# Lesson 4Rocking the ResidualsSolidify Understanding

### 1.

Interpret the data provided in the box plot for the first quiz of a unit of study for a group of math students.

What is the median score?

What is the range?

Did students do well on the quiz?

### 2.

Interpret the data provided in the box plot for the second quiz of a unit of study for a group of math students.

What is the median score?

What is the range?

Did students do well on the quiz?

### 3.

Look back at the box plots for problems 1 and 2. Which quiz did students perform better on the first quiz or the second quiz? Why?

Data is often collected using a survey with several questions. Questions that need to be answered with a number generate numerical data. Questions that produce responses that are not numbers generate categorical data.

Determine whether the questions in the following problems create numerical data or categorical data.

### 4.

What is your shoe size?

numerical data

categorical data

### 5.

What is the color of your eyes?

numerical data

categorical data

### 6.

What type of pet do you have?

numerical data

categorical data

### 7.

How tall are you?

numerical data

categorical data

### 8.

Where is your favorite place to visit?

numerical data

categorical data

### 9.

How many siblings do you have?

numerical data

categorical data

## Set

The data sets in problems 10 and 11 are scatterplots that have the regression line and the residuals marked. For each exercise, use the given data set to create a residual plot, and then assess the fit of the linear function to the data based on the residuals.

Data Set 1

Residual Plot 1

Data Set 2

Residual Plot 2

### 12.

Consider the residual plot below, determine whether the regression line is a good fit to the data or not, and then explain why it is or is not a good fit.

### 13.

Consider the residual plot below, determine whether the regression line is a good fit to the data or not, and then explain why it is or is not a good fit.

Decide if you agree or disagree with the following statements, and explain why.

### 14.

By analyzing the residuals, the quality of fit between a function and the data can be determined.

### 15.

When bivariate data have a strong correlation, that means that one of the data items causes the other item.

## Go

Use technology to compute the correlation coefficient for a linear regression equation for each set of data. Then interpret the correlation coefficient, and describe the nature of the data in terms of the fit to a linear model.

Shoe Size

Height (inches)

### 17.

Absences at school

Scored on Test

### 18.

Number of Visitors to Store

Number of Sales Transactions

### 19.

Games Played

Points Scored per Game