Scatter Plot

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  • Scatter Plot

    1. 1. Scatter Plot Nishant Narendra
    2. 2. Content <ul><li>Six Sigma – an introduction </li></ul><ul><li>Scatter Plot </li></ul><ul><li>When </li></ul><ul><li>Why </li></ul><ul><li>How </li></ul><ul><li>Example </li></ul><ul><li>Relationships </li></ul><ul><li>Summary </li></ul>
    3. 3. Six Sigma <ul><li>A statistical measure of variation. </li></ul><ul><li>Developed by Motorola for the first time in the mid-1980’s. </li></ul><ul><li>Full Six Sigma equals to 99.9997% accuracy. </li></ul><ul><li>A ‘tool box’ of quality and management tools for problem resolution. </li></ul><ul><li>A business philosophy focusing on continuous improvement. </li></ul><ul><li>An organized process for structured analysis of data. </li></ul>
    4. 4. Common Tools <ul><li>Affinity Diagram </li></ul><ul><li>Kano Model </li></ul><ul><li>Critical-To-Quality (CTQ) tree </li></ul><ul><li>Pareto Charts </li></ul><ul><li>Control Charts </li></ul><ul><li>Run Charts </li></ul><ul><li>Failure Modes and Effect Analysis (FMEA) </li></ul><ul><li>5 Whys Analysis </li></ul><ul><li>Brainstorming </li></ul><ul><li>Cause and Effect (C&E) Diagram </li></ul><ul><li>Flow Diagrams </li></ul><ul><li>Scatter Plots </li></ul>
    5. 5. Scatter Plot <ul><li>Also called as scatter diagram, scattergram, Correlation Analysis, or X-Y Analysis. </li></ul><ul><li>It is a basic graphic tool that illustrates the relationship between two variables. </li></ul><ul><li>Scatter plots are a useful diagnostic tool for determining association, but if such association exists. </li></ul>
    6. 6. Scatter Plot <ul><li>The Scatter Diagram is a Quality Tool that can be used to show the relationship between &quot;paired data&quot; and can provide more useful information about a production process. </li></ul>
    7. 7. Description <ul><li>The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. </li></ul><ul><li>The dots on the scatter plot represent data points. </li></ul><ul><li>If the variables are correlated, the points will fall along a line or curve. </li></ul><ul><li>The better the correlation, the tighter the points will hug the line. </li></ul>
    8. 8. When <ul><li>When you have paired numerical data. </li></ul><ul><li>When your dependent variable may have multiple values for each value of your independent variable. </li></ul><ul><li>When trying to determine whether the two variables are related, such as… </li></ul><ul><ul><li>When trying to identify potential root causes of problems. </li></ul></ul><ul><ul><li>After brainstorming, using a fishbone diagram, to determine objectively whether a particular cause and effect are related. </li></ul></ul><ul><ul><li>When determining whether two effects that appear to be related both occur with the same cause. </li></ul></ul><ul><ul><li>When testing for autocorrelation before constructing a control chart. </li></ul></ul>
    9. 9. Benefits: <ul><li>Helps identify and test probable causes. </li></ul><ul><li>By knowing which elements of your process are related and how they are related: </li></ul><ul><ul><li>You will know what to control. </li></ul></ul><ul><ul><li>What to vary to affect a quality characteristic. </li></ul></ul>
    10. 10. How <ul><li>On gridline or graph paper: </li></ul><ul><li>STEP #1 </li></ul><ul><li>Decide which paired factors you want to examine. Both factors must be measurable on some incremental linear scale. </li></ul><ul><li>Draw an &quot;L&quot; form. Make your scale units at even multiples, such as 10, 20, etc. so as to have an even scale system. </li></ul><ul><li>Collect 30 to 100 paired data points. </li></ul><ul><li>Find the highest and lowest value for both variables. </li></ul>
    11. 12. <ul><li>On the Horizontal axis (Known as the &quot;X&quot; axis, from Left to Right) you place the Independent or &quot;cause&quot; variable. </li></ul>STEP #2
    12. 13. <ul><li>On the Vertical axis (Known as the &quot;Y&quot; axis, from Bottom to Top) you place the Dependent or &quot;effect&quot; variable. </li></ul>STEP #3
    13. 14. <ul><li>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.) </li></ul><ul><li>The shape that the cluster of dots takes will tell you something about the relationship between the two variables that you tested. </li></ul>STEP #4
    14. 15. Example <ul><li>In a bakery the data was gathered for identifying relationship between minutes of cooking and defective pieces. </li></ul><ul><li>Below mentioned was the sample collected: </li></ul><ul><li>Minutes Cooking Defective Pies </li></ul><ul><li>10 1 </li></ul><ul><li>45 8 </li></ul><ul><li>30 5 </li></ul><ul><li> 75 20 </li></ul><ul><li>60 14 </li></ul><ul><li>20 4 </li></ul><ul><li>25 6 </li></ul>
    15. 16. Scatter Plot
    16. 17. Three Parameters for relationship <ul><li>Correlation </li></ul><ul><li>Slope </li></ul><ul><li>Direction </li></ul>
    17. 18. Correlation <ul><li>Measures how well the data line up. The more the data resembles a straight line, the better the correlation to each other. </li></ul>
    18. 19. Correlation
    19. 20. No Correlation
    20. 21. Slope <ul><li>Measures the steepness of the data. </li></ul><ul><li>Equidistant the data slope shows the correlation is good and greater the importance of the relationship. </li></ul>
    21. 22. Strong Correlation
    22. 23. Moderate Correlation
    23. 24. No Correlation
    24. 25. Direction <ul><li>The &quot;X&quot; variable can have a positive or a negative impact on the &quot;Y&quot; variable. </li></ul><ul><li>In positive correlation both the values increases together. </li></ul><ul><li>In negative correlation both the values decreases together. </li></ul>
    25. 26. Positive Correlation
    26. 27. Negative Correlation
    27. 28. Banana Shaped Correlation
    28. 29. Boomerang Shaped Correlation
    29. 30. Summary <ul><li>Scatter Plot is a Quality Tool used to analyze numeric data. </li></ul><ul><li>Used to identify correlation between the causes and effects and to understand their correlation. </li></ul><ul><li>Helpful to control the effects in the desired manner after identifying the kind of correlation. </li></ul><ul><li>Useful for Cause and Effect Analysis. </li></ul>
    30. 31. Thank You…

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