Description

Firstly let us discuss about histogram which is an extension of a bar plot. Within bar plot each data point is represented using a bar. Histogram is a bar representation of any data based on logical grouping of bars, which are called as bins. Each bar in a histogram is called as a bin. On X-axis we plot the variable of interest and on the Y-axis we plot the frequency of occurrence of that variable. If we represent probability on the Y-axis on histogram, instead of the raw count then it is called as probability distribution.

Normal distribution is any data which does not have any abnormalities. For e.g. heights of people; we will find a small proportion of people whose height is very less or very high. Majority of the people will have their height which will fall within the range of 5 feet to 6 feet.

A normal distribution can be characterized using Mean (Mu) & Standard Deviation (Sigma). We can know about the shape of the distribution if we know these two parameters. Also a normal distribution extends from –infinity to +infinity. If a process follows normal distribution & if operates at 1 sigma level then approximately 68% of the outputs meet the customer requirements. If a process follows normal distribution & if operates at 2 sigma level then approximately 95% of the outputs meet the customer requirements. If a process follows normal distribution & if operates at 3 sigma level then approximately 99.73% of the outputs meet the customer requirements.