COMM 104 Lecture Notes - Lecture 11: Simple Linear Regression, Mean Squared Error, Point Estimation
Document Summary
Chapter 11: correlation coefficient and simple linear regression analysis. Generally when two variables (x and y) move in the same direction (both increase or both decrease) the covariance is large and positive. It follows that generally when two variables move in the opposite directions (one increases while the other decreases) the covariance is a large negative number. When there is no particular pattern the covariance is a small number. What is large and what is small: it is sometimes difficult to determine without a further statistic which we call the correlation coefficient. The correlation coefficient gives a value between -1 and +1: -1 indicates a perfect negative correlation, -0. 5 indicates a moderate negative relationship, +1 indicates a perfect positive correlation, +0. 5 indicates a moderate positive relationship, 0 indicates no correlation. The sample correlation coefficient r= sxy sx s y. This is a point predictor of the population correlation coefficient (pronounced rho )
