Skip to content

Home / Statistical Tools / Analysis Tools / Correlation and Covariance / Examples / Examples

Examples

Quick Start — Pearson Correlation

Create your first correlation matrix in less than two minutes.

This example shows the simplest way to measure the linear relationship between two numeric variables using Pearson Correlation.

Goal

Measure the linear correlation between height and weight and assess whether the relationship is statistically significant.

Sample Data

Download CorrelationAndCovariance_Pearson.xlsx

Excel Protected View

When you open downloaded files, Excel displays a Protected View warning. You must click Enable Editing before you can use Quantum XL with the file.

Excel Protected View warning

Alternatively, you can copy the sample data from the table below and paste it directly into a new Excel workbook.

Height Weight
62 115
64 125
66 140
68 155
70 165
65 130
67 148
69 160
71 170
63 120

Each row represents one person's height (in inches) and weight (in pounds). The data should show a clear positive linear relationship — taller people tend to weigh more.

Steps

  1. Launch the analysis

    From the Excel ribbon, select QXL Stat Tools → Analysis Tools → Correlation and Covariance → Pearson Correlation.

  2. Select your data

    Select cells A1:B11 (the header row plus all 10 data rows).

  3. Configure the analysis

    In the Correlation dialog:

    • Data Columns: "Height" and "Weight" should both be checked

    Click Finish to generate the correlation matrix.

Result

Quantum XL creates a compact two-dataset correlation output showing:

  • Coefficient — A value close to 1 indicates a strong positive linear relationship (weight increases as height increases). A value close to -1 would indicate a strong negative relationship. Values near 0 indicate little to no linear relationship.
  • p-Value — If the p-value is less than 0.05, the relationship is statistically significant (highlighted in red).

For this sample data, the coefficient should be very close to 1 with a tiny p-value, confirming a strong significant positive linear relationship between height and weight.

More Examples

Ready for more? See these variations:

  • Spearman Correlation — Rank-based correlation for non-linear or ordinal data
  • Covariance — Unstandardized measure showing variance on the diagonal