学习大二下商统夏南新-statistics for business11for ln2012ch.16regression analysis model building

1、中山大学岭南学院教授 夏南新博士 制作Chapter 16 REGRESSION ANALYSIS: MODEI BUILDING16.1 General Linear Model Modeling Curvilinear RelationshipsThe model is called a second-order model with one predictor variable.In multiple regression analysis the word linear in the term “general linear model” refers only to the fact

2、 that , , , all have exponents of 1; it does not imply that the relationship between and the is linear. Interaction In this second-order model, the variable is added to account for the potential effects of the two variables acting together. This type of effect is called interaction. Nonlinear Models

3、 That Are Intrinsically LinearModels in which the parameters have exponents other than 1 are called nonlinear models.The Exponential ModelThis model is appropriate when the depende-nt variable increases or decreases by a con-stant percentage, instead of by a fixed amou-nt, as increases. We can trans

4、form this nonlinear model to a linear model by taking the logarithm of both sides of equation .16.6 Autocorrelation and the Dubin-Watson TestThe value of at time , denoted by , is related to the value of at previous time per-iods, which is called serial correlation or autocorrelation. Suppose the va

5、lues of are independent but are related the following manner:where is a parameter with an absolute val-ue less than one and is a normally and ind-ependently distributed random variable with a mean of zero and a variance of .DURBIN-WATSON TEST STATISTICIf successive values of the residuals are close together (positive autocorrelation), the value of the Durbin-Watson test statistic will be small. If successive values of the residuals are far apart (negative a