options: Investigate chance processes and develop, use, and evaluate probability models.

Draw informal comparative inferences about two populations.

Use probability to evaluate outcomes of decisions.

Summarize, represent, and interpret data on two categorical and quantitative variables.

Summarize and describe distributions.

Understand independence and conditional probability and use them to interpret data.

Use the rules of probability to compute probabilities of compound events in a uniform probability model.

Investigate patterns of association in bivariate data.

Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Summarize, represent, and interpret data on a single count or measurement variable.

Interpret linear models.

Use random sampling to draw inferences about a population.

Understand and evaluate random processes underlying statistical experiments.

Calculate expected values and use them to solve problems.

Develop understanding of statistical variability.
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.


correct_option_index: 7