##
*Histogram
in Quality Control*

*Histogram in Quality Control*

###
*What is histogram?*

*What is histogram?*

Histogram is a spc technique. It is graphical tool that
represent the data values with the help of vertical rectangular bar. Height of
the bars is corresponding to the frequency of the data values.

- A frequency distribution shows different value in data occurs
- A Histogram most commonly used graph to show frequency distribution.
- It looks very much like a bar chart, but there is important difference between them.

###
*Why does the
histogram use as a QC tool?*

*Why does the histogram use as a QC tool?*

Showcases the large amount of data using vertical bars, It
is help in analysis the properties of data in statistical process control such
as.

*Distribution of the data*

*Spread of the data*

*Variation in the process*

*Skewness of the data*

*Help summaries data from process that has been collected over a period of time.*

*Help graphically represent data frequency distribution*

*in bar chart*.

###
*What does it to do?*

*What does it to do?*

- Reveals centering, variable and underlying distribution of data
- It is help to the process capable to meeting customer requirement

###
*Difference in
histogram and bar chart*

*Difference in histogram and bar chart*

Histogram is similar to bar charts but there are two main
differences.

- There are no gaps between the bars in histogram.
- The area of each bar is proportional to the frequency that is represented. Hence all area is proportional to the all frequency.

###
*When to use a histogram*

*When to use a histogram*

- When the data are numerical.
- When you want see data shape distribution, especially when determining whether the process output is distributed approximately normal.
- When analyse whether a process can meet the customer`s requirement.
- When analyse what the output from a supplier`s process looks like.
- When process parameter change from one time period to another time period.
- When determine whether the outputs of two or more process are different.
- When you wish to communicate the distribution of data quickly and easily to other.

*Histogram Construction*- Collect at least from 30 to 100 consecutive data point from a process.
- Use the histogram work sheet to set up the histogram. It will help you determine the number of bar, the range of number that go into each bar and the labels for the bar edges. After calculating W in step 2 of the work sheet, use your judgment to adjust it to a convenient number. For Example, you might decimal to round 0.9 to an even 1.0 the value of W must not have more decimal place than the numbers you will be graphing.
- Draw x- and y-axis on graph paper. Mark and label the y-axis for counting of data values. Mark and label the x-axis with the help of L values from worksheet. Spaces between these numbers will represent the histogram bars. Do not permit for spaces between these bars.
- For each data point, mark off one count on the top of suitable bar with an x or by shading that portion of the bar.

*How does a histogram help to the analysis the data?*####
*Symmetrical distribution or Normal distribution*

*Symmetrical distribution or Normal distribution*

A common pattern is the bell-shaped curve knows as the
“Normal distribution.” In a normal distribution, points are as likely to occur
on one side of the average as on the other. Be alert that the other
distributions looks like similar to the normal distribution. Statically
calculations must be used to prove a normal distribution.

**Skewed distribution**- The skewed distribution is asymmetrical because a natural limit prevent outcome on one side.
- The distribution peak`s off center toward the limit and a tail stretches away from it. For example, a distribution of analysis of very pure products would be skewed, because the products cannot be more than 100 percent pure.

Other lesson of natural limits are holes that cannot be miniature
than the diameter of the drill bit or call- keeping times that cannot be less
than zero. These distributions are called right skewed when its tail are right
position and when tail are left position it is called left skewed. It is
depending on the direction of the tail.

*Skewed Right*

*Skewed Left*####
*Doubled peaked or bi-modal distribution *

*Doubled peaked or bi-modal distribution*

The bi-modal distribution shape looks like the
back of a two humped camel. The outcomes of two processes with different
distribution are combined in one set of data.

*Doubled peaked or bi-modal distribution*

For example, a distribution of quality data from
a two shift analysis might be bi-modal, if each shift produces a different
distribution of results. Stratification often reveals this problem.

####
*Plateau Distribution *

*Plateau Distribution*

*Plateau Distribution*

The plateau might be called “multi-modal distribution.”
Several process with normal distribution are combined because there are many
peaks close together, the top of the distribution resembles a plateau.

*Edge peak distribution*

The edge peak distribution shape looks like the normal
distribution besides that it has a large peak at one tail.

Usually this is caused by faulty construction of histogram,
with data lumped together into a group labelled “greater than...”

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