Density Data
Why density or histogram charts?
Density plots & histograms are a great way to see the distribution of a numerical variable.
A histogram consists of a series of bars, where the height of the bar represents the number of observations that fall within a specific range of values (bins). For a smoothed representation of a histogram, consider a density plot.
Kernel density estimation (KDE) is the application of kernel smoothing to approximate the probability density function of a variable.
Data entry
If you select a density chart a table is generated where columns define groups / variables you want to compare and rows represent individual values within groups.
| Group A | Group B |
|---|---|
| 80 | 120 |
| 60 | 95 |
| 70 | 142 |
| 72 |
Have groups with unequal sample sizes? Simply leave cells blank and they will be ignored.
When your ready to plot, Graphmatik will parse your data and calculate all frequency & density metrics when navigating over to the chart workspace.
Density and Column charts share the same data type, so you can seamlessly switch between the two chart types without having to re-enter any data and explore multiple ways of visualizing your dataset.
Key features
- Each column defines a separate group / variable.
- You can only plot numerical variables.
- Provides useful information about the distribution of your data.
Types of density plots
You can create 3 types of density charts. Select from the plots below to learn more.
Statistical analyses supported
- Descriptive statistics
- Independent samples t-test
- Dependant samples t-test
- One-way ANOVA