1. Warm-Up List all of the different types of graphs you can remember from previous years:

2. Make your best paper airplane out of the printer paper provided Go out in the hall and fly it to your best ability and record how many feet/squares it flies before coming to rest Do this 3 times Come back in and record your answer here: Also put your dot where it needs to go…(dotplot) Paper Airplanes

3. Section 1.1 Data Analysis: Categorical and Quantitative Data

4. Variables… An Individual is an object described by data from a data set. (Can be people, animals, or objects). A Variable is any characteristic of an individual. A variable can take different values for different individuals. A Categorical Variable places an individual into one of several groups or categories. A Quantitative Variable takes numerical values for which it makes sense to find an average.

5. Ways to display categorical data Bar Graphs Pie Charts Segmented Bar Graph

6. To Display Variables The Distribution of a variable tells us what values the variable takes and how often it takes these values. A Frequency Table (used for categorical variables) lists individuals by a variable and how many individuals are within the categories of that variable. A Relative Frequency Table is a frequency table that divides each count by the total, making each value a percentage of the whole. ABCDF 10192173 ABCDF 10/6019/6021/607/603/60

7. Roundoff Error Roundoff Error is when percents or vales are rounded within a relative frequency or frequency table (respectively) which causes the total to not match what is expected. Ex: Student AP Scores In Millions Total: 10,531,277 students 12345 1.72.13.82.3.9

8. Marginal Distribution A Marginal Distribution is a distribution of one categorical variable in a two-way table. As in, how the variable is distributed among the total of the table. It is called a marginal distribution because it will literally use the margin of the two-way table. MalesFemales Left Handed13 Right Handed1514

9. Conditional Distribution A Conditional Distribution of a variable describes the values of the variable among individuals who have a specific value of another variable. There is a separate conditional distribution for each value of the other variable. MalesFemales Left Handed13 Right Handed1514

10. Example of Conditional Dist. Let’s do the conditional distribution for males… Now females… Young Adults by Gender and Chance of Getting Rich Gender OpinionFemaleMaleTotal Almost No Chance9698194 Some Chance but probably not 426286712 A 50-50 Chance6967201416 A Good Chance6637581421 Almost Certain4865971083 Total236724594826

11. Your Answer… To compare these, we would use a side-by-side bar graph ResponseFemaleMale Almost No Chance4.1%4.0% Some Chance but probably not 18.0%11.6% A 50-50 Chance29.4%29.3% A Good Chance28.0%30.8% Almost Certain20.5%24.3%

12. Association We say there is an association between two variables if specific values of one variable tend to occur in common with specific values of the other. For example, if we surveyed all of the fall athletes there would be an association between being male and playing football.

13. Simpson’s Paradox Simpson’s Paradox is when an association between two variables that holds for each individual value of a third variable can be reversed when the data for all values of the third variable are combined. Example: Accident Victims and how they are transported HelicopterRoad Victim Died64260 Victim Survives136840 Total2001100

14. More information Serious Accidents HelicopterRoad Died4860 Survived5240 Total100 Less Serious Accidents HelicopterRoad Died16200 Survived84800 Total1001000

15. Homework Pg. 22 (9-12, 14-15, 17-22, 26)