few_shots: 
Example:
Concept/skill: 
Explain why a fraction a/b is equivalent to a fraction (n × a)/(n × b) by using visual fraction models, with attention to how the number and size of the parts differ even though the two fractions themselves are the same size. Use this principle to recognize and generate equivalent fractions.
Grade 4 expectations in this domain are limited to fractions with denominators 2, 3, 4, 5, 6, 8, 10, 12, and 100.

Problem/activity:
Task
Cruz and Erica were both getting ready for soccer.
Cruz ran 1 lap around the school.
Erica ran 3 laps around the playground.
Erica said,
I ran more laps, so I ran farther.
Cruz said,
4 laps around the school is 1 mile, but it takes 12 laps around the playground to go 1 mile. My laps are much longer, so I ran farther.
Who is right? Draw a picture to help you explain your answer.


Answer: 
Yes. This task asks students to compare two fractions that are equal. The students need to explain why the two fractions are equal by drawing a picture, which involves using visual fraction models.

Now, determine whether the following problem or activity can enable students to understand the concepts or skills in the provided description.

shot_info: im_task_000212_pos_0

4
standard_description: 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.
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.


aligns: yes