2-2 making predictions indicators d1- read, create, and interpret graphs d2- analyze how decisions...
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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.