few_shots: Example:
Problem/activity:
Narrative
The purpose of this activity is for students to create composite shapes and compose new shapes from composite shapes. For example, students may find that a rectangular prism can be made from four triangular prisms or from eight smaller cubes. Students then discover that they can combine two of the composed rectangular prisms to make a new rectangular prism.
Required Materials
Materials to Gather
Geoblocks
Launch
Groups of 4
Give each group geoblocks.
Display a large cube.
“This cube can be built from other shapes. See how many ways you can build a cube using other shapes.”
3 minutes: partner work time
Share responses.
Activity
“Choose a different shape. Try to find smaller shapes you can use to build that shape.”
5 minutes: partner work time
Student Response
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Activity Synthesis
Display a cube and a composite cube—for example, a cube composed of four small cubes and one rectangular prism.
Demonstrate putting the shapes together.
“What new shape did I make with the cube and the cube I made?”
“Put your shapes together to make a rectangular prism.”
Display a rectangular prism and two triangular prisms.
“Find these shapes. Put them together. What new shape can you make?” (a cube, a rectangular block)


Standard options: 
A. Partition circles and rectangles into two and four equal shares, describe the shares using the words halves, fourths, and quarters, and use the phrases half of, fourth of, and quarter of. Describe the whole as two of, or four of the shares. Understand for these examples that decomposing into more equal shares creates smaller shares.
B. Distinguish between defining attributes (e.g., triangles are closed and three-sided) versus non-defining attributes (e.g., color, orientation, overall size); build and draw shapes to possess defining attributes.
C. Compose two-dimensional shapes (rectangles, squares, trapezoids, triangles, half-circles, and quarter-circles) or three-dimensional shapes (cubes, right rectangular prisms, right circular cones, and right circular cylinders) to create a composite shape, and compose new shapes from the composite shape. (Students do not need to learn formal names such as “right rectangular prism.”)


Your response:
C

Now, assign the following problem or activity to one or more relevant standards it aligns with.
shot_info: im_lesson_009431

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options: Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.

Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?

Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.

Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
problem_activity: Task
Is there an association between the weight of an animal’s body and the weight of the animal’s brain? 1. Make a scatterplot using the following data.
|      Animal      | Body Weight (kg) | Brain Weight (g) |
|------------------|------------------|------------------|
| Mountain beaver  |       1.35       |       8.1        |
|       Cow        |       465        |       423        |
|    Grey wolf     |      36.33       |      119.5       |
|       Goat       |      27.66       |       115        |
|    Guinea pig    |       1.04       |       5.5        |
|  Asian elephant  |       2547       |       4603       |
|      Donkey      |      187.1       |       419        |
|      Horse       |       521        |       655        |
|   Potar monkey   |        10        |       115        |
|       Cat        |       3.3        |       25.6       |
|     Giraffe      |       529        |       680        |
|     Gorilla      |       207        |       406        |
|      Human       |        62        |       1320       |
| African elephant |       6654       |       5712       |
|  Rhesus monkey   |       6.8        |       179        |
|     Kangaroo     |        35        |        56        |
|  Golden hamster  |       0.12       |        1         |
|      Mouse       |      0.023       |       0.4        |
|      Rabbit      |       2.5        |       12.1       |
|      Sheep       |       55.5       |       175        |
|      Jaguar      |       100        |       157        |
|    Chimpanzee    |      52.16       |       440        |
|       Mole       |      0.122       |        3         |
|       Pig        |       192        |       180        |
Do there appear to be outliers in this data? Which animals appear to be outliers? Explain how you identified these outliers.
Removing the outliers from the data set, make a new scatterplot of the remaining animal body and brain weights.
Does there appear to be a relationship between body weight and brain weight? If yes, write a brief description of the relationship.
Take a piece of uncooked spaghetti and use that spaghetti to informally fit a line to the data. Attempt to place your line so that the vertical distances from the points to the line are as small as possible.
How well does the spaghetti line appear to fit the data? Explain.


correct_option_index: 2

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