## Histogram in Quality Control

### 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?

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
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?

• 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

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 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

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

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

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 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...”

### To determine loss on drying (LOD)

To determine loss on drying (LOD) Definition:   Loss on drying compares the weight of product sample before and after drying. The result is the percentage of moisture in a product. % Moisture = (Begging weight-Ending weight) X 100                         -----------------------------------------------                                    Begging weight  Loss on drying determine by two method which is given below: Method (1):   Weigh accurately a dry empty glass Petri dish. Put the sample (about 0.5 to 5 gm as par requirement, Its mean if sample having less wet take 5 g weight. If sample having more wet than take 0.5 g weight ) in dish and weigh. Note down the reading. Distribute the sample in Petri dish by gentle shaking. Place the loaded dish (without cover) in the drying chamber for two hours. Maintain the oven temperature 105 +/- 5 0 C.  After drying is completed, open the drying chamber, cover the dish and allow it to cool at room temp.  Keep it in the desiccator for 15

### IR स्पेक्ट्रोस्कोपी और इसका सिद्धांत

IR स्पेक्ट्रोस्कोपी और इसका सिद्धांत  IR स्पेक्ट्रोस्कोपी क्या है और इसका सिद्धांत क्या है? एक अणु या एक रासायनिक यौगिक में यह पता लगाने के लिए कि कौन से कार्यात्मक समूह या समूह मौजूद हैं, हम IR स्पेक्ट्रोस्कोपी तकनीक का उपयोग करते हैं। आईआर स्पेक्ट्रोस्कोपी तकनीक का प्रदर्शन करने के लिए हम विद्युत चुम्बकीय स्पेक्ट्रम की एक विशिष्ट श्रेणी का उपयोग करते हैं जिसे हम IR (इन्फ्रारेड) विकिरण कहते हैं आईआर विकिरण तरंग दैर्ध्य की विभिन्न श्रेणियों से बना है और IR विकिरण के इस अनुप्रयोग को IR स्पेक्ट्रोस्कोपी के रूप में जाना जाता है क्योंकि यह पहचानने के लिए कि सभी कार्यात्मक समूह मौजूद हो सकते हैं हम IR विकिरण का उपयोग करते हैं इसलिए जब हम किसी नमूने पर आईआर विकिरण लागू करते हैं, तो हम इस तकनीक को आईआर स्पेक्ट्रोस्कोपी कहते हैं  अब आइए नजर डालते हैं आईआर स्पेक्ट्रोस्कोपी के सिद्धांत पर   जिस किसी भी नमूना का हम विश्लेषण करना चाहते हैं (नमूना किसी भी रूप में हो सकता है - ठोस, तरल या गैस) हम उसका नमूना तैयार करते हैं और इसे आईआर स्पेक्ट्रोफोटोमीटर में डालते हैं IR स्पेक्ट्रोफोटोमीटर वह उपकरण है

### Karl Fischer (K.F.)

Karl Fischer (K.F.) Karl Fischer (KF) titration is water determination techniques which is industrial scientists. It is performed by volumetric or coulometric measurement techniques. Principles of Karl Fischer titration: The KF reaction is based upon an early reaction called the Bunsen reaction, in which sulfur dioxide is oxidized by iodine with the consumption of water during this oxidation. German scientist Karl Fischer published a method in  a year 1935 for determination of water content in samples. This was a titrimetric method based on Bunsen re action used for determination of sulfur dioxide in aqueous solutions. The original reaction is as below: SO 2 + I 2 + 2H 2 O → H 2 SO 4 + 2HI The KF titration reaction currently accepted is as follows, and was reached by several advances in understanding the mechanism of the reaction and modification of the original reagent: The titration reaction is said to reach its endpoint once the iodine stops reacting with the