Exam 15: Understanding Regression Analysis Basics

If we wanted to use a type of regression that first enters the variable that explains the most variance,then the variable that explains the second highest level of variance and so on,we would use ordinal regression.

When regression is used as a screening device,the items to report are (1)dependent variable, (2)statistically significant independent variables, (3)signs of beta coefficients,and (4)standardized beta coefficients for the significant variables.

In bivariate regression analysis,the dependent variable is one that is:

When you find "mixed" results in multiple regression (i.e. ,some betas are significant,others are not),you:

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?

If Maxwell House Coffee was considering a line of gourmet iced coffee,it would want to know how coffee drinkers feel about gourmet iced coffee;that is,their attitudes toward buying,preparing,and drinking it would be the dependent variables.Maxwell House might consider developing:

The VIF is useful for identifying multicollinearity.

In bivariate regression,if the F value is not significant (say .051),then:

In bivariate regression analysis,the independent variable is one that is:

One of the best tools for unraveling prediction complexity is:

A regression line using the least squares criterion will result in high residuals.

In the formula for bivariate regression analysis,the point where the line cuts the y axis when x = 0 is known as b,the beta.

Which of the following stipulates that independent multiple regression variables must be statistically independent and uncorrelated with one another?

A prediction is a statement of what is believed will happen in the future made on the basis of past experience or prior observation.

Multiple regression requires specification of a general conceptual model that identifies independent and dependent variables and shows their expected relationships.

A regression plane is the shape of the independent variable in multiple regression analysis.

What is the best way to make a prediction?

A predictive model simply examines what has happened in the past and predicts the future.

If the ANOVA F test is not significant in bivariate regression analysis,we must trim the model by eliminating the insignificant dependent variable(s).

Multicollinearity refers to correlations among the dependent variables and makes predictions much more accurate because predicting one variable also allows you to predict the correlated variable(s).