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Predicting Swings

Day 4
by

Summer Roland

on 25 August 2013

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Transcript of Predicting Swings

Day 4 - Predicting Swings
Look at the pictorial graph you created, describe the relationship between the length of the swinger and the number of swings that the graph displays.
Your picture graph is a picture of the swingers on the number line. The same information can be displayed symbolically on a two-coordinate graph.
A

two-coordinate

graph

- shows the outcome of a series of experiments when a variable is changed by steps.
Just like on the picture graph, the horizontal line
(x-axis)
- represents what is known before the experiment starts. This is the independent variable.
In this case we know the length of the pendulum even before we swing it.
The vertical line
(y-axis)
- represents what we find out by doing the experiment. This is the dependent variable.
In this case we find out how many swings each different pendulum makes in 15 seconds.
You now have created three graphs of the swingers.
-> The real swingers hanging from the number line was a concrete graph
-> The picture graph was a drawing of the real objects.
-> The two-coordinate graph used dots to symbolize the real objects.
How are the three graphs different?

How are they the same?

Why is a picture graph useful?




What is the benefit of using a two-coordinate graph?
Picture graphs are useful because they are made on paper, so they are easily stored; they look like the real thing so they don't need precise labeling.
The benefit of using a two-coordinate graph is that not all experimental results can be made into concrete or pictorial graphs.
A

prediction
is an estimate based on information or experience.
Two-coordinate graphs are useful for making predictions.
For instance, can you predict how many swings an 80-cm pendulum will make in 15 seconds?
Read "Swinging through History?"
X
= independent variable
Y
= dependent variable
Length of pendulum v. Number of swings
Titles to graphs are all written the same
IV v. DV
The independent variable versus the dependent variable.
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