# Lesson 10

Putting It All Together

These materials, when encountered before Algebra 1, Unit 3, Lesson 10 support success in that lesson.

## 10.1: Which One Doesn’t Belong: Data Correlations (5 minutes)

### Warm-up

This warm-up prompts students to compare four representations of data. It gives students a reason to use language precisely (MP6). It gives the teacher an opportunity to hear how students use terminology and talk about characteristics of the items in comparison to one another.

### Launch

Arrange students in groups of 2–4. Display the data representations for all to see. Give students 1 minute of quiet think time and then time to share their thinking with their small group. In their small groups, ask each student to share their reasoning why a particular item does not belong, and together find at least one reason each item doesn’t belong.

### Student Facing

Which one doesn’t belong?

D
$$x$$ $$y$$
3 6
3.75 8.50
7.25 7.50
5.50 11
6 9
8 10.25

### Activity Synthesis

Ask each group to share one reason why a particular item does not belong. Record and display the responses for all to see. After each response, ask the class if they agree or disagree. Since there is no single correct answer to the question of which one does not belong, attend to students’ explanations and ensure the reasons given are correct. During the discussion, ask students to explain the meaning of any terminology they use, such as association, correlation, linear model, strong relationship, and weak relationship. Also, press students on unsubstantiated claims.

## 10.2: Electric Power (15 minutes)

### Optional activity

The mathematical purpose of this activity is for students to practice synthesizing the component skills needed to interpret data. Students are presented with a scatter plot, table, and some concluding statements about the data. Students critique the concluding statements, and explain their reasoning (MP3).

### Launch

Try to gauge students’ current understanding of the context of utility bills. Ask them, “What type of relationship do you think makes sense for energy consumption and electric bill prices?” Students should understand that the more energy is used, the higher the utility bill. A sample response to this question is, “I think there’s a positive relationship between the two because I think as the energy consumption increases, then the electric bill increases also.”

### Student Facing

Here are Elena’s representations of the data set.

energy (kwh) electric bill price (dollars)
500 50
560 57.60
610 65.10
675 70.25
700 74.80
755 90.66
790 92.34
836 105
892 150
940 173
932 182

energy (kwh) electric bill price (dollars)
967 170
999 198
1,005 201.22
1,039 215.35
1,057 217
1,100 233
1,191 284.62
1,150 256.98
1,200 289.60
1,270 292

After analyzing the data, Elena concludes:

1. An estimate for the correlation coefficient for the line of best fit is $$r = \text-0.98$$.
2. Energy consumption and the price of electric bills have a positive relationship.
3. Energy consumption and the price of electric bills have a weak relationship.

## 10.3: Confident Players (20 minutes)

### Optional activity

The mathematical purpose of this activity is for students to practice synthesizing the component skills needed to interpret data. Students are given a data set presented in a table and they practice creating a scatter plot, calculating the correlation coefficient with technology, and drawing conclusions about the data.

### Launch

Ensure that students have access to appropriate technology to input data, create a scatter plot, and calculate the correlation coefficient.

### Student Facing

Before Diego’s game, his coach asked each of his players, “On a scale of 1–10, how confident are you in the team winning the game?” Here is the data he collected from the team.

players   confidence in winning (1–10)   number of points scored in a game
Player A  3 2
Diego 6 10
Player B 10 2
Player C 4 10
Player D 7 13
Player E 5 6
Player F 8 15
Player G 4 3
Player H 9 15
Player I 7 12
Player J 1 0
Player K 9 14
Player L 8 13
Player M 5 8
1. Use technology to create a scatter plot, a line of best fit, and the correlation coefficient.
2. Is there a relationship between players’ level of confidence in winning and the amount of points they score in a game? Explain your reasoning.
3. How many points does the linear model predict a player will score when his or her confidence is at a 4?
4. Which players performed worse than the model predicted?
5. Did Diego score better or worse than the linear model predicts?