Exam 19: Regression Analysis in Marketing Research

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:

In the following straight line formula, y = a + bx, the variable being predicted is the beta weight, b.

Immediately in bivariate analysis the researcher must find out whether or not a linear relationship exists in the population.

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

Whose paper entitled "Regression toward mediocrity in hereditary stature" began the work that gave us linear regression?

Which of the following in multiple regression is a handy measure of the strength of the overall relationship?

If you have a good regression model, apply regression analysis to predict outside of the boundaries of the data used to develop your regression model.

A predictive model is defined as an approach to prediction that:

In multiple regression analysis, we are trying to predict an independent variable using more than two dependent variables.

In evaluating your bivariate regression analysis findings you first determine whether or not a linear relationship between the independent and dependent variable exists in the population and secondly you:

What is the proper SPSS command sequence to run multiple regression analysis?

In evaluating your bivariate regression analysis findings you first determine whether or not a linear relationship between the independent and dependent variable exists in the population. Which of the following best describes what you are doing in this step?

The National Football League office discovered data covering attendance at professional football games in the late 1940s and early 1950s. The game with the highest attendance was between the St. Louis Cardinals and the New York Giants. The office also found considerable information that someone had collected on each game day, such as the level of GDP, the DOW, numbers of persons employed, number of new businesses formed during the week preceding the game, and the population. A student intern took the information and built a regression model to predict game attendance for the upcoming season. The model:

In regression the variable being predicted, b, is known as the dependent variable.

While the scaling assumptions of multiple regression require that both the independent and dependent variables be at least interval scaled, we may use nominal independent variables by using:

There is a type of multiple regression, called stepwise multiple regression, that does the trimming operation automatically.

The standard error of the estimate is used as a measure of the accuracy of the predictions in regression; it is analogous to the standard error of the mean used in estimating a population mean from a sample.

In a straight-line formula, the intercept is 4, the slope is 2, and the independent variable is 6. The predicted variable's level is:

Stepwise multiple regression is useful if a researcher has many dependent variables but needs additional dependent variables in order to obtain a good predictive model.