1. Correlation and Regression: The Need to Knows Correlation is a statistical technique: tells you if scores on variable X are related to scores on variable y. Does knowing x tell you anything about y? Correlation coefficient r: -1 ………….. 0 …………… +1 +/- 1 = perfect relationship +/-.1-.3 = weak relationship 0 = no relationship +/-.4-.6= moderate relationship +/-.7-.9 = strong relationship (actual # depend on what you’re studying) The closer the data points “fit” a straight line, the stronger the linear correlation between x and y

2. Coefficient of determination r 2 tells you how much of the variability in the the y scores in explained by variable x typically expressed as a % Standard error of estimation Sy.xlike SEM, measures “error” by looking at the average distance points are to the line. The greater the Sy.x, the greater the scatter and the smaller r will be. r = -.19 r 2 = 3.5% Sy.x = 7.77 Is r weak, moderate, or strong? How much of the variability in night fear scores is explained by age? On average, how far is night fear scattered around the best fitting line? df for correlation = # pairs -2

3. The ANOVA is not significant (p=.168) so age is probably a poor predictor of night fear. Equation for the straight line: Y’ = bx + a b = slope = -.181 a = constant = 19.166 Y’ = a predicted value of Y for a specified value of X – use the formula! Y’ = (-.181)(28) + 19.166 Y’ = 14.10 What would the predicted night fear score be for someone who is 28 years old? Regression