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.

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.

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?
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: 1

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