# Lesson 8: Analyzing Bivariate Data

Let’s analyze data like a pro.

## 8.1: Speed vs. Step Length

A researcher found an association between a dog’s stride length and its speed: the longer a dog’s steps, the faster it goes. The predicted speed in meters per second, $s$, as a function of step length in meters, $l$, is

$$s = 4l-1.6$$

What does the rate of change of the function tell you about the association between stride length and speed?

## 8.2: Animal Brains

Is there an association between the weight of an animal’s body and the weight of the animal’s brain?

What do you notice in the table of data?

animal body weight (kg) brain weight (g)
cow 465 423
grey wolf 36 120
goat 28 115
donkey 187 419
horse 521 655
potar monkey 10 115
cat 3 26
giraffe 529 680
gorilla 207 406
human 62 1320
rhesus monkey 7 179
kangaroo 35 56
sheep 56 175
jaguar 100 157
chimpanzee 52 440
pig 192 180

Consider the scatter plot of the data. Are there any outliers?

Experiment with the line to fit the data. Drag the points to move the line. You can close the expressions list by clicking on the double arrow.

1. Without including any outliers, does there appear to be an association between body weight and brain weight? Describe the association in a sentence.
2. Adjust the line by moving the green points, fitting the line to your scatter plot, and estimate its slope. What does this slope mean in the context of brain and body weight?

## 8.3: Equal Body Dimensions

Earlier in this unit, your class gathered data on height and arm span.

1. Sometimes a person’s arm span is equal to their height. Is this true for anyone in the class?

2. Build a scatter plot of arm span versus height, and describe the association. Click on the plus sign  to get a menu and add a table, if you choose.
3. Is the line $y = x$ a good fit for the data? If so, explain why. If not, find the equation of a better line.
4. Examine the scatter plot. Which person in your class has the largest ratio between their arm span and their height? Explain or show your reasoning.

## Summary

People often collect data in two variables to investigate possible associations between two numerical variables and use the connections that they find to predict more values of the variables. Data analysis usually follows these steps:

1. Collect data.
2. Organize and represent the data, and look for an association.
3. Identify any outliers and try to explain why these data points are exceptions to the trend that describes the association.
4. Find a function that fits the data well.

Although computational systems can help with data analysis by graphing the data, finding a function that might fit the data, and using that function to make predictions, it is important to understand the process and think about what is happening. A computational system may find a function that does not make sense or use a line when the situation suggests that a different model would be more appropriate.