2000 words economic assignment due 13 hours
fanbing910724
EQAWeek15.pptx8220 Economics and Quantitative Analysis
Week 15
Learning Outcome 4:
Analyze business and economic data and interpret quantitative analysis to inform business decisions using quantitative analytic techniques.
Lecturer: Dr. Dayal Talukder
ICL Business School, Auckland
Key elements:
Introduction to multiple linear regression
Multiple regression
LO 4 :
Analyze business and economic data and interpret quantitative analysis to inform business decisions using quantitative analytic techniques.
3
Multiple regression analysis
Objectives
On completion of this topic students should be able to:
apply and interpret multiple regression
interpret the output that relates to multiple linear regression.
Simple regression is bivariate linear regression in which one dependent variable, y, is predicted by one independent variable, x.
There are situations in which a dependent variable may be predicted by using more than one independent variable: CEOs compensations may be predicted by using variables such as company earnings per share, age of CEO, size of company, and company sales.
Regression analysis with two or more independent variables is called multiple regression analysis.
Multiple regression is similar in principle to simple regression. However it is more complex conceptually and computationally.
Simple Versus Multiple Regression
Probabilistic Multiple Regression Model Y = β0 + β1X1 + β2X2 + β3X3 + . . . + βkXk+ ε
where Y = the value of the dependent (response) variable
β0 = the regression constant
β1 = the partial regression coefficient of independent variable 1
β2 = the partial regression coefficient of independent variable 2
βk = the partial regression coefficient of independent variable k
k = the number of independent variables
ε = the error of prediction
Multiple Regression Models
Probabilistic Multiple Regression Model Y = β0 + β1X1 + β2X2 + ε
where Y = the value of the dependent (response) variable
β0 = the regression constant
β1 = the partial regression coefficient of independent variable 1
β2 = the partial regression coefficient of independent variable 2
ε = the error of prediction
Multiple Regression Models (our interest)
The Estimated Multiple Regression Model
LO1
X
