Ch. 6 Correlation
1. Introduction
1. First step in determining cause-effect
2. Evaluation of tests
2. Scatter plot: defined as the graph of paired X and Y values.
a. How to
| STUDENT | STATS GRADE | ANXIETY SCORE |
| 1 | 7 | 3 |
| 2 | 5 | 4 |
| 3 | 2 | 9 |
| 4 | 6 | 4 |
| 5 | 10 | 1 |
b. Linear Relationship vs. Curvilinear Relationship
3. Equation of a straight line:
a. Positive
b. Negative
c. Perfect
d. Imperfect
A. Magnitude, Direction of correlation coefficient
B. Link between scatter plot and correlation coefficient
3.Pearson r
A. Definitions 1: Pearson r measures the extent to which paired scores occupy the same or opposite position within their own distributions
| TEST | MEAN | STANDARD DEVIATION | RAW SCORE |
| Assignment | 15 | 2.5 | 16(80%) |
| Test | 36 | 6 | 48(80%) |
B. Qualities
1. Independent of scale units, number of subjects, differences in variability
4. Calculation of Pearson r
5. Pearson r and explained variability
A. coefficient of determination
A. Spearman Rho
| Chair Rankings (X) | Agency Rankings (Y) |
| 2 | 4 |
| 1 | 3 |
| 3 | 1 |
| 4.5 | 2 |
| 4.5 | 5 |
| 7 | 10 |
| 8 | 7 |
| 6 | 7 |
| 9 | 7 |
| 10 | 9 |
A. Restricted range
7. Correlation and causation
X Y