MGCR 271 Lecture Notes - Lecture 14: Single-Stage-To-Orbit, Confidence Interval, Multicollinearity

• School: McGill University
• Department: Management Core
• Course: MGCR 271
• Professor: Glenn Zabowski

Document Summary

Y = 0 + 1x1 + 2x2 + 3x3 + + kxk + . Multiple regression is regression analysis with more than one independent variable. Estimated regression line: y = b0 + b1x1 + b2x2 + b3x3 + + bkxk. K = the true regression parameters. bk = the estimated regression coefficients. = the error term. bk = the change in y (the dependent variable) per unit increase in xk with all other independent variables held fixed. You wish to construct a regression model which will explain a higher percentage of the variation in sales price. The percent of variation in the dependent variable that is explained by the set of independent variables. The strength of the linear association between the set of independent variables and the dependent variable. R is the square root of r2. (regression line is not significant). (regression line is significant). Commonly used for testing the overall significance of a regression line.