1. Happy Tuesday Scientists! Today: Return scored work Grades Data and Graphs Study Guide Collecting Discrete Data Activity HW: Graph your data a second time using different scales for the DV

2. Graphing Study Guide Are you ready to go?

3. There are 4 major types of graphs we’ll use this year. I can remember that

4. Types of Data 1.Discrete data = consist of categories (days of the week, kind of animal, color) 2.Continuous data = consist of measurements involving a scale with equal measurements; a range of values (temperature, length of time in hours, height in centimeters)

5. Four major types of graphing 1.Pie chart/graph = consist of categories (display percentages) 2.Bar graph = used to represent discrete data; the IV is typically qualitative and DV is typically quantitative

6. Four major types of graphing 3. Line graph = used to represent continuous data, usually tracking changes over time; the IV and DV are both quantitative and the line connecting the points has meaning 4. Scatter plot = similar to a line graph, but the data points are NOT connected because a connecting line would have no meaning; a line of best fit might be constructed to show a trend in the data; IV and DV are typically both quantitative

7. Scatter plot This is a line of best fit – it is a straight line that comes as close to possible as all the data points The data points should NOT be directly connected because the IV is discrete

8. Bar Graph This is what we mean by discrete data – it’s a category and there’s no “middle value” between each category

9. Line Graph Each value here has meaning (I could determine how Jill’s elevation at 3.2 hours) so I connect my data points with a line

10. Pie chart Here I have data organized in a percentage out of 100 – this is good way to represent it

11. A few final thoughts Scientists must represent their data graphically in a fair way that tells the most accurate story It is possible to change the scale of a graph to make it look like the data tells a much different story than the numbers actually show Additional labels might also be used to make the reader infer certain things from the same set of data

12. For example… At most, how different is the average rainfall from this year’s rainfall? This graph has been manipulated to make it look like there is a much bigger difference in rainfall than 0.4 cm Bad scientist!

13. A more accurate graph Good scientist!

14. Another Example Quarter, Year Image source: http://www.politicalmathblog.com/?p=401http://www.politicalmathblog.com/?p=401 N umber of jobs (thousands) U.S. Jobs by Quarter What conclusions can you draw from this graph?

15. Here’s the same graph with a few extra labels… Image source: http://www.politicalmathblog.com/?p=401http://www.politicalmathblog.com/?p=401 The data is the same but it could be used to create different inferences

16. Collecting and Graphing Discrete Data (title) A common example of discrete data is information collected when people complete surveys. Come up with a survey question you could ask of every student in this classroom (record on your paper under the title) – Example: Mrs. Ellis will be asking each student. “What is the name of your 7 th grade science teacher.

17. Identify the IV and DV (Record) IV = DV = Create a data table (on your paper) Begin collecting data

18. Graph your data Since this is discrete data, what types of graphs would be appropriate to use?