1. MARE 250 Dr. Jason Turner Correlation & Linear Regression

2. Means Tests Vs. Associations Means tests – t-test, ANOVA – test for differences between/among means (Responses among/between factors) Associations – tests for relationships between/among variables (responses)

3. Linear Regression Linear regression investigates and models the linear relationship between a response (Y) and predictor(s) (X) Both the response and predictors are continuous variables (“Responses”) Linear regression analysis is used to: - determine how the response variable changes as a particular predictor variable changes - predict the value of the response variable for any value of the predictor variable

4. Regression vs. Correlation Linear regression investigates and models the linear relationship between a response (Y) and predictor(s) (X) Both the response and predictors are continuous variables (“Responses”) Correlation coefficient (Pearson) – measures the extent of a linear relationship between two continuous variables (“Responses”)

5. When Regression vs. Correlation? Linear regression - used to predict relationships, extrapolate data, quantify change in one versus other is weighted direction Correlation coefficient (Pearson) – used to determine whether there is a relationship or not IF Regression – then it matters which variable is the Response (Y) and which is the predictor (X) Y – (Dependent variable) X – (Independent) X causes change in Y (Y outcome dependent upon X) Y Does Not cause change in X (X –Independent)

6. Linear Regression Regression provides a line that "best" fits the data (from response & predictor) The least-squares criterion (method used to draw this "best line“) requires that the best-fitting regression line is the one with the smallest sum of the squared error terms (the distance of the points from the line).

7. Linear Regression The R 2 and adjusted R 2 values represent the proportion of variation in the response data explained by the predictors Adjusted R 2 is a modified R 2 that has been adjusted for the number of terms in the model. If you include unnecessary terms, R 2 can be artificially high

8. y Is This Them? Are These They? y = b 0 + b 1 x y = dependent variable b 0 + b 1 = are constants b 0 = y intercept b 1 = slope x = independent variable Urchin density = b 0 + b 1 (salinity)

9. Effects of Outliers Outliers may be influential observations A data point whose removal causes the regression equation (line) to change considerably Consider removal much like an outlier If no explanation – up to researcher

10. Warning on Regression Regression is based upon assumption that data points are scattered about a straight line What can we do to determine if a Regression is warranted?

11. Correlation Coefficient (r)(Pearson) – measures the extent of a linear relationship between two continuous variables (responses) Pearson correlation of cexa Ant and cexa post = 0.811 P-Value = 0.000 IF p < 0.05 THEN the linear correlation between the two variables is significantly different than 0 IF p > 0.05 THEN you cannot assume a linear relationship between the two variables Correlation Coefficient

13. “R 2 D2 it is you, it is you” Coefficient of Determination ( R 2 ) - Expression of the proportion of the total variability in the response (s) attributable to the dependence of all of the factors R 2 – used for assessing the “goodness of fit” of a regression model Should use Adjusted R 2 as it is a more conservative measure R 2 values range from 0 to 100%. An R 2 of 100% means that all of the variability in the data can be explained by the model

14. Coefficient Relationships The coefficient of determination (r 2 ) is the square of the linear correlation coefficient (r)

15. Next Week Regression Analysis: _ Urchins versus % Rock The regression equation is _ Urchins = - 0.557 + 0.0361 % Rock Predictor Coef SE Coef T P Constant -0.5569 0.3820 -1.46 0.146 % Rock 0.036116 0.0062 5.80 0.000 S = 3.27363 R-Sq = 11.0% R-Sq(adj) = 10.6%