**Name** |
**Description** |

Basketball - it's a tall man's sport - or is it? | Basketball is a tall man's sport in most regards. Shooting, rebounding, blocking shots - the taller player seems to have the advantage. But is that still true when shooting free throws?
The students will use the data of NBA players to construct scatter plots to determine if there is a correlation between the height of a basketball player and his free throw percentage. The students will use technology to create the graphs, find the regression line and calculate the correlation coefficient. |

Compacting Cardboard | Students with investigate the amount of space that could be saved by flattening cardboard boxes. The analysis includes linear graphs and regression analysis along with discussions of slope and a direct variation phenomenon. |

Hand Me Your Data | Students will gather and use data to calculate a line of best fit and correlation coefficient with their classmates' height and hand size. They will use their line of best fit to make approximations. |

Heart Rate and Exercise: Is there a correlation? | In this lesson, Algebra 1 students will use supplied heart rate data to determine if heart rate and the amount of time spent exercising each week are correlated. Students will create their own scatter plots and lines of best fit for the data and study correlation using GeoGebra. Students will gather evidence to support or refute statistical statements made about correlation. The lesson provides easy to follow steps for using GeoGebra, a free online application, to generate a correlation coefficient for two given variables. |

Height Scatterplot Lab | This lesson is a Follow Up Activity to the Algebra Institute and allows students to apply their skills on analyzing bivariate data. This STEM lesson allows students the opportunity to investigate if there is a linear relationship between a person's height and foot length. Using technology the students can determine the line of best fit, correlation coefficient and use the line for interpolation. |

How technology can make my life easier when graphing | Students will use GeoGebra software to explore the concept of correlation coefficient in graphical images of scatter plots. They will also learn about numerical and qualitative aspects of the correlation coefficient, and then do a matching activity to connect all of these representations of correlation coefficient. They will use an interactive program file in GeoGebra to manipulate the points to create a certain correlation coefficient. Step by step instructions are included to create the graph in GeoGebra and calculate the correlation coefficient "R." |

Scatter Plots and Correlations | In this lesson, students will interpret and analyze data to create a scatter plot and line of best fit. Students will make predictions for the number of views of a video for any given number of weeks on the charts.
The lesson provides suggestions for finding the line of best fit using different technologies to graph, GeoGebra free online software, Excel spreadsheets, and graphing calculators. Teachers can determine which technology will best suit their class or incorporate all three as part of the lesson. |

Scrambled Coefficient | Students explore correlation of data through an activity allowing them to order situations from negative correlation to positive correlation. Students make an initial prediction of order given just the written situation and make adjustments to the order as each component is introduced: data table and scatter plot, line of best fit, correlation coefficient. Discussion after each step allows students to explain how they change their predictions as they are given more information. At the end of the lesson, students are provided with a real life example of how correlation coefficient is used to determine strength of relationships among real data.
Students will learn how to use the Linear Regression feature of graphing calculators to find the true line of best fit and the correlation coefficient.
The lesson includes the guided card sorting task, a formative assessment, and a summative assessment. |

Span the Distance Glider - Correlation Coefficient | This lesson will provide students with an opportunity to collect and analyze bivariate data and use technology to create scatter plots, lines of best fit, and determine the correlation strength of the data being compared. Students will have a hands on inquire based lesson that allows them to create gliders to analyze data. This lesson is an application of skills acquired in a bivariate unit of study. |

Steel vs. Wooden Roller Coaster Lab | This lesson is a Follow Up Activity to the Algebra Institute and allows students to apply their skills on analyzing bivariate data. This STEM lesson allows students the opportunity to investigate if there is a linear relationship between a coaster's height and speed. Using technology the students can determine the line of best fit, correlation coefficient and use the line for interpolation. This lesson also uses prior knowledge and has students solve systems of equations graphically to determine which type of coaster is faster. |

Study of Crowd Ratings at Disney | In this lesson, students develop a strong use of the vocabulary of correlation by investigating crowd ratings for a month at Disney. Students will find weekly crowd rating regression lines and regression correlations and discuss what this means for a Disney visit. |

Why Correlations? | This lesson is an introductory lesson to correlation coefficients. Students will engage in research prior to the teacher giving any direct instruction. The teacher will provide instruction on how to find the correlation coefficient by hand and using Excel. |

Why do I have to have a bedtime? | This is a predict, observe, explain type lesson that allows students to make predictions based on prior knowledge, observe both the teacher and their peers in order to create a discussion, and receive the opportunity to express themselves and their ideas while explaining what they learned. Students will be participating in an activity where they will collect data after making a prediction and then construct a scatter plot. From the scatterplot, students will make an interpretation of the data by calculating the correlation coefficient (r value) and deciding if there is a correlation or not in terms of its strength and magnitude, then explaining what that means. |