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Course
3 Unit 5 - Patterns in Variation
1st Edition
Patterns in Variation is the sixth major unit from the statistics and probability strand of the Contemporary Mathematics in Context program. Measures of variation for single-variable data introduced in Course 1, Unit 1, were the range, interquartile range, mean absolute deviation, and percentiles. Box plots were introduced to help visualize variation within a distribution. In Course 3, Unit 2, Modeling Public Opinion, sampling distributions were summarized with 90% box plots. (See the descriptions of Course 3 Units.)
Unit Overview
Patterns in Variation extends student understanding of the measurement of variation, develops student ability to use the normal distribution as a model of variation, and introduces students to the probability and statistical inference involved in the control charts used in industry for statistical process control.
Unit Objectives
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Sample Overview
The sample material is from Lesson 2 of this unit. Students are introduced to the normal distribution and its characteristics (symmetry and the relationship between the mean and the median). Students learned to measure variation using the standard deviation in Lesson 1 and now in Lesson 2 learn to interpret the number of standard deviations from the mean as a measure of location.
Instructional Design
Throughout the curriculum, interesting problem contexts serve as the foundation for instruction. As lessons unfold around these problem situations, classroom instruction tends to follow a common pattern as elaborated under Instructional Design.
View Sample Material
You will need the free Adobe Acrobat Reader software to view and print the sample material.
- Unit Table of Contents (C3U5_TOC.pdf -
70 kb)
- Lesson 2 (C3U5_362-375.pdf - 1.1 Mb)
Contact Adobe with any technical questions about their software or its installation.
How the Statistics and Probability Strand Continues
In Course 4, students extend their understanding of the binomial distribution and how the binomial distribution is used in statistical inference to test a single proportion and to compare two treatments in an experiment.