QMB Module 7

Which of the following variables is categorical? Gender Height Weight Age

gender

The term used to describe the case when the independent variables in a multiple regression model are correlated is: causation. explanatory correlation. multicollinearity. regression.

multicollinearity

The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is called the: slope of the least squares regression line. error term. multiple coefficient of determination. correlation.

multiple coefficient of determination

Since the multiple regression equation generates a plane or surface, its graph is called a: response surface. response plane. dependent variable graph. dependent variable plane.

response service

The term in the multiple regression model that accounts for the variability in y that cannot be explained by the linear effect of the p independent variables is the: response variable, . leading coefficient, . correlation coefficient, r. error term, .

error term

In a multiple regression model, the variance of the error term, ε, is assumed to be: larger as the values of x increase. 1. the same for all values of x1, x2,..., xp. 0.

the same values

In multiple regression analysis, any observation with a standardized residual of less than _____ or greater than _____ is known as an outlier. -1; 1 -2; 2 -4; 4 -3; 3

-2;2

In a multiple regression model, the error term ε is assumed to have a mean of: μ 0. 1. -1.

0

When we use the estimated regression equation to develop an interval that can be used to predict the mean for ALL units that meet a particular set of given criteria, that interval is called a(n): population interval. prediction interval. estimation interval. confidence interval.

confidence interval

A variable used to model the effect of categorical independent variables is called a(n): quantitative variable. categorical variable. dummy variable. explanatory variable.

dummy variable

In general, R2 always _____ as independent variables are added to the regression model. increases or decreases depending on how the variables relate to the response variable. increases stays the same decreases

increases

In a multiple regression model, the values of the error term, ε, are assumed to be: always negative. dependent on each other. independent of each other. zero.

independent of each other

When we conduct significance tests for a multiple regression relationship, the t test can be conducted for each of the independent variables in the model. Each of those tests are called tests for: pairwise significance. individual significance. overall significance. complete significance.

individual significance

If a categorical variable has k levels, then: n dummy variables are needed. k - 1 dummy variables are needed. k + 1 dummy variables are needed. k dummy variables are needed.

k-1 dummy variables are needed

The study of how a dependent variable y is related to two or more independent variables is called: factorial design analysis. linear regression analysis. multiple regression analysis. least significant difference analysis.

multiple regression analysis.

In a multiple regression model, the values of the error term, ε, are assumed to be: skewed to the left. normally distributed. skewed to the right. uniformly distributed.

normally distributed

When we conduct significance tests for a multiple regression relationship, the F test will be used as the test for: pairwise significance. individual significance. overall significance. complete significance.

overall significance

All things held constant, which interval will be wider: a confidence interval or a prediction interval? It cannot be determined from the information given. prediction interval The confidence interval and the prediction interval will have the same width. confidence interval

prediction interval

When we use the estimated regression equation to develop an interval that can be used to predict the mean for a specific unit that meets a particular set of given criteria, that interval is called a(n): population interval. prediction interval. estimation interval. confidence interval.

prediction interval

In multiple regression analysis: there can be several independent variables, but only one dependent variable. there must be only one independent variable. the coefficient of determination must be larger than 1. there can be any number of dependent variables, but only one independent variable.

there can be several independent variables, but only one dependent variable.

Dummy variables must always have: positive values. a value of 1. values of either 0 or 1. a value of 0.

values either 0 or 1