Students are sitting in homogeneous pairs for this lesson. I will assign pairs based on my assessment of students' ability to write the equation of a line between two points. In yesterday's lesson, the Independent Practice focused on this skill.
I begin this lesson by showing students the title slide of Scatterplot_Day 2. I ask students to n write down their own definition of correlation and causation in their notebooks. The students have seen the term correlation in the previous lesson. If necessary, I may also remind them of our investigation of hours of sleep and grades in school to jog their memories.
I encourage my students to use the root word, cause, in preparing a definition of causation. Since this is an important idea, that students seldom get right the first time, we will do a Think-Pair-Share to further develop their thinking. I find that when student have sufficient time to think about it, some are able to come to the conclusion that there can be a correlation between any two quantitative variables, but causation is something different. Causation implies that a change in one variable causes a predictable change in the value of the other variable.
In order to check students' level of comfort with these ideas, I do a informal assessment using a non-verbal cue. I put a #1 under the word correlation on the board and a #2 under the word causation. Then, as I display a description of a set of bivariate data, students either hold up one finger or two to show their choice of correlation or causation. This quick activity will help me to gauge student understanding of the concepts before moving on with the lesson.
To close this lesson, I want students to think about examples of correlation and then come up with one of their own. We have looked at several examples over the last two days. The students should have a pretty good sense of what types of situations might have correlation. I will ask students do this individually, after I display Slide 3 from Scatterplot_Day 2. If time permits, I want to have students share their examples. I will list them on the board by type of correlation (positive, negative, none).