1. While studying the relationship between advertising and sales growth, a researcher
determines that the relationship is sometimes weak and at other times moderate. This variation
from one situation to another is the variation in the _____ of the relationship between advertising
and sales growth.
E. dispersion
2. If a consistent and systematic relationship is not present between two variables, then:
A. a strong association is evident.
3. A _____ relationship is one between two variables whereby the strength and/or direction
of the relationship changes over the range of both variables.
E. collinear
4. Which of the following is true of relationships between variables?
E. In a linear relationship between two variables, the strength and direction of the relationship
changes over the range of both variables.
5. In a certain town, when the number of automobiles owned went up, the number of service
stations for automobiles also went up. This illustrates the concept of _____.
A. coalescence
6. A researcher plots a scatter diagram of two variables. The dots on the plot are scattered
roughly in a circle. This indicates that the relationship (covariation) between the two variables is:
A. linear and positive.
A. The null hypothesis for the Pearson correlation coefficient states that there is always a strong
association between two variables.
8. In calculating the Pearson correlation coefficient, we assume that:
A. when the correlation coefficient is weak, there is a consistent, systematic relationship
between the two variables.
9. The Spearman rank order correlation coefficient differs from the Pearson correlation
coefficient in that the Spearman rank order correlation:
A. primarily establishes a weak association between variables, whereas the Pearson correlation
coefficient establishes a strong association between variables.
10. The coefficient of determination:
E. is a stronger measure than the Pearson correlation coefficient.
11. If the coefficient of correlation between two variables is -0.6, the coefficient of
determination will be:
A. -0.6.
12. _____ is a statistical technique that uses information about the relationship between an
independent or predictor variable and a dependent variable to make predictions.
A. Non-parametric hypothesis coefficient
13. A fundamental basis of regression analysis is the assumption of:
A. a curvilinear relationship between two weakly associated dependent variables.
14. In bivariate regression analysis, the procedure used to determine the best-fitting line is
called the:
E. minimum error procedure.
15. With regard to the least squares procedure, any data point that does not fall on the
regression line is the result of:
A. specific variance.
16. Which of the following is true of the fundamentals of regression analysis?
A. A fundamental basis of regression analysis is the assumption of a circular relationship
between the independent and dependent variables.
17. The statistical procedure that produces predictions with the lowest sum of squared
differences between actual and predicted values in a regression equation is called:
A. SPSS.
18. If a researcher is interested in measuring the effect of two independent variables on a
dependent variable, he/she should use:
A. the Pearson correlation coefficient.
20. Which of the following statements is true of model F statistics?
E. Standardization using beta coefficient augments the effects of using different scales of
measurement.
21. The pattern of covariation around the regression line which is not constant around the
regression line and varies in some way when the values change from small to medium and large
is known as _____.
A. multiple regression
22. _____ refers to the pattern of covariation that is constant around the regression line,
whether the values are small, medium, or large.
A. Linearity
23. Multicollinearity is a(n):
A. statistical procedure that estimates regression equation coefficients which produce the
lowest sum of squared differences between the actual and predicted values of the dependent
variable.
24. In a regression model, if independent variables exhibit multicollinearity, then:
A. the strength of association of variables changes according to the beta coefficient.
25. Which of the following is an advantage of the partial least squares method of structural
equation modeling?
A. Solutions are possible with the simplest models, which are based on a few questions.
B. The method is parametric, so it can be applied to data that is not normally distributed.
26. To measure whether a relationship between two variables exists, we rely on the concept
of statistical significance.
27. The strength of association between two variables is determined by the size of the
correlation coefficient.
28. When two variables have a curvilinear relationship, the formula that best describes the
linkage is very simple.
29. Covariation refers to the degree of association between two variables.
30. A scatter plot wherein the dots form an ellipse indicates a positive relationship between
variables.
31. Scatter diagrams are a visual way to describe the relationship between two variables and
the covariation they share.
32. The use of the Pearson correlation coefficient assumes the variables have a normally
distributed population.
33. The smaller the size of the coefficient of determination, the stronger the linear relationship
between the two variables being examined.
34. Large samples result in more confidence that a relationship exists, even if it is weak.
35. It is possible for a correlation to be statistically significant and still lack substantive
significance.
36. Regression analysis assumes a linear relationship is a bad description of the relationship
between two variables.
37. The use of a simple regression model assumes that the error terms associated with
making predictions are dependently distributed.
38. Regression analysis assumes there is a straight line relationship between the independent
and dependent variables.
39. The least squares procedure determines the best-fitting line by maximizing the vertical
distances of all the data points from the line.
40. In a regression analysis, the horizontal distance between the estimated regression line and
the actual data points is the unexplained variance called error.
41. In multiple regression, the value of a beta coefficient can never be greater than 1.
42. Multiple regression analysis is an extension of bivariate regression.
43. A problem area for marketing researchers in multiple regression is when the independent
variables are highly correlated among themselves.
44. When the correlations between independent variables in regression are high enough to
cause problems, one approach is to create summated scales consisting of the independent
variables that are highly correlated.
45. The calculation of a solution using the partial least squares method of structural equation
modeling is similar to ordinary least squares regression, but is extended to obtain a solution for
path models with more than two stages and variables measured with more than a single
question.
46. Discuss the relationship between the Pearson correlation coefficient and the coefficient
of determination.
47. What are the several assumptions made while calculating the Pearson correlation
coefficient?
48. Discuss multiple regression analysis.
49. Discuss the concept of multicollinearity.
50. What are the advantages of using the partial least squares method of structural equation
modeling?