STAT-S 300 Lecture Notes - Lecture 6: Dependent And Independent Variables, Total Variation, Minitab
Document Summary
Regression line- line that describes how a response variable y changes as an explanatory variable x changes: often used to predict value of y for given value x, regression, unlike correlation, requires having explanatory and response variables. Interpreting a regression line: regression line is model for data, like density curves, suppose y is response variable on y-axis and x is explanatory variable on x-axis. Regression line relating y to x has equation of form = a + bx. Y hat - predicted value of response variable y for given value of explanatory variable x. Prediction: extrapolation- use of lsrl for prediction of y-value outside interval of values of explanatory variable x used to obtain line. Do not make predictions using x-values far outside range appearing in. Residuals and the least-squares regression line data: residual- difference between observed value of response variable and value predicted by regression line residual = observed y predicted y = y .
