BUSS1020 Lecture 13: BUSS1020 Lecture 13

• School: University of Sydney
• Department: Business Studies
• Course: BUSS1020
• Professor: Parinaz Ezzati

Document Summary

This chapter discusses multiple regression models that use two or more numerical independent variables, x, to predict value of a numerical dependent variable y. 3d scatter plots are often used as a starting point for analysis of multiple regression models. Interpreting the regression coefficients: the simple linear regression model can be extended by assuming a linear relationship between each independent variable and the dependent variable. E. g. with k independent variables, the multiple regression model is expressed as: like in simple regression, you use least-squares method to compute sample regression coefficients b0, b1, and b2 as estimates of the population parameters b0, Predicting dependent variable y: you just enter the x values into the equation, you should only predict within the range of the values of all the independent variables r2, adjusted r2, and the overall f test: Nb: in general the f stat can be defined as the quadratic function of a set of t stats.