Created on: March 28, 2015

Website Address: https://library.curriki.org/oer/Cluster--MA8CCSSMathContent8SP-Statistics-and-Probability

TABLE OF CONTENTS

- Cluster - MA.8.CCSS.Math.Content.8.NS The Number System
- Curriki Project Based Geometry
- Cluster - MA.8.CCSS.Math.Content.8.EE Expressions and Equations
- Cluster - MA.8.CCSS.Math.Content.8.F Functions
- Cluster - MA.8.CCSS.Math.Content.8.G Geometry
- Cluster - MA.8.CCSS.Math.Content.8.SP Statistics and Probability

- Quiz - 8.NS The Number System Cluster: Know that there are numbers that are not rational, and approximate them by rational numbers. Standards: 8.NS.1, 8.NS.2
- 8.NS.1 Know that numbers that are not rational are called irrational.
- 8.NS.2 Use rational approximations of irrational numbers to compare the size of irrational numbers.

- Selling Geometry
- Designing a Winner
- What's Your Angle, Pythagoras?
- TED Talk: House of the Future
- The Art of Triangles
- How Random Is My Life?
- Introduction to Angles
- Lesson -- Drawing right triangles from word problems
- Accessing Curriki Geometry Projects
- Introduction to Curriki Geometry

- Quiz - 8.SP Statistics and Probability: Investigate patterns of association in bivariate data: Standards: 8.SP.1, 8.SP.2, 8.SP.3, 8.SP.4
- 8.SP.1 Investigate patterns of association in bivariate data: Construct and interpret scatter plots
- 8.SP.2 Investigate patterns of association in bivariate data: Straight lines as models
- 8.SP.3 Investigate patterns of association in bivariate data: Use the equation of a linear model to solve problems
- 8.SP.4 Investigate patterns of association in bivariate data: Frequencies and relative frequencies

IN COLLECTION

Contents align to MA.8.CCSS.Math.Content.8.SP Statistics and Probability

Standards: 8.SP.1, 8.SP.2, 8.SP.3, 8.SP.4

Investigate patterns of association in bivariate data: Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.

Investigate patterns of association in bivariate data: Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.

Investigate patterns of association in bivariate data: Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.

Investigate patterns of association in bivariate data: Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?