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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

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

Analyzing Residuals

Algebra I

» Unit:

Modeling With Statistics

Big Idea:So how good is your line of best fit? Students interpret residuals for a line of best fit using online applets.

James Bialasik

Suburban Env.

14 Resources

6 Favorites

14 Resources

6 Favorites

Intro to Stats

Algebra II

» Unit:

Statistics

Big Idea:Liars, Darn Liars, and Statisticians…but stats don’t really lie, they’re just easily manipulated.

Merrie Rampy

Rural Env.

9 Resources

8 Favorites

9 Resources

8 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

In the Middle

Algebra II

» Unit:

Statistics

Big Idea: Too much data! Too many numbers! Use a frequency distribution to find the mean.

Merrie Rampy

Rural Env.

11 Resources

2 Favorites

11 Resources

2 Favorites

What's Normal

Algebra II

» Unit:

Statistics

Big Idea:What's normal, anyway? How does being normal have anything to do with mathematics?

Merrie Rampy

Rural Env.

9 Resources

1 Favorite

9 Resources

1 Favorite

Understanding the Correlation Coefficient

Algebra I

» Unit:

Data and Statistics

Big Idea:Students "uncover" the meaning of the correlation coefficient (r) by graphing and examining a variety of data sets.

Amanda Hathaway

Urban Env.

13 Resources

4 Favorites

13 Resources

4 Favorites

Outliers and Outsiders: The Impact on Data

Algebra I

» Unit:

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

Big Idea:Students real world relationships to gain understanding of the power of individual data points.

Jason Colombino

Urban Env.

14 Resources

14 Resources

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

How's Your Spread

Algebra II

» Unit:

Statistics

Big Idea:You want me to subtract and square how many numbers?!? Make the process of managing data less crazy using a frequency distribution.

Merrie Rampy

Rural Env.

13 Resources

1 Favorite

13 Resources

1 Favorite

Modeling with Non-Linear Data

Algebra I

» Unit:

Modeling With Statistics

Big Idea:Oh No! I dropped all of my skittles on the floor…how many have the “s” up and how many do not? This is the beginning of the study into non-linear regression.

James Bialasik

Suburban Env.

11 Resources

2 Favorites

11 Resources

2 Favorites

Linear Regression and Residuals

Algebra II

» Unit:

Statistics: Two Variables

Big Idea:Examining the size and distribution of errors made by a model can help us determine if the model is appropriate.

Colleen Werner

Suburban Env.

14 Resources

2 Favorites

14 Resources

2 Favorites

HSS-ID.C.7

Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.*

HSS-ID.C.8

Compute (using technology) and interpret the correlation coefficient of a linear fit.*

HSS-ID.C.9

Distinguish between correlation and causation.*