1. Displays of variables Measurement Variables (interval, ratio) Frequency distributions Histograms Stem and leaf displays Scatterplots

2. Stem and Leaf Display Arrangement of data, similar to a histogram, but replacing bars with numbers from the data Stem most significant digit or set of digits for the display Leaf next significant digit

3. Most Significant Digits Digits furthest to the left in the number 123 counting by 10s 123 counting by 100s 4715 counting by 100s 4715 counting by 1000s 3.14 counting by 1s

4. Next Significant Digit Digits furthest to the right in the number 123 counting by 10s 123 counting by 100s 4715 counting by 100s 4715 counting by 1000s 3.14 counting by 1s

5. Arrange the data Consider only one variable One entry for each observation (number) in the data set for that variable Enter most significant digit(s) on left, if not already entered Enter next digit on the right

6. Example Digits: 10 7 0 13 3 2 3 3 8 6 2 1 1 2 1 11 17 10 2 25 13 2 7 0 4 11 5 4 1 1 12 3 2 5 19 11 3 8 17 8 2 8 15 3 1 8 4 4 1 3 2 4 6 6 11 10 0 19 7 9 Begin: 0:7032338 1:03 2:

7. Finish 0:000111111122222222333333344444 55666777888889 1:000111123357799 2:5 Clearly to get this nice ordering it is better to have the original data

8. Order data first If you can!! 0 0 0 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 5 5 6 6 6 7 7 7 8 8 8 8 8 9 10 10 10 11 11 11 11 12 13 13 15 17 17 19 19 25 If not, rearrange after you make the initial stem and leaf plot

9. Try it Data: 65 77 0 30 6 87 30 89 48 95 85 23 81 66 65 23 35 10 37 46 99 82 49 12 58 46 73 73 53 2 74 48 87 31 84 58 99 11 98 91 15 47 13 98 34 43 33 12 52 59 34 49 6 47 53 82 27 76 1 89

10. Answer 0:0 1 2 6 6 1:0 1 2 2 3 5 2:3 3 7 3:0 0 1 3 4 4 5 7 4:3 6 6 7 7 8 8 9 9 5:2 3 3 8 8 9 6:5 5 6 7:3 3 4 6 7 8:1 2 2 4 5 7 7 9 9 9:1 5 8 8 9 9

11. Scatterplots Display two variables Independent variable - X Does not depend on any other variable in the model Dependent variable - Y Depends upon one or more independent variables in the model

12. Model X Y IndependentDependent Causes

13. Example Cities X - % unemployment Y - crime rate Persons X - amount of prejudice Y - amount of discrimination