Scatter Plot Diagram to Analyze Correlation | |||||||||||||||||||
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Scatter Plot DiagramsScatter plot diagrams are used to evaluate the correlation or cause-effect relationship (if any) between two variables (e.g., speed and gas consumption in a vehicle). When you think there's a cause-effect link between two indicators (e.g., calories consumed and weight gain) then you can use the scatter plot diagram to prove or disprove it. If the points are tightly clustered along the trend line, then there's probably a strong correlation. If it looks more like a shotgun blast, there is no correlation. From the nist.gov website, (no correlation, strong positive, strong negative): If the R2 correlation of determination (square of the correlation coefficient) is greater than 0.8, then 80% of the variability in the data is accounted for by the equation. Most statistics books imply that this means that you have a strong correlation. Here is an example of the scatter plot diagram created using the QI Macros for Excel SPC software. The correlation coefficient and correlation of determination information can be found in the intermediate spreadsheet created when the macro is run. R2 is also displayed on the chart.
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It contains over 70 fill in the blank templates such as the Ishikawa diagram, QFD, DOE, FMEA, PPAP, and Gage R&R for MSA. Performs ANOVA, t-test, F-test, and regression analysis.
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