Box plots are most useful forex

This results in a chart with one box plot visualizing the distribution of the chosen numeric attribute. You can add additional Numeric field variables to compare multiple distributions from different attribute fields in a table.

For example, in a county dataset, Population and Population are added as Numeric field variables. The resulting chart displays two box plots, one visualizing the distribution of Population, and the other visualizing the distribution of Population, for all counties in the dataset.

When only a single Numeric field variable is added, you have the option of adding a Category variable as a method of comparing distributions across categories. For example, Population is set as the Numeric field variable and StateName as the Category variable for a county dataset.

The resulting chart displays a box plot for each state, visualizing the distribution of Population for all counties belonging to each state. Multiple series You can use multiple series box plots to compare distributions of different types, or by different categories. Multiple series box plots can be created by specifying a Category field and multiple Numeric fields, or by specifying a Split by category field. When using a Category variable with multiple Numeric fields, each Numeric field added to the series table creates a series.

For example, in a county dataset, StateName is set as the Category variable and Population, Population, and Population are set as the Numeric field variables. The resulting chart will have states as categories along the x-axis, with three series each Population, Population, and Population Alternatively, a Split by variable can be added as a way to further divide the data and create multiple series.

For example, Population is set as the Numeric field variable, StateName as the Category variable, and ElectionWinner as a Split by field for a county dataset. The resulting chart will display two side-by-side box plots for each state box plots total , one visualizing the distribution of Population of all counties in each state with the ElectionWinner value of Democrat, and one for all counties in each state with the ElectionWinner value of Republican.

You can also use Split by fields when multiple Numeric field variables are used instead of a Category variable. For example, Population, Population, and Population are set as the Numeric field variables and ElectionWinner is set as the Split by field for a county dataset. The resulting chart will display the three Numeric field variables along the x-axis Population, Population, and Population , each with two side-by-side box plots: one displaying the distribution for all counties with the ElectionWinner value of Democrat, and the other for all counties with the ElectionWinner value of Republican.

Display multiple series When you use a Split by field to create multiple series, you have two options for visualizing the results: Side-by-side —Create side-by-side box plots, one for each series. As mean lines —Create one box plot for each Category value or Numeric field variable and use lines to show the mean for each unique value in the Split by field. For example, Population is set as the Numeric field variable, StateName is set as the Category variable, and ElectionWinner is set as a Split by field for a county dataset.

The Series table populates each unique ElectionWinner value Democrat and Republican , but instead of splitting each state into a box plot for each ElectionWinner value, the resulting chart displays one box plot for each state visualizing the distribution of Population for counties within that state, and the mean value of each Split by series Democrat and Republican is overlaid on the box plots showing where the mean value of each series falls in relation to the total distribution.

Standardization When you create a box plot from multiple Numeric fields, a z-score standardization is applied by default. Standardization allows numeric variables of different units to be comparable. For example, a box plot comparing the distributions of income with values in the tens of thousands and unemployment rate values ranging between 0 and 1. Standardization of the attribute values involves a z-transform, where the mean for all values is subtracted from each value and divided by the standard deviation for all values.

The z-score standardization puts all the attributes on the same scale, allowing multiple distributions to be visualized in the same chart. To visualize the raw values instead, uncheck the Standardize values z-score check box in the Chart Properties pane. Axes Several options control the axes and related settings. X-axis label character limit Category labels are truncated at 11 characters by default.

When labels are truncated, you can hover over the label to view the full text. To display the entire label text in the chart, increase the label character limit. Y-axis bounds Default y-axis bounds are set based on the range of data values represented on the y-axis. Customize these values by typing a new axis bound value. You can set axis bounds to keep the scale of your chart consistent for comparison. Click the Reset button to revert the axis bound to the default value.

Number format You can format the way an axis displays numeric values by specifying a number format category or by defining a custom format string. They provide a graphical rendition of statistical data based on the minimum, first quartile, median, third quartile, and maximum, also Outliers can be plotted as individual points. The term "box plot" comes from the fact that the graph looks like a rectangle with lines extending from the top and bottom. Because of the extending lines, this type of graph is sometimes called a box-and-whisker plot.

The distances between the different box parts represent the degree of a data dispersion and a data asymmetry to identify outliers. The points values can be compared between themselves single-series chart or values inside the category multi-series chart. In case of several series points are grouped by categories.

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