Type:

Other

Description:

This lesson is designed to introduce students to the concept of chaos and how it relates to probability. The lesson briefly delves into the ideas of mean and variance as well. This lesson provides links to discussions and activities related to chaos as well as suggested ways to integrate them into the lesson. Finally, the lesson provides links to follow-up lessons designed for use in succession with the current one. Note, reading level is not indicated because the lesson does not include student reading material.

Subjects:

  • Mathematics > General

Education Levels:

  • Grade 1
  • Grade 2
  • Grade 3
  • Grade 4
  • Grade 5
  • Grade 6
  • Grade 7
  • Grade 8
  • Grade 9
  • Grade 10
  • Grade 11
  • Grade 12

Keywords:

Vocational/Professional Development Education,Higher Education,Middle School,Grade 7,Undergraduate (Upper Division),Grade 9,Undergraduate (Lower Division),NSDL_SetSpec_ncs-NSDL-COLLECTION-000-003-112-016,High School,Grade 10,Grade 11,Grade 12,Mathematics,oai:nsdl.org:2200/20120614151549224T,NSDL

Language:

English

Access Privileges:

Public - Available to anyone

License Deed:

Creative Commons Attribution Non-Commercial Share Alike

Collections:

None
Update Standards?

CCSS.Math.Practice.MP4: Common Core State Standards for Mathematics

Model with mathematics.

CCSS.Math.Content.7.SP.C.6: Common Core State Standards for Mathematics

Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability.

CCSS.Math.Content.HSS-IC.A.1: Common Core State Standards for Mathematics

Understand statistics as a process for making inferences about population parameters based on a random sample from that population.

CCSS.Math.Content.HSS-IC.B.6: Common Core State Standards for Mathematics

Evaluate reports based on data.

CCSS.Math.Content.HSS-MD.A.1: Common Core State Standards for Mathematics

(+) Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions.
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