利用Excel进行统计分析-Chapter14-Introduction to Multiple Regression.ppt

Statistics for ManagersUsing Microsoft? Excel 5th Edition Chapter 14 Introduction to Multiple Regression Learning Objectives In this chapter, you learn: How to develop a multiple regression model How to interpret the regression coefficients How to determine which independent variables to include in the regression model How to determine which independent variables are most important in predicting a dependent variable How to use categorical variables in a regression model The Multiple Regression Model Multiple Regression Equation Multiple Regression Equation Multiple Regression Equation2 Variable Example A distributor of frozen dessert pies wants to evaluate factors thought to influence demand Dependent variable: Pie sales (units per week) Independent variables: Price (in $) Advertising ($100’s) Data are collected for 15 weeks Multiple Regression Equation2 Variable Example Sales = b0 + b1 (Price) + b2 (Advertising) Sales = b0 +b1X1 + b2X2 Where X1 = Price X2 = Advertising Multiple Regression Equation2 Variable Example, Excel Multiple Regression Equation2 Variable Example Multiple Regression Equation2 Variable Example Coefficient of Multiple Determination Reports the proportion of total variation in Y explained by all X variables taken together Coefficient of Multiple Determination (Excel) Adjusted r2 r2 never decreases when a new X variable is added to the model This can be a disadvantage when comparing models What is the net effect of adding a new variable? We lose a degree of freedom when a new X variable is added Did the new X variable add enough independent power to offset the loss of one degree of freedom? Adjusted r2 Shows the proportion of variation in Y explained by all X variables adjusted for the number of X variables used (where n = sample size, k = number of independent variables) Penalizes excessive use of unimportant independent variables Smaller than r2 Useful in comparing models Adjusted r2 F-Test for Overall Signific