2-2 Making Predictions

Indicators D1- Read, create, and interpret graphs

D2- Analyze how decisions about graphing affect the graphical representation

Pages 60-63

Line graphs can be used to make predictions. Find the trend in the data and follow it out to a specific time.

A trend can be linear (make a straight line) or non-linear (make a curved line)

A linear trend shows constant change.

A non-linear trend shows irregular or varying change.

Words Typed

0

100

200

300

400

500

600

700

1 2 4 6

Time (min)

Wor

ds T

yped

Use a Line Graph to PredictTime (min)

Words typed

0 0

1 40

2 85

3 128

4 169

5 214

6 258

Enrique is writing a 600-word paper for class. The table shows the time it has taken Enrique to type the paper so far. Make a line graph and predict the total time it will take him to type his paper.

{The data values go from 0-

700. You want to predict

the amount

of time it will take him to

type 600 words.

Graph the data and connect the

points.

Continue the graph with a dotted line in the same

direction until you reach the horizontal position of

600 words.

Scatter Plots sometimes called scattergrams

• A scatter plot displays two sets of data on the same graph.

• Like line graphs, scatter plots are useful in making predictions because they show trends in data.

• If the points in a scatter plot come close to making a straight line, the 2 sets of data are related.

Use a Scatter plot to Predict

The scatter plot shows the number of days that San Bernadino, California, failed to meet air quality standards from 1990 to 1998. Use it to predict the number of days of bad air quality in 2004.

Bad Air Quality Days

0

50

100

150

200

1985 1990 1995 2000 2005

Year

Nu

mb

er o

f D

ays

w/

AQ

I va

lues

> 1

00The vertical

axis represents the # of Days

SB, Cal. Failed to meet air

quality standards

The horizontal axis represents the

year

The line goes through the

middle of the data.

By looking at the pattern in the scatter plot, we can predict that the number of days of bad air quality in 2004 was around 48 days.