The purpose of this task is to develop an understanding of the correlation coefficient. The task asks students to plot various data sets and use technology to calculate the correlation coefficient. They will order the graphs and create new data sets to develop the idea that the correlation coefficient indicates the strength and direction of a linear relationship in the data. Students also consider situations in which two variables are highly correlated, but the relationship is not necessarily causal.

Khan video explaining the correlation coefficient and what it means in terms of the data set.

Students will develop a set of tools for understanding and interpreting variability in data, and begin to make more informed decisions from data. They work with data distributions of various shapes, centers, and spreads. Students build on their experience with bivariate quantitative data from Grade 8. This module sets the stage for more extensive work with sampling and inference in later grades.

Texas Instruments activity that uses linear regression specifically to calculate a correlation coefficient.

Students will develop, justify, and evaluate conjectures about the relationship between two quantitative variables over time in the United States: the median age (in years) when women first marry and the percentage of women aged 25-34 with a bachelor's degree or higher.

Students find common ratios of geometric sequences on a spreadsheet and create scatter plots of the sequences to see how each curve is related to the value of the common ratio and/or the sign of the first term of the sequence.

The purpose of this task is to solidify students understanding of linear models for data by interpreting the slopes and intercepts of regression lines with various units. Students are asked to use linear models to compare and analyze data. In the task they draw conclusions and justify arguments about data. In addition they are asked to consider additional data that could be collected to inform their conclusions.

The purpose of this task is to solidify understanding of correlation coefficient and to develop linear models for data. Students are asked to estimate and calculate correlation coefficients. In the task they estimate lines of best fit and then compare them to the calculated linear regression. The task demonstrates the dangers of using a linear model to extrapolate well beyond the actual data. The task ends with an opportunity to use the correlation coefficient and scatter plot to determine the appropriateness of a linear model.

This task explores the real world topic of building light rails. Throughout the implementation of this task the students will learn about the cost of building railways and how to implement them within a budget. This task explores such mathematical concepts of using coordinates to find the distance between points, using coordinates to build polygons and find the area and length of sides, and writing equations of parallel lines.

Students are introduced to modeling linear data through an investigation of comparing grams of fat and calories in fast food hamburgers.