In the Ball Bounce task, teams of three students collect real-time ball bounce data for two minutes to organize, display and use to form predictions. After collecting and organizing their data, students first observe how the data behave. Then they make predictions using data, scatterplots, or the equation of the curve of best fit to solve a
practical problem. This task offers students collaborative experience in using mathematical models of linear functions.

Students research two variables about the car model they intend to purchase. They use Desmos to compare these same variables across five different car models. Students use analysis of bivariate data in order to decide which car makes sense to buy. Students prepare and share a summary statement and include desmos screen shots in order to justify their purchase. This task offers students experiences in making predictions, using data, scatterplots, or the equation of the curve of best fit for decision-making in a practical problem.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.

Developing a curve of best fit for dataMathematics Instructional Plans (MIPs) help teachers align instruction with the 2016 Mathematics Standards of Learning (SOL) by providing examples of how the knowledge, skills and processes found in the SOL and curriculum framework can be presented to students in the classroom.

Determining a curve of best fit Mathematics Instructional Plans (MIPs) help teachers align instruction with the 2016 Mathematics Standards of Learning (SOL) by providing examples of how the knowledge, skills and processes found in the SOL and curriculum framework can be presented to students in the classroom.

In this module, students reconnect with and deepen their understanding of statistics and probability concepts first introduced in Grades 6, 7, and 8. There is variability in data, and this variability often makes learning from data challenging. Students develop a set of tools for understanding and interpreting variability in data and begin to make more informed decisions from data. Students work with data distributions of various shapes, centers, and spreads. Measures of center and measures of spread are developed as ways of describing distributions. The choice of appropriate measures of center and spread is tied to distribution shape. Symmetric data distributions are summarized by the mean and mean absolute deviation, or standard deviation. The median and the interquartile range summarize data distributions that are skewed. Students calculate and interpret measures of center and spread and compare data distributions using numerical measures and visual representations.
To access this resource, you will need to create a free account for the system on which it resides. The partner provides personalized features on their site such as bookmarking and highlighting which requires a user account.

In Module 5, students synthesize what they have learned during the year by selecting the correct function type in a series of modeling problems without the benefit of a module or lesson title that includes function type to guide them in their choices. Skills and knowledge from the previous modules support the requirements of this module, including writing, rewriting, comparing, and graphing functions and interpretation of the parameters of an equation. Students also draw on their study of statistics in Module 2, using graphs and functions to model a context presented with data and tables of values. In this module, we use the modeling cycle as the organizing structure rather than function type.
To access this resource, you will need to create a free account for the system on which it resides. The partner provides personalized features on their site such as bookmarking and highlighting which requires a user account.

This is a hands on lesson to get students up and moving while also learning about calculating quadratic regressions.  It incorporates techology such as DESMOS and/or CODAP to get students comfortable with these programs and loooking at data in different ways.