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This free course, Interpreting data: Boxplots and tables, is concerned with two main topics. In Section 1, you will learn about another kind of graphical display, the boxplot. A boxplot is a fairly simple graphic, which displays certain summary statistics of a set of data. Boxplots are particularly useful for assessing quickly the location, dispersion, and symmetry or skewness of a set of data, and for making comparisons of these features in two or more data sets. Boxplots can also be useful for drawing attention to possible outliers in a data set. The other topic, which is covered in Sections 2 and 3, is that of dealing with data presented in tabular form. You are, no doubt, familiar with such tables: they are common in the media and in reports and other documents. Yet it is not always straightforward to see at first glance just what information a table of data is providing, and it often helps to carry out certain calculations and/or to draw appropriate graphs to make this clearer. In this free course, some other kinds of data tables and some different approaches are covered.
Syllabus
- Introduction
- Learning outcomes
- Overview
- 1 Boxplots
- 1 Boxplots
- 1.1 Simple boxplots
- 1.2 Boxplot activity
- 1.3 Comparing data sets using boxplots
- 1.4 Boxplot activity 2
- 1.5 Summary
- 1.6 Exercise
- 2 Producing useful tables
- 2 Producing useful tables
- 2.1 Data sets in different tabular forms
- 2.2 Basic table layout
- 2.3 Table activity
- 2.4 Including the results of useful calculation
- 2.5 Early retirement from the National Health Service
- 2.6 Summary
- 3 Interpreting data in table
- 3 Interpreting data in table
- 3.1 Health personnel in Thailand
- 3.2 Health care personnel in Thailand: activities
- 3.3 HIV testing in sub-Saharan Africa
- 3.4 Guidelines for graphics
- 3.5 The British Crime Survey
- 3.6 Summary of Section 3
- 4 Conclusion
- 4 Conclusion
- Acknowledgements
Tags
Reviews
5.0 rating, based on 1 Class Central review
4.5 rating at OpenLearn based on 4 ratings
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The content proved to be a rigorous and practical exploration of essential statistical tools. The material adeptly demystifies the construction and interpretation of boxplots, clarifying components like the median, quartiles, and adjacent values, which are fundamental for assessing location, dispersion, and skewness. The systematic approach for comparing data sets using these visualizations is particularly powerful. Furthermore, the sections on producing and interpreting tables transform raw data into clear, communicative summaries by emphasizing thoughtful layout, simplification, and the calculation of insightful rates and percentages.