few_shots: 
Example:
Problem/activity:
Find the general solution for:
$${\mathrm{cos}\left({\pi\over2}+x\right)=\mathrm{tan}{\pi\over4}}$$


Concept/skill: 
Understand that by similarity, side ratios in right triangles are properties of the angles in the triangle, leading to definitions of trigonometric ratios for acute angles.

Answer: 
No. This problem does not directly teach how side ratios in right triangles lead to definitions of trigonometric ratios. Instead, it involves solving a trigonometric function.

Now, does the problem or activity shown below enable students to learn the full intent of the following concept or skill?

shot_info: fl_problem_001894_neg_0

HS_0
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.
{"table": [{"caption": [{"_value": "Body and brain weight by animal. Source:", "a": [{"_attributes": {"class": ["newWindow"], "href": "http://mste.illinois.edu/malcz/DATA/BIOLOGY/Animals.html"}, "_value": "http://mste.illinois.edu/malcz/DATA/BIOLOGY/Animals.html"}]}], "tbody": [{"tr": [{"th": [{"_value": "Animal"}, {"_value": "Body Weight (kg)"}, {"_value": "Brain Weight (g)"}]}, {"td": [{"_value": "Mountain beaver"}, {"_value": "1.35"}, {"_value": "8.1"}]}, {"td": [{"_value": "Cow"}, {"_value": "465"}, {"_value": "423"}]}, {"td": [{"_value": "Grey wolf"}, {"_value": "36.33"}, {"_value": "119.5"}]}, {"td": [{"_value": "Goat"}, {"_value": "27.66"}, {"_value": "115"}]}, {"td": [{"_value": "Guinea pig"}, {"_value": "1.04"}, {"_value": "5.5"}]}, {"td": [{"_value": "Asian elephant"}, {"_value": "2547"}, {"_value": "4603"}]}, {"td": [{"_value": "Donkey"}, {"_value": "187.1"}, {"_value": "419"}]}, {"td": [{"_value": "Horse"}, {"_value": "521"}, {"_value": "655"}]}, {"td": [{"_value": "Potar monkey"}, {"_value": "10"}, {"_value": "115"}]}, {"td": [{"_value": "Cat"}, {"_value": "3.3"}, {"_value": "25.6"}]}, {"td": [{"_value": "Giraffe"}, {"_value": "529"}, {"_value": "680"}]}, {"td": [{"_value": "Gorilla"}, {"_value": "207"}, {"_value": "406"}]}, {"td": [{"_value": "Human"}, {"_value": "62"}, {"_value": "1320"}]}, {"td": [{"_value": "African elephant"}, {"_value": "6654"}, {"_value": "5712"}]}, {"td": [{"_value": "Rhesus monkey"}, {"_value": "6.8"}, {"_value": "179"}]}, {"td": [{"_value": "Kangaroo"}, {"_value": "35"}, {"_value": "56"}]}, {"td": [{"_value": "Golden hamster"}, {"_value": "0.12"}, {"_value": "1"}]}, {"td": [{"_value": "Mouse"}, {"_value": "0.023"}, {"_value": "0.4"}]}, {"td": [{"_value": "Rabbit"}, {"_value": "2.5"}, {"_value": "12.1"}]}, {"td": [{"_value": "Sheep"}, {"_value": "55.5"}, {"_value": "175"}]}, {"td": [{"_value": "Jaguar"}, {"_value": "100"}, {"_value": "157"}]}, {"td": [{"_value": "Chimpanzee"}, {"_value": "52.16"}, {"_value": "440"}]}, {"td": [{"_value": "Mole"}, {"_value": "0.122"}, {"_value": "3"}]}, {"td": [{"_value": "Pig"}, {"_value": "192"}, {"_value": "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