Data and series
Prepare data and build series
Use the spreadsheet to correct values, combine related measurements, and decide which observations belong to each visible series.
Hands-on example
Try this in FigureCheck
Open the example spreadsheet, edit values, and split Measurement into Control and Treatment series.
Opens a small synthetic dataset with a step-by-step guide in a new editable session.Open a file or begin from scratch
FigureCheck accepts CSV, TSV, TAB, and XLSX files. The first row should contain column names, and each later row should represent one observation.
Without a file, choose Start with blank table. Paste blocks copied from Excel or Google Sheets, then add rows or columns as needed. FigureCheck saves table edits in the background and refreshes the graph when you return to Figure.
Choose axes and series
In the spreadsheet, click a column letter to select it. Select X and one or more Y columns, then choose Make chart. The first selected column becomes X and the remaining columns become plotted Y series.
Use Shift for a continuous group of columns or Ctrl/Command to select columns individually. The same toolbar can assign uncertainty or split the points using a categorical column.
X and Y columns
X is normally the independent variable and Y is the measured response. Changing the Y column also updates the suggested Y-axis label.
Multiple measurements
Add more Y columns when several measurements share an X scale. When later Y columns use a different X column, assign that X column to the corresponding section of the table.
Split and merge groups
Choose a categorical column to separate one measurement into series such as Control and Treatment. Related category values can be merged under a clearer group name.
Highlight and remove rows
Check rows in the spreadsheet to highlight their points on the graph. In Stats, suggested outliers can be reviewed individually and removed only after you select them.
Outliers are reported relative to the selected fit or baseline. A point far from one model can be reasonable under another model.