Standard 3 : Summarizing Data (Descriptive Statistics) (Archived)



This document was generated on CPALMS - www.cpalms.org


Learn to work with summary measures of sets of data, including measures of the center, spread, and strength of relationship between variables. Learn to distinguish between different types of data and to select the appropriate visual form to present different types of data.

General Information

Number: MA.912.S.3
Title: Summarizing Data (Descriptive Statistics)
Type: Standard
Subject: X-Mathematics (former standards - 2008) - Archived
Grade: 912
Body of Knowledge: Statistics

Related Benchmarks

This cluster includes the following benchmarks
Code Description
MA.912.S.3.1: Read and interpret data presented in various formats. Determine whether data is presented in appropriate format, and identify possible corrections. Formats to include:
  • bar graphs
  • line graphs
  • stem and leaf plots
  • circle graphs
  • histograms
  • box and whiskers plots
  • scatter plots
  • cumulative frequency (ogive) graphs
MA.912.S.3.2: Collect, organize, and analyze data sets, determine the best format for the data and present visual summaries from the following:
  • bar graphs
  • line graphs
  • stem and leaf plots
  • circle graphs
  • histograms
  • box and whisker plots
  • scatter plots
  • cumulative frequency (ogive) graphs
MA.912.S.3.3: Calculate and interpret measures of the center of a set of data, including mean, median, and weighted mean, and use these measures to make comparisons among sets of data.
MA.912.S.3.4: Calculate and interpret measures of variance and standard deviation. Use these measures to make comparisons among sets of data.
MA.912.S.3.5: Calculate and interpret the range and quartiles of a set of data.
MA.912.S.3.6: Use empirical rulesĀ such as theĀ 68-95-99.7 rule to estimate spread of distributions and to make comparisons among sets of data.
MA.912.S.3.7: Calculate the correlation coefficient of a set of paired data, and interpret the coefficient as a measure of the strength and direction of the relationship between the variables.
MA.912.S.3.8: Determine whether a data distribution is symmetric or skewed based on an appropriate graphical presentation of the data.
MA.912.S.3.9: Identify outliers in a set of data based on an appropriate graphical presentation of the data, and describe the effect of outliers on the mean, median, and range of the data.


Related Access Points

This cluster includes the following access points.

Independent

Access Point Number Access Point Title
MA.912.S.3.In.a: Describe information in bar graphs, circle graphs, and single-line graphs representing data from real-world situations.
MA.912.S.3.In.b: Collect data and display in single-line graphs, circle graphs, and bar graphs.
MA.912.S.3.In.c: Determine the mode by identifying the number that occurs most often and the mean by finding the average.
MA.912.S.3.In.d: Calculate the range and median for data from real-world situations.

Supported

Access Point Number Access Point Title
MA.912.S.3.Su.a: Identify information in simple pictographs and bar graphs that represent data from real-world situations.
MA.912.S.3.Su.b: Organize data in pictographs and bar graphs and identify the labels for categories.
MA.912.S.3.Su.c: Identify the number that occurs most frequently (mode) in a set of data with up to nine numbers.
MA.912.S.3.Su.d: Find the difference between the largest and smallest numbers in a set of data (range) and the median in a real-world situation.

Participatory

Access Point Number Access Point Title
MA.912.S.3.Pa.a: Identify quantity in data sets of 10 by counting objects, pictures, or symbols and identify which category has more, less, or none.


Related Resources

Vetted resources educators can use to teach the concepts and skills in this topic.

Lesson Plan

Name Description
Radioactive Decay: Is It Safe for Us to Stay?:

Students will collect data using inexpensive split peas and black beans in order to model how to calculate the amount of a radioactive element remaining after a specific number of half-lives have passed. Students will then use this data to outline and create a response to a scenario-based writing prompt.

Tutorial

Name Description
MIT BLOSSOMS - Flaws of Averages:

This learning video presents an introduction to the Flaws of Averages using three exciting examples: the "crossing of the river" example, the "cookie" example, and the "dance class" example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. Most students at any level in high school can understand the concept of the flaws of averages presented here. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. Materials needed include: pen and paper for the students; and a blackboard or equivalent. During this video lesson, students will learn about three flaws of averages: (1) The average is not always a good description of the actual situation, (2) The function of the average is not always the same as the average of the function, and (3) The average depends on your perspective. To convey these concepts, the students are presented with the three real world examples mentioned above. The total length of the four in-class video segments is 12 minutes, leaving lots of time in a typical class session for the teacher to work with the students on their own learning examples (such as those from the supplementary notes) to firm up the ideas presented here on the flaws of averages.

Virtual Manipulative

Name Description
Histogram Tool:

This virtual manipulative histogram tool can aid in analyzing the distribution of a dataset. It has 6 preset datasets and a function to add your own data for analysis.



Student Resources

Vetted resources students can use to learn the concepts and skills in this topic.

Virtual Manipulative

Title Description
Histogram Tool:

This virtual manipulative histogram tool can aid in analyzing the distribution of a dataset. It has 6 preset datasets and a function to add your own data for analysis.