Scatter Plot

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Scatter Plot Nishant Narendra

Transcript of Scatter Plot

Page 1: Scatter Plot

Scatter Plot

Nishant Narendra

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Content• Six Sigma – an introduction• Scatter Plot• When• Why• How• Example• Relationships• Summary

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Six Sigma• A statistical measure of variation.• Developed by Motorola for the first time

in the mid-1980’s.• Full Six Sigma equals to 99.9997%

accuracy.• A ‘tool box’ of quality and management

tools for problem resolution.• A business philosophy focusing on

continuous improvement.• An organized process for structured

analysis of data.

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Common Tools• Affinity Diagram • Kano Model• Critical-To-Quality (CTQ) tree• Pareto Charts• Control Charts• Run Charts • Failure Modes and Effect Analysis (FMEA)• 5 Whys Analysis• Brainstorming• Cause and Effect (C&E) Diagram• Flow Diagrams• Scatter Plots

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Scatter Plot• Also called as scatter diagram,

scattergram, Correlation Analysis, or X-Y Analysis.

• It is a basic graphic tool that illustrates the relationship between two variables.

• Scatter plots are a useful diagnostic tool for determining association, but if such association exists.

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Scatter Plot

• The Scatter Diagram is a Quality Tool that can be used to show the relationship between "paired data" and can provide more useful information about a production process.

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Description• The scatter diagram graphs pairs of

numerical data, with one variable on each axis, to look for a relationship between them.

• The dots on the scatter plot represent data points.

• If the variables are correlated, the points will fall along a line or curve.

• The better the correlation, the tighter the points will hug the line.

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When• When you have paired numerical data. • When your dependent variable may have multiple

values for each value of your independent variable. • When trying to determine whether the two variables

are related, such as… • When trying to identify potential root causes of

problems. • After brainstorming, using a fishbone diagram, to

determine objectively whether a particular cause and effect are related.

• When determining whether two effects that appear to be related both occur with the same cause.

• When testing for autocorrelation before constructing a control chart.

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Benefits:• Helps identify and test

probable causes.

• By knowing which elements of your process are related and how they are related:• You will know what to

control. • What to vary to affect a

quality characteristic.

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How• On gridline or graph paper: STEP #1 • Decide which paired factors you want to

examine. Both factors must be measurable on some incremental linear scale.

• Draw an "L" form. Make your scale units at even multiples, such as 10, 20, etc. so as to have an even scale system.

• Collect 30 to 100 paired data points. • Find the highest and lowest value for

both variables.

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• On the Horizontal axis (Known as the "X" axis, from Left to Right) you place the Independent or "cause" variable.

STEP #2

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• On the Vertical axis (Known as the "Y" axis, from Bottom to Top) you place the Dependent or "effect" variable.

STEP #3

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• Plot your data points at the intersection of your data plots of the X and Y values. For Example = X = 5, Y = 2. Go right 5 spaces, and then go up 2 spaces to plot the point (from O, which is the origin point.)

• The shape that the cluster of dots takes will tell you something about the relationship between the two variables that you tested.

STEP #4

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• In a bakery the data was gathered for identifying relationship between minutes of cooking and defective pieces.

• Below mentioned was the sample collected: Minutes Cooking Defective Pies

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Example

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Scatter Plot

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Three Parameters for relationship• Correlation • Slope • Direction

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Correlation

• Measures how well the data line up. The more the data resembles a straight line, the better the correlation to each other.

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Correlation

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No Correlation

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Slope • Measures the steepness of the data.

• Equidistant the data slope shows the correlation is good and greater the importance of the relationship.

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Strong Correlation

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Moderate Correlation

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No Correlation

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Direction • The "X" variable can have a positive or

a negative impact on the "Y" variable.

• In positive correlation both the values increases together.

• In negative correlation both the values decreases together.

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Positive Correlation

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Negative Correlation

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Banana Shaped Correlation

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Boomerang Shaped Correlation

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Summary • Scatter Plot is a Quality Tool used to analyze

numeric data.

• Used to identify correlation between the causes and effects and to understand their correlation.

• Helpful to control the effects in the desired manner after identifying the kind of correlation.

• Useful for Cause and Effect Analysis.

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Thank You…