1. Chapter 1
A Physics Toolkit
1.3 Graphing Data

2. 1.3 Section Original Speed (m/s) Braking Distance (m) 11 18 16 32 2049 25 68 29 92

3. Domestic Car Sales 1.3 Section 1940's 1950's 1960's 1970's 1980's
1940's
1950's
1960's
1970's
1980's
1990's
2000's
Car Sales in Thousands (Domestic)
4,800
7,200
11,120
8,987
11,653
11,985
12,087

4. Section
1.3

5. Section
1.3

6. Section
1.3

7. Graphing Data 1.3 In this section you will:
Graph the relationship between independent and dependent variables.
Interpret graphs.
Recognize common relationships in graphs.

8. Graphing Data 1.3 Identifying Variables
Section
Graphing Data
1.3
Identifying Variables
A variable is any factor that might affect the behavior of an experimental setup.
It is the key ingredient when it comes to plotting data on a graph.
The independent variable is the factor that is changed or manipulated during the experiment.
The dependent variable is the factor that depends on the independent variable.
Question: What is the independent variable, x or y?
Answer: x

9. Section
Graphing Data
1.3
This is a scatter plot graph.
What is the independent variable? Dependent variable?

10. Graphing Data 1.3 Linear Relationships
Section
Graphing Data
1.3
Interpolation is a method of constructing new data points within the range of a set of known data points.
“reading between the points”
Extrapolation is the process of constructing new data points outside a set of known data points.
“reading beyond the points”
Linear Relationships
Scatter plots of data may take many different shapes, suggesting different relationships.

11. Graphing Data 1.3 Linear Relationships
Section
Graphing Data
1.3
Linear Relationships
When the line of best fit is a straight line, as in the figure, the dependent variable varies linearly with the independent variable. This relationship between the two variables is called a linear relationship.
The relationship can be written as an equation:

12. Graphing Data 1.3 Linear Relationships
Section
Graphing Data
1.3
Linear Relationships
The slope is the ratio of the vertical change to the horizontal change. To find the slope, select two points, A and B, far apart on the line. The vertical change, or rise, Δy, is the difference between the vertical values of A and B. The horizontal change, or run, Δx, is the difference between the horizontal values of A and B.

13. Graphing Data 1.3 Linear Relationships
Section
Graphing Data
1.3
Linear Relationships
As presented in the previous slide, the slope of a line is equal to the rise divided by the run, which also can be expressed as the change in y divided by the change in x.
If y gets smaller as x gets larger, then Δy/Δx is negative, and the line slopes downward.
The y-intercept, b, is the point at which the line crosses the y-axis, and it is the y-value when the value of x is zero.

14. Graphing Data 1.3 Nonlinear Relationships
Section
Graphing Data
1.3
Nonlinear Relationships
When the graph is not a straight line, it means that the relationship between the dependent variable and the independent variable is not linear.
There are many types of nonlinear relationships in science. Two of the most common are the quadratic and inverse relationships.
Check this vocabulary term.

15. Graphing Data 1.3 Nonlinear Relationships
Section
Graphing Data
1.3
Nonlinear Relationships
The graph shown in the figure is a quadratic relationship.
A quadratic relationship exists when one variable depends on the square of another.
A quadratic relationship can be represented by the following equation:

16. Graphing Data 1.3 Nonlinear Relationships
Section
Graphing Data
1.3
Nonlinear Relationships
The graph in the figure shows how the current in an electric circuit varies as the resistance is increased. This is an example of an inverse relationship.
In an inverse relationship, a hyperbola results when one variable depends on the inverse of the other.
An inverse relationship can be represented by the following equation:

17. Graphing Data 1.3 Nonlinear Relationships Predicting Values
Section
Graphing Data
1.3
Nonlinear Relationships
There are various mathematical models available apart from the three relationships you have learned. Examples include: sinusoids—used to model cyclical phenomena; exponential growth and decay—used to study radioactivity
Combinations of different mathematical models represent even more complex phenomena.
Predicting Values
Relations, either learned as formulas or developed from graphs, can be used to predict values you have not measured directly.
Physicists use models to accurately predict how systems will behave: what circumstances might lead to a solar flare, how changes to a circuit will change the performance of a device, or how electromagnetic fields will affect a medical instrument.

18. Section Check 1.3 Question 1
Which type of relationship is shown following graph?
Linear
Inverse
Parabolic
Quadratic
Answer: B
Reason: In an inverse relationship a hyperbola results when one variable depends on the inverse of the other.

19. Section Check 1.3 Question 2 What is line of best fit?
The line joining the first and last data points in a graph.
The line joining the two center-most data points in a graph.
The line drawn close to all data points as possible.
The line joining the maximum data points in a graph.
Answer: C
Reason: The line drawn closer to all data points as possible, is called a line of best fit. The line of best fit is a better model for predictions than any one or two points that help to determine the line.

20. Section Check 1.3 Question 3
Which relationship can be written as y = mx?
Linear relationship
Quadratic relationship
Parabolic relationship
Inverse relationship
Answer: A
Reason: Linear relationship is written as y = mx + b, where b is the y intercept. If y-intercept is zero, the above equation can be rewritten as y = mx.

21. Section Check 1.3 Question 4 What is this relationship?
Linear relationship
Quadratic relationship
Parabolic relationship
Inverse relationship
Answer: B

22. Section Check 1.3 Question 5
What relationship has the equation y = a/x?
Linear relationship
Quadratic relationship
Parabolic relationship
Inverse relationship
Answer: D

23. Section Check 1.3 Question 6
What is the difference between interpolation and extrapolation?
Answer: Interpolation is reading between the data points. Extrapolation is reading beyond the data points.

24. Imagine an experiment you might conduct.
Section
Exit Ticket
1.3
Imagine an experiment you might conduct.
What would be your independent and dependent variables?
Write five lines describing your experiment. Be sure to identify the independent and dependent variables.
Sketch the graph of your experiment showing the independent and dependent variables.

25. Chapter Summary 1.1 Chapter 1 Test The test is worth 50 points.
Section
Chapter Summary
1.1
Chapter 1 Test
The test is worth 50 points.
- Multiple Choice (13 worth 1 pt. each)
- Matching (25 worth 1 pt. each)
- Problems ( 2 worth 6 pts. Each)