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Go Deeper: How to Conduct Correlation Analysis in Excel

Imagine that you'd like to know if a person's weight is related to their systolic blood pressure. Does it increase or decrease with weight?

To understand and explore the linear relationships between two or more sets of numbers, Excel provides tools to analyze the variance (i.e., covariance) and relationships (i.e., co-relation) between two or more sets of numbers.

Here is an example of correlation analysis in Excel using QI Macros add-in.

1. Select the data

Select two or more columns of data:

This sample data is found in QI Macros Test Data > statistical.xlsx > Correlation-Covariance tab

2. Click on QI Macros, Statistical Tools, Regression and Other Statistics and then Correlation:

3. Evaluate the Correlation Results:

Correlation Results will always be between -1 and 1.

• -1 to < 0 = Negative Correlation (more of one means less of another)
• 0 = No Correlation
• > 0 to 1 = Positive Correlation (more of one means more of another)

If the correlation is greater than 0.80 (or less than -0.80), there is a strong relationship.

In this example:

• Sample 1 and Sample 2 have a positive correlation (.414)
• Sample 1 and Sample 3 have a negative correlation (-.07)
• Sample 2 and Sample 3 have a negative correlation (-.608)

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Regression Analysis

If you'd like more information, run regression analysis on the data. Correlation is the "Multiple R" in the results.

Please note that the straight lines represent the Upper and Lower Prediction Intervals, while the more curved lines are the Upper and Lower Confidence Intervals

Confidence Intervals provide a view into the uncertainty when estimating the mean, while Prediction Intervals account for variation in the Y values around the mean.

The 95% and 99% Confidence Levels reference when your alpha value is set at .05 (95%) or .01 (99%). This provides you with information on how the confidence level can impact your results, depending on where alpha is set.

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