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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.

James Bialasik

Suburban Env.

13 Resources

11 Favorites

13 Resources

11 Favorites

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.

James Bialasik

Suburban Env.

15 Resources

22 Favorites

15 Resources

22 Favorites

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.

James Dunseith

Urban Env.

20 Resources

6 Favorites

20 Resources

6 Favorites

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.

James Bialasik

Suburban Env.

14 Resources

10 Favorites

14 Resources

10 Favorites

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.

Rhonda Leichliter

Rural Env.

11 Resources

4 Favorites

11 Resources

4 Favorites

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.

Rhonda Leichliter

Rural Env.

11 Resources

2 Favorites

11 Resources

2 Favorites

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.

James Bialasik

Suburban Env.

10 Resources

9 Favorites

10 Resources

9 Favorites

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!

Jason Colombino

Urban Env.

21 Resources

8 Favorites

21 Resources

8 Favorites

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!

Jason Colombino

Urban Env.

18 Resources

11 Favorites

18 Resources

11 Favorites

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.

Jason Colombino

Urban Env.

17 Resources

11 Favorites

17 Resources

11 Favorites

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.

Amanda Hathaway

Urban Env.

13 Resources

2 Favorites

13 Resources

2 Favorites

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).

Rhonda Leichliter

Rural Env.

11 Resources

4 Favorites

11 Resources

4 Favorites

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.

Amanda Hathaway

Urban Env.

11 Resources

6 Favorites

11 Resources

6 Favorites

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.

Jason Colombino

Urban Env.

14 Resources

2 Favorites

14 Resources

2 Favorites

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.