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Dynamic Control Chart



A control chart can be very useful to analyze the performance of a variable over a period of time. It's a type of chart that uses standard deviations above and below the mean and allows you to spot any outliers that lie outside the bands created around the mean. Control Charts are made up of the distribution band (within the distribution, Upper Bound, and Lower Bound), a line chart or a circle chart.

Control chart is a statistical tool used to determine if a manufacturing or business process is in a state of control. Control chart uses standard deviations above and below the mean, which are displayed as bands - with outliers identified using color.

Standard deviation is used to show how far away data is from the mean. In this chart, the measure of standard deviation is used to identify outliers in the data. (Data points that differ significantly from other observations).

A control chart consists of:

-Points representing measurements in samples taken from a process at different times.

-The mean of all measurements in the sample.

-A center line is drawn at the mean.

-The control limits: UCL – the upper control limit and LCL – the lower control limit.

-Signals: Outliers (any point outside the control limits).


Standard Deviation-

The standard deviation calculates the spread of data. When data points are close together, the standard deviation is low. When data points are more spread out, the standard deviation is high.The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you that you have a relatively large standard deviation.


Why Standard Deviation is this useful?

Here's an example: If you are comparing test scores for different schools, the standard deviation will tell you how diverse the test scores are for each school.

Let's say X Elementary has a higher mean test score than Y Elementary. Your first reaction might be to say that the kids at X Elementary are smarter. But a bigger standard deviation for one school tells you that there are relatively more kids at that school scoring toward one extreme or the other. By asking a few follow-up questions you might find that, say, X Elementary's mean was skewed up because the school district sends all of the gifted education kids to X Elementary. Or that Y Elementary's scores were dragged down because students who recently have been "mainstreamed" from special education classes have all been sent to Y Elementary.


Problem Statement-

Create a dynamic control chart where the user can select the value for Standard Deviation.


Dataset: Sample Superstore.

Start by creating a Parameter for Standard Deviation.


Step 1- Select the down arrow in the data pane and select the “Create Parameter.” option.

Step 2- In the Dialogue, box performs the following steps:

➢ Set the name as “Standard Deviation”.

➢ Change Data type to Integer.

➢ Change allowable values to List.

➢ In list values enter three values 1,2,3 respectively.

Step 3- Create a normal line chart using Order Date (Month) Continuous and Profit.

Step 4- Create a calculated field for Lower Bound using the following formula:

WINDOW_AVG(SUM([Profit])) - (WINDOW_STDEV(SUM([Profit])) * [Standard Deviation])

Step 5- Similarly create another Calculated field for Upper Bound using the following formula:

WINDOW_AVG(SUM([Profit])) + (WINDOW_STDEV(SUM([Profit])) * [Standard Deviation])

Step 6- Finally create another Calculated Field for defining Outliers using the following formula:

SUM([Profit]) < [Lower Bound] or SUM([Profit]) > [Upper Bound]

You can see these 3 calculated fields Under Measure Names.

Step 7- Bring Measure values to Rows shelf and in the “Measure Values” box keep only the Lower Bound, Upper Bound and remove the rest. For removing you have to click on drop down arrow and then select Remove.

Step 8- Click on the Measure Values field in the Rows shelf and select Dual Axis.

Once Dual Axis is done, this is how it should look:

Step 9- Right-click on the Right axis and select “Synchronize Axis”.

Once synchronized, this is how it should look:


Step 10- Bring Outliers to Color in the Profit marks card.

To Change the Color, Go to Color mark and Click on Edit Color button and select the color as you want.

After changing Colors, it will look like

Step 11- Display Parameter, click on drop down arrow and Select Show Parameter option.

Output: This is the Output we get and now you can check Standard Deviation functioning by Selecting 1,2 and 3 Options.

Step 12- Change Standard Deviation to 2.

Step 13- Change Standard Deviation to 3, You can see that Outliers are beyond these lines.


Conclusion-

Control charts can help us to understand that, if the process we are dealing with is in control or out of control. just understanding this is very important because the process that is in control is very easy to improve or change than one that is out of control. Control Chart is used to determine if a manufacturing or business process is in a state of control.

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