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Scatter Plot


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  • Transcript

    • 1. Scatter Plot Nishant Narendra
    • 2. Content
      • Six Sigma – an introduction
      • Scatter Plot
      • When
      • Why
      • How
      • Example
      • Relationships
      • Summary
    • 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.
    • 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
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 11.  
    • 12.
      • On the Horizontal axis (Known as the "X" axis, from Left to Right) you place the Independent or "cause" variable.
      STEP #2
    • 13.
      • On the Vertical axis (Known as the "Y" axis, from Bottom to Top) you place the Dependent or "effect" variable.
      STEP #3
    • 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
    • 15. Example
      • 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 1
      • 45 8
      • 30 5
      • 75 20
      • 60 14
      • 20 4
      • 25 6
    • 16. Scatter Plot
    • 17. Three Parameters for relationship
      • Correlation
      • Slope
      • Direction
    • 18. Correlation
      • Measures how well the data line up. The more the data resembles a straight line, the better the correlation to each other.
    • 19. Correlation
    • 20. No Correlation
    • 21. Slope
      • Measures the steepness of the data.
      • Equidistant the data slope shows the correlation is good and greater the importance of the relationship.
    • 22. Strong Correlation
    • 23. Moderate Correlation
    • 24. No Correlation
    • 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.
    • 26. Positive Correlation
    • 27. Negative Correlation
    • 28. Banana Shaped Correlation
    • 29. Boomerang Shaped Correlation
    • 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.
    • 31. Thank You…