Chapter 16: Regression Analysis: Model Building
Quiz
A regression model in the form of y = b0 + b1x1 + e is referred to as a
- simple first-order model with two predictor variables
- simple second-order model with one predictor variable
- simple second-order model with two predictor variables
- simple first-order model with one predictor variables
- none of the above
A regression model in the form of y = b0 + b1x1 + b2X2+ e is referred to as a
- second-order model with three predictor variables
- second-order model with two predictor variables
- second-order model with one predictor variable
- first-order model with one predictor variables
- none of the above
Serial correlation is the
- correlation between serial numbers of products
- same as autocorrelation
- same as leverage
- none of the above
The joint effect of two variables acting together is called
- autocorrelation
- interaction
- serial correlation
- none of the above
A test to determine whether or not first order autocorrelation is present is
- a t test
- an F test
- a test of interaction
- none of the above
Which of the following tests is used to determine whether additional variables make a significant contribution to a multiple regression model?
- a t test
- a z test
- an F test
- none of the above
In multiple regression analysis, the general linear model
- can not be used to accommodate curvilinear relationships between dependent variables and independent variables
- can be used to accommodate curvilinear relationships between independent variables and dependent variables
- must contain more than two independent variables
- none of the above
The range of the Durbin-Watson statistic is between
- a. -1 and 1
- 0 and 1
- - infinity and + infinity
- 0 and 4
- none of the above
The multiple regression approach to the analysis of variance uses dummy variables.
- True
- False
The correlation in error terms that arises when the error terms at successive points in time are related is termed
- leverage
- multicorrelation
- autocorrelation
- parallel correlation
- none of the above