Prepare a jar that contains about 30–50 small objects, or display a picture of such a jar, such as the one in the activity’s Launch.
In this Warm-up, students compute absolute guessing errors using class data. The absolute errors they find here will be plotted in the next activity.
The absolute errors could be calculated and compiled in different ways, by hand or using technology, including a spreadsheet tool (MP5). For example, students could:
Tell students to guess the number of objects in the collection. If no collection is available, display the image and ask students to guess the number of snap cubes in the jar.
Collect all of the guesses from the students, and display them for all to see.
Read the Task Statement with the class. Make sure students understand what they are asked to compute. Consider arranging students in groups of 2–4 so students can split up the calculations, if desired. Provide access to calculators and spreadsheet technology.
Give each student a copy of the blackline master to record their calculations, then reveal the actual number of items in the collection. If using the image given in this lesson, the actual number of snap cubes in the jar is 47.
Students should compute at least 12 absolute guessing errors, and more if time permits.
Use the actual number of items to calculate the absolute guessing error of each guess, or how far the guess is from the actual number. For example, suppose the actual number of objects is 100.
Record the absolute guessing error of at least 12 guesses in Table A of the handout (or elsewhere, as directed by your teacher).
Sample response for 20 guesses, when the actual number of objects is 47:
| guess | absolute guessing error |
|---|---|
| 27 | 20 |
| 44 | 3 |
| 46 | 1 |
| 59 | 12 |
| 53 | 6 |
| 36 | 11 |
| 35 | 12 |
| guess | absolute guessing error |
|---|---|
| 65 | 18 |
| 50 | 3 |
| 62 | 15 |
| 58 | 11 |
| 28 | 19 |
| 38 | 9 |
| 30 | 17 |
| guess | absolute guessing error |
|---|---|
| 55 | 8 |
| 37 | 10 |
| 57 | 10 |
| 41 | 6 |
| 40 | 7 |
| 60 | 13 |
If desired, display a completed table for all to see, or simply invite students to share some observations about the absolute guessing errors they found. If no one mentioned that all the values are positive, ask them about it and solicit some ideas about why this is the case.
Also ask students if they could tell from the data how good the guesses were. (Were the guesses close? Were there a lot of overestimates or underestimates?)
Tell students that they will plot the data next.
Some students may record negative values for the absolute guessing errors of guesses that are lower than the actual number, not realizing that the term “absolute error” refers to “how far away” and, therefore, cannot be negative. Suggest that they revisit the examples in the Task Statement, and clarify the term as needed.
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Prepare a jar that contains about 30–50 small objects, or display a picture of such a jar, such as the one in the activity’s Launch.
In this Warm-up, students compute absolute guessing errors using class data. The absolute errors they find here will be plotted in the next activity.
The absolute errors could be calculated and compiled in different ways, by hand or using technology, including a spreadsheet tool (MP5). For example, students could:
Tell students to guess the number of objects in the collection. If no collection is available, display the image and ask students to guess the number of snap cubes in the jar.
Collect all of the guesses from the students, and display them for all to see.
Read the Task Statement with the class. Make sure students understand what they are asked to compute. Consider arranging students in groups of 2–4 so students can split up the calculations, if desired. Provide access to calculators and spreadsheet technology.
Give each student a copy of the blackline master to record their calculations, then reveal the actual number of items in the collection. If using the image given in this lesson, the actual number of snap cubes in the jar is 47.
Students should compute at least 12 absolute guessing errors, and more if time permits.
Use the actual number of items to calculate the absolute guessing error of each guess, or how far the guess is from the actual number. For example, suppose the actual number of objects is 100.
Record the absolute guessing error of at least 12 guesses in Table A of the handout (or elsewhere, as directed by your teacher).
Sample response for 20 guesses, when the actual number of objects is 47:
| guess | absolute guessing error |
|---|---|
| 27 | 20 |
| 44 | 3 |
| 46 | 1 |
| 59 | 12 |
| 53 | 6 |
| 36 | 11 |
| 35 | 12 |
| guess | absolute guessing error |
|---|---|
| 65 | 18 |
| 50 | 3 |
| 62 | 15 |
| 58 | 11 |
| 28 | 19 |
| 38 | 9 |
| 30 | 17 |
| guess | absolute guessing error |
|---|---|
| 55 | 8 |
| 37 | 10 |
| 57 | 10 |
| 41 | 6 |
| 40 | 7 |
| 60 | 13 |
If desired, display a completed table for all to see, or simply invite students to share some observations about the absolute guessing errors they found. If no one mentioned that all the values are positive, ask them about it and solicit some ideas about why this is the case.
Also ask students if they could tell from the data how good the guesses were. (Were the guesses close? Were there a lot of overestimates or underestimates?)
Tell students that they will plot the data next.
Some students may record negative values for the absolute guessing errors of guesses that are lower than the actual number, not realizing that the term “absolute error” refers to “how far away” and, therefore, cannot be negative. Suggest that they revisit the examples in the Task Statement, and clarify the term as needed.