1. Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the other.) 2. No cause and effect like true experiment. 3. One variable (X) is associated with changes in another variable (Y).

2. Correlation Correlation coefficient details 1. strength 2. direction 3. relationship between the two variables.

3. Correlation

4. Magnitude of r indicates the strength positive or negative. R is a linear relationship. Curvilinear Shapes.

5. .00 to.25 ( + - ) little or no relationship.25 to.50 fair degree of relationship.50 to.75 moderate to fair relationship.75 to 1.00 excellent relationship

6. 1. Correlation matrix intercorrelations 2. Significance of correlation coefficients 3. Null hypothesis 4. Significance

7. Correlations matrix intercorrelations

8. 1. Significance of correlation coefficients 2. Null hypothesis 3. There is a significant level but be careful greater sample size gives a greater chance of achieving significance.

11. Regression When a researcher wants to establish the relationship as a basis for prediction regression analysis is used.

12. Regression X Y must be correlated first X - independent or predictor variable Y - dependent or criterion variable Linear Regression line - best describes orientation of all data points in the scatter plot

13. Regression Y = a + bX Y - intercept when X = 0, a = regression constant b = slope of line

14. Regression

17. Coefficient of the Determination r 2 The square of the correlation coefficient is the indicative of the total variance in Y score that can be predicted from X score. r =.87r 2 =.76 that means 76% of the variance in SBP can be accounted for by knowing the variance in age.

18. Coefficient of the Determination r 2 r2 = coefficient of determination explained variance 1 - r 2 = coefficient of non determinant unexplained variance Standard errors of the estimate