Home / Statistical Tools / Analysis Tools / Dotplot / Examples / Examples
Examples¶
Quick Start¶
Create your first dot plot in less than two minutes.
This example shows the simplest way to create a dot plot: a single column of numeric data. Quantum XL plots each value as a dot along a number line, stacking dots vertically when values overlap.
Goal¶
Create a dot plot showing the distribution of temperature readings from a single column of data.
Sample Data¶
Download DotPlot_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.

Alternatively, you can copy the sample data from the table below and paste it directly into a new Excel workbook.
| Temperature |
|---|
| 68.1 |
| 69.5 |
| 71.2 |
| 71.8 |
| 72.0 |
| 72.3 |
| 72.5 |
| 72.5 |
| 72.8 |
| 73.0 |
The table shows the first 10 of 25 rows. Download the full dataset above for the complete example.
Each row represents one temperature reading. The dataset has a dense cluster of values in the low 70s with a few isolated readings at the extremes.
Steps¶
-
Launch the analysis
From the Excel ribbon, select QXL Stat Tools → Analysis Tools → Dot Plot.
-
Select your data
Select cells A1:A26 (the header row plus all 25 data rows).
-
Configure the analysis
In the Dot Plot dialog:
- Data Columns: "Temperature" should be checked
Quantum XL automatically selects the single provided data column, so you don't need to change any options. Click Finish to generate the chart.
Result¶
Quantum XL creates a dot plot showing the distribution of temperature readings. Each dot represents one observation. You can see a dense cluster of dots in the 72–74 range, a gap between 78 and 80, and a few isolated dots at the high end (80.2, 82.1). This pattern is immediately visible because every individual data point is plotted.
More Examples¶
Ready for more? See these variations:
- Multiple Data Columns — Compare distributions across several columns side by side
- Count Frequency — When your data is pre-aggregated with counts
- GroupBy — Compare segments side by side