1. Graphs And Graphing Thursday, June 02, 2016

2. Graphs Bar - comparing trials/variables Line - change over time Circle (pie) - parts of a whole: %

3. Remember --- DRY MIX Dependent Responding variable Y-Axis UP Manipulated Independent variable X-Axis

4. Bar & Line Graphs X-axis = independent variable (tested variable) Y-axis = dependent variable (what is being measured) Include descriptive titles Include key when applicable Label (title) each axis including units

5. Bar & Line Graphs X-axis = INdependent variable = INput Y-axis = dependent variable = depends on the independent

6. DRY MIX Dependent Responding Variable Y-Axis Manipulated Independent Variable X-Axis

7. Bar Graph Example Descriptive Title Key (legend) Y-axis label with correct unit abbreviation X-axis label Y-axis scale fills page X-axis scale clear and easy to read

8. Bar Graphs Used to compare different items or trials Used to track one item over time What can you say about stamp prices based on this graph?

9. Line Graphs Used to show how two or more variables are related Used to show change over time What is the difference in pulse rate recovery between athletes and non-athletes? Used to show how two or more variables are related Used to show change over time What is the difference in pulse rate recovery between athletes and non-athletes? Pulse Recovery Rate of Athletes and Non-athletes

10. Circle or Pie Graphs Used to show percentage of a whole Always totals 100% See the big picture quickly Where does most of a consumer’s money get spent?

11. Tables Used to organize information List information in columns or rows Show relationships between two or more variables Pay close attention to the headings and the units What happens as water temperature rises? Amount of Oxygen that can be Dissolved in Water Water Temperature (°C) Oxygen Content (ppm) 1011.29 1510.10 209.11 258.27 307.56

12. Graphing Graphs are a useful tool in science. The visual characteristics of a graph make trends in data easy to see. One of the most valuable uses for graphs is to "predict" data that is not measured on the graph.

13. Graphing Steps Identify the Variables Determine the range Determine the scale Number and label each axis Plot the points Draw the graph Give your graph a title

14. Identify the Variables –Independent Variable - (the thing you changed)  Goes on the X axis (horizontal)  Should be on the left side of a data table. –Dependent Variable - (changes with the independent variable)  Goes on the Y axis (vertical)  Should be on the right side of a data table.

15. Range –Subtract the lowest data value from the highest data value. –Do each variable separately.

16. Scale –Determine a scale, (the numerical value for each square), that best fits the range of each variable. –Spread the graph to use MOST of the available space.

17. Label Axis's You need to tell everyone reading your graph what the graph means. Be sure to include units.

18. Plotting Plot each data value on the graph with a dot. You can put the data number by the dot, if it does not clutter your graph.

19. Drawing –Draw a curve or a line that best fits the data points. –Most graphs of experimental data are not drawn as "connect-the- dots".

20. Title Your title should clearly tell what the graph is about. If your graph has more than one set of data, provide a "key" to identify the different lines Sunspots Over Time

21. X-AxisY-Axis 11 22 33 44 55 66 77 88 99 Sample Data Table

22. Sample Graph

23. Interpolation Interpolate: predicting data between two measured points on the graph.

24. Extrapolation Extrapolate: extending the graph, along the same slope, above or below measured data.