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- HSS-ID.B.5Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
- HSS-ID.B.6Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.*

Scatterplots and Non-Linear Data

Algebra I

» Unit:

Modeling With Statistics

Big Idea:In this lesson students discover that some bivariate data should not be modeled by linear functions. Other functions are considered.

Got Ups? A Statistics Unit Task

Algebra I

» Unit:

Modeling With Statistics

Big Idea:Students are able to demonstrate all that they have learned throughout the statistics unit in this open-ended performance task.

Expore Correlation on Gapminder

12th Grade Math

» Unit:

Statistics: Bivariate Data

Big Idea:Gapminder (http://www.gapminder.org/) is a powerful tool that packs a lot of data into one space.

Using a Scatterplot to Model Data

Algebra I

» Unit:

Modeling With Statistics

Big Idea:Students collect and organize bivariate data and determine if a correlation between the variables exists.

Predicting the Height of a Criminal (Day 1 of 2)

Algebra I

» Unit:

Linear Functions

Big Idea:The fun part of this lesson is to introduce to students that the femur length of a person is directly proportional to their height.

Predicting the Height of a Criminal (Day 2 of 2)

Algebra I

» Unit:

Linear Functions

Big Idea:On Day 2 students complete the analysis and compare prediction equations calculated by hand and on the TI-Nspire calculator.

A Bivariate Relationship

Algebra I

» Unit:

Modeling With Statistics

Big Idea:Students estimate a line of best fit and write a prediction equation modeling the data. Students then use a calculator to determine a line of best fit, before comparing the two equations.

Cinderella's Slipper: Scatterplots, Residuals and Goodness of Fit

Algebra I

» Unit:

Our City Statistics: Who We Are and Where We are Going

Big Idea:Students explore the idea of Goodness of Fit for different data sets and learn to fit data that can be modeled with linear associations!

Correlation and Causation

Algebra I

» Unit:

Our City Statistics: Who We Are and Where We are Going

Big Idea:Students will distinguish between correlation and causation by analyzing relevant real life examples!

Our City Statistics Project and Assessment

Algebra I

» Unit:

Our City Statistics: Who We Are and Where We are Going

Big Idea:Students demonstrate interpersonal and data literacy skills as use statistics to learn about their community.

Battery Life

Algebra I

» Unit:

Multiple Representations: Situations, Tables, Graphs, and Equations

Big Idea:Will the digital devices run out of charge on the way to school? Students reason and make predictions based on a graph and compare the charge of a cell phone and a video game player.

Introduction to Scatter Plots, Line of Best Fit, and the Prediction Equation

Algebra I

» Unit:

Linear Functions

Big Idea:The emphasis in this lesson is to take students a little beyond the basics of Scatter Plots to explain the correlation coefficient (r) and the coefficient of determination (r squared).

Predicting Water Park Attendance

Algebra I

» Unit:

Multiple Representations: Situations, Tables, Graphs, and Equations

Big Idea:From scatterplot to predictions. Students plot data, approximate a line of best fit, generate an equation for the line to make predictions.

How does this fit? CalculatingCorrelation

Algebra I

» Unit:

Our City Statistics: Who We Are and Where We are Going

Big Idea:Students will using statistics to understand the goodness of fit for a linear model of bivariate data.

HSS-ID.B.6a

Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.

HSS-ID.B.6b

Informally assess the fit of a function by plotting and analyzing residuals.

HSS-ID.B.6c

Fit a linear function for a scatter plot that suggests a linear association.