Using a Combination of Statistical Information Presented in Different Diagrams

Interpreting Statistical Diagrams:

Familiarise yourself with several types of statistical diagrams: bar graphs, line graphs, pie charts, histograms, and scatterplots.
These diagrams serve different purposes, and can provide various insights depending on how data is presented.
Bar graphs compare different groups, while line graphs show trends over time.
Pie charts represent proportions of a whole, while histograms display frequency distributions of continuous data.
Scatterplots are used to identify possible relationships between two variables.
It’s vital to analyse the axes, scales, labels of the diagrams, and the key if provided.

Comparative Analysis of Diagrams:

Different diagrams may present the same data in various manners. Understanding these different representations is key to comparative analysis.
Identify commonalities and differences between diagrams - this can involve comparing values, spotting patterns or contrasting trends over time.
Learn to discern whether likenesses or variances between diagrams represent significant patterns that make sense within context.

Using Multiple Diagrams:

Sometimes, more than one diagram is necessary to give a comprehensive understanding of the data.
Cross-reference information between different diagrams. A pattern noticed in one diagram might become more meaningful when corroborated or further detailed in another.
Practice deciphering and connecting information presented across multiple diagrams. This skill is valuable for comprehensive data analysis.

Synthesising Information:

Digest and synthesise information from a variety of diagrams.
This involves interpreting trends, making inferences, and contextualising the data to understand their broader implications.
The ability to collate information from diverse diagrams into a meaningful whole supports comprehensive data interpretation, which can then influence decision making.

Remember, utilising statistical information from different diagrams is a skill that involves recognising different types of statistical diagrams, comparing and cross-referencing between them, and synthesising the information presented into a cohesive understanding. It is key for thorough and comprehensive data analysis.