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Examples

Quick Start

Create your first scatter plot in less than two minutes.

This example shows the simplest way to create a scatter plot: two columns of numeric data paired as X and Y, with a linear regression line fitted to the data.

Goal

Create a scatter plot showing the relationship between temperature and yield, with a linear trendline and regression statistics.

Sample Data

Download ScatterPlot_QuickStart.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.

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

Temperature Yield
150 77.2
160 78.5
170 81.3
180 84.1
190 85.7
200 88.9
210 90.2
220 93.6
230 94.8
240 97.1

Each row represents one observation with paired Temperature (X) and Yield (Y) values. As temperature increases, yield tends to increase — the scatter plot will reveal this relationship.

Steps

  1. Launch the analysis

    From the Excel ribbon, select QXL Stat Tools → Analysis Tools → Scatter Plot.

  2. Select your data

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

  3. Configure the analysis

    In the Scatter Plot dialog:

    • X Columns: "Temperature" should be checked
    • Y Columns: "Yield" should be checked
    • Regression: "Linear" should be selected (default)

    Click Finish to generate the chart.

Result

Quantum XL creates a scatter plot with Temperature on the X-axis and Yield on the Y-axis. A linear regression line fits through the data points, showing a clear positive relationship. The statistics table alongside shows:

  • R² — how well the line fits the data (closer to 1 = better fit)
  • Coefficients — the intercept and slope of the regression equation
  • p-values — whether the relationship is statistically significant

A yellow input cell lets you enter a Temperature value to predict the expected Yield.

More Examples

Ready for more? See these variations: