (machining on two different milling machines for instance).

You have to :

With Kolmogorov-Smirnov test, Shapiro-Wilk test and Anderson-Darling test, you test the null hypothesis : "the data are normally distributed".

The upper quartile, or third quartile (Q3), is the value under which 25% of data points are found when they are arranged in increasing order.

The most common method of finding outliers with the Interquartile Range (IQR) is to define outliers as values that fall outside of 2.5 x IQR below Q1 or 2.5 x IQR above Q3

What is the percentage of data within 2 standard deviation (σ) of the mean (μ) : μ ± 2 x σ ?