Let's say you're working in a car tire disc manufacturing process, you capture 100 observations of the inner diameter of the car tire disk. You then create a graph of observed frequencies versus the inner diameter as shown in the diagram on the screen. This display is called a histogram. The height of each bar is equal to the frequency of occurrence of the inner diameter. The histogram represents a visual display of the data, in which one may more easily see three properties, shape, location or central tendency and spread in the inner diameter of the car tire disk data, you will see that the distribution of the inner diameter is roughly symmetric. And it's tapering in the center with a central tendency very close to zero.

74 Millimeter, the variability in the disc diameter is apparently relatively high, as some discs are as small as 73.97 millimeter, while others are as large as 74.03 millimeter. Thus, the histogram gives some insights into the process that inspection of the raw data in this table does not. Several guidelines are helpful in constructing histograms. When the data are numerous, grouping them into bins or cells, as in the disk diameter example is very useful. generally use between four and 20 bins. Often choosing the number of bins, approximately equal to the square root of the sample size works well.

Make the bins of uniform with style The lower limit for the first bin just slightly below the smallest data value. These guidelines are necessary if you're constructing a histogram manually. These are mostly taken care of when you use Minitab to construct a histogram, it is now time to learn how to create a histogram on Minitab. Thank you for attending. See you in the next lecture.