scatter plot
TRANSCRIPT
Scatter Plot
Nishant Narendra
05/03/23 2
Content• Six Sigma – an introduction• Scatter Plot• When• Why• How• Example• Relationships• Summary
05/03/23 3
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.
05/03/23 4
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
05/03/23 5
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.
05/03/23 6
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.
05/03/23 7
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.
05/03/23 8
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.
05/03/23 9
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.
05/03/23 10
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.
05/03/23 11
0
2
4
6
8
10
12
0 2 4 6
EastWestNorth
05/03/23 12
• On the Horizontal axis (Known as the "X" axis, from Left to Right) you place the Independent or "cause" variable.
STEP #2
05/03/23 13
• On the Vertical axis (Known as the "Y" axis, from Bottom to Top) you place the Dependent or "effect" variable.
STEP #3
05/03/23 14
• 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
05/03/23 15
• 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
10 145 830 5
75 20 60 14
20 425 6
Example
05/03/23 16
Scatter Plot
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 17
Three Parameters for relationship• Correlation • Slope • Direction
05/03/23 18
Correlation
• Measures how well the data line up. The more the data resembles a straight line, the better the correlation to each other.
05/03/23 19
Correlation
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 20
No Correlation
0
1020
30
4050
60
7080
90
0 2 4 6 8 10 12 14
Minutes Defected Cookies
05/03/23 21
Slope • Measures the steepness of the data.
• Equidistant the data slope shows the correlation is good and greater the importance of the relationship.
05/03/23 22
Strong Correlation
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 23
Moderate Correlation
0
20
40
60
80
100
120
140
0 2 4 6 8
Minutes Defected Cookies
05/03/23 24
No Correlation
0
1020
30
4050
60
7080
90
0 2 4 6 8 10 12 14
Minutes Defected Cookies
05/03/23 25
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.
05/03/23 26
Positive Correlation
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 27
Negative Correlation
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 28
Banana Shaped Correlation
0102030405060708090
100
0 2 4 6 8
Minutes Defected Cookies
05/03/23 29
Boomerang Shaped Correlation
0
10
20
30
40
50
60
70
80
0 2 4 6 8
Minutes Defected Cookies
05/03/23 30
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.
05/03/23 31
Thank You…