The mathematical purpose of this activity is for students to collect, summarize, interpret, and draw conclusions from bivariate data using scatter plots, best fit lines, residuals and correlation coefficients. Students measure the approximate lengths of the humerus bone and heights of their classmates to collect data and create a linear model. The model is then used to approximate the height of an ancient human based on the length of a found humerus bone.

Making graphing technology available gives students an opportunity to choose appropriate tools strategically (MP5). By collecting their own data and using a best fit line to find additional information, students are modeling with mathematics (MP4).

The purpose of this discussion is for students to communicate how they used mathematics to justify their findings.

Ask students:

• “How confident are you in your answer? What information helped you determine your confidence?” (Not very confident. Since the correlation coefficient is near 0.6, there is only a moderate relationship between height and humerus length. Additionally, this ancient human ancestor may have a different anatomy—for example, apes tend to have proportionally longer arms than humans do.)
• “How did you use mathematics to estimate the height of the ancient human?” (First, I collected data on the height and the approximate length of the humerus of my classmates. I then made a scatter plot to determine whether or not a linear model was appropriate and then computed a line of best fit. I substituted 24 centimeters into my line of best fit and obtained my answer.)
• “Do you think that the way you measured the humerus of your classmates impacted your choice of linear model? Explain your reasoning.” (Yes, I think that I probably overestimated the bone length because I was measuring from the outside and not the inside. I think that my model likely overestimates height.)

The mathematical purpose of this activity is for students to collect, summarize, interpret, and draw conclusions from bivariate data using scatter plots, best fit lines, residuals, and correlation coefficients.

Students could examine number of penalties (such as number of flags thrown against a football team) or severity of penalties (such as penalty yards assessed against a football team).

Making graphing technology available gives students an opportunity to choose appropriate tools strategically (MP5). By selecting which variables to use for their model and finding additional data to support a claim, students are modeling with mathematics (MP4).

The purpose of this discussion is for students to communicate how they used mathematics to justify their findings.

If students are able to research their own data, tell the groups to create a visual display to present their findings. Some questions for discussion are listed here:

• “If there is a negative correlation, do you think the penalties would cause fewer wins?” (Although it may be part of the cause, it is generally not the main reason a team loses several games.)
• “Were there any interesting data points you would like to research more about?” (Some points were very far away from the rest of the data and might be interesting to look at more closely.)
• “How did you reach a conclusion? Explain your reasoning.” (I created a scatter plot from data that I collected. I then plotted the residuals to verify that a linear model would be appropriate. I computed a line of best fit and a correlation coefficient and found that there was a weak relationship between penalties and wins because the correlation coefficient was between 0 and 0.5.)