Recommended Chart Use

Chart	Common Use of Chart
Bar Chart	Examine one or more measures (such as sales and costs) over a grouping (such as region or quarter).
Pie Chart	View a grouping's proportions (such as the percentage of sales that each region a company contributes to the company total).
Line Chart	See trends of one or more measures, such as quarterly revenues and expenses. Successive observations should be related, since the line visually relates the points. If they are not related, use a bar chart instead.
Histogram	Examine the distribution of a metric (for example, the quantity of highly discounted transactions compared to low discounted transactions).
Scatterplot	How two measures relate (for example, which investments provide a favorable date of return at a low risk).
Multiscape	See a measure over the cross-product of two groupings (such as a breakdown of quarterly sales by region).
Heat Map	See items in groups, with the space and coloring based on data metrics. This may help understand how groups perform versus each other, for example progress on fault reports grouped by type of fault.
Data Sheet	See the underlying raw data and for visually correlating across multiple measures (for example, does a discount increase as the transaction amount increases).
Parabox	See multiple metrics and/or multiple groupings relate (for example, how do the offensive statistics of a sports league's highest-paid player compare to the lowest-paid players, and where within the league are these players).
Data Constellations	See relationships between items. A variety of relationships may be viewed:
	- Multiple levels of a hierarchy in a single display (for example, what were actual sales as a percent of forecast at the company, subsidiary, division and department levels).
	- Pair-wise relationships (for example, what are the purchasing affinities between products).
Time Table	See events over time at a granular level (for example, server alarms for network intrusion detection). Events may be grouped.
Map	See data displayed geographically (like airline flight numbers by city) and look for possible relationships between the geographical entities (like flight delays between cities).
Counts	Statistics on fields (like total sales for a selected area of interest) and the meta data (type and cardinality of a field, for example).