Question 1

Bivariate regression analysis is defined as a predictive analysis technique in which:
A) a pattern is identified over time and projected into the future
B) a relationship that exists across time is observed to make a prediction
C) one variable is used to predict the level of another by use of the straight-line formula
D) one variable is used to predict the level of another by use of a scatter diagram
E) a relationship that exists at one point in time is observed to make a prediction

Accepted Answer

Bivariate regression analysis is a technique in which a straight-line formula (or equation) is used to predict the level of one variable based on the level of another variable. The aim of this analysis is to find the best-fitting line that describes the relationship between the two variables. Therefore, option C is the best choice as it correctly describes bivariate regression analysis.

Question 2

Which of the following residuals shows an exact prediction?
A) 0
B) +1.0
C) -25
D) +25
E) 100.0

Accepted Answer

A residual of 0 indicates that the predicted value and the actual value are the same, meaning that the prediction was exact.

Question 3

In bivariate regression analysis, the dependent variable is one that is:
A) used to predict the independent variable, and it is the x in the regression formula
B) used to predict the independent variable, and it is the y in the regression formula
C) predicted, and it is usually termed x in the regression formula
D) predicted, and it is usually termed y in the regression formula
E) predicted, and it is termed b in the regression formula

Accepted Answer

In bivariate regression analysis, the dependent variable is the one that is being predicted, and it is typically represented by y in the regression formula (e.g., y = a + bx). The independent variable is the one that is used to predict the dependent variable and is typically represented by x in the formula.

Question 4

In the formula for a straight line, the intercept is known as:
A) the dependent variable
B) the variable used to predict the dependent variable
C) the change in y for any unit change in x
D) the point where the line cuts the y axis when x = 0
E) b

Accepted Answer

The intercept is the point where the line cuts the y axis when x = 0. This is commonly represented in the formula as b or the y-intercept.

Question 5

Which of the following is NOT true of prediction?
A) It is a statement of what is believed will happen in the future.
B) It may be based on prior observation.
C) We are seldom confronted with the need to make predictions.
D) It may be based on past experience.
E) Marketing managers are constantly faced with the need to make predictions.

Accepted Answer

The statement "We are seldom confronted with the need to make predictions" is not true. In fact, predictions play a significant role in many aspects of our lives, including business, finance, sports, and weather forecasting. Marketing managers, for example, constantly make predictions about consumer behavior and market trends to inform their strategies and decision-making.

Question 6

Which of the following SPSS commands allows you to run bivariate regression?
A) ANALYZE; BIVARIATE; REGRESSION
B) ANALYZE; REGRESSION; BIVARIATE
C) REGRESSION; BIVARIATE
D) ANALYZE; REGRESSION; LINEAR
E) REGRESSION; BIVARIATE; LINEAR

Accepted Answer

The answer of Which of the following SPSS commands allows...

Question 7

When you compare how far the predicted values are from the actual or observed values, you are performing:
A) analysis of prediction
B) control
C) analysis of residuals
D) analysis of variance
E) analysis of values

Accepted Answer

The process of comparing predicted values to actual or observed values and analyzing the differences between them is known as analysis of residuals. This is typically done to assess the accuracy and reliability of a statistical model, and to identify any sources of error or bias in the model. Options A, B, D, and E do not accurately describe this process.

Question 8

A good way to assess a predictive model's predictive accuracy is through:
A) measuring its predictive accuracy
B) measuring its analysis of residuals
C) measuring its analysis of variance
D) measuring its reliability
E) measuring its consistency

Accepted Answer

The answer of A good way to assess a predictive...

Question 9

In the formula for a straight line, the slope is defined as:
A) the change in y for any 1-unit change in x
B) where the line cuts the y axis when x = 0
C) the variable used to predict the dependent variable
D) the dependent variable
E) the predicted variable

Accepted Answer

The slope is defined as the change in y for any 1-unit change in x.

Question 10

What are two ways of making a prediction?
A) guessing and using statistics
B) extrapolation and guessing
C) building a predictive model and guessing
D) extrapolation and building a predictive model
E) guessing and hypothesizing

Accepted Answer

Extrapolation involves extending current trends, patterns or relationships into the future, while building a predictive model involves analyzing historical data and pattern, and using it to forecast future trends. Therefore, using extrapolation and building a predictive model are two reliable ways of making predictions.

Question 11

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?
A) the plum line criterion
B) the least squares criterion
C) the bearing line criterion
D) the b slope criterion
E) the right angle criterion

Accepted Answer

The least squares criterion is used to establish the best fit of a straight line through the points on a scatter diagram. This criterion minimizes the sum of the squared distances between each data point and the line of best fit, indicating the line that is closest to all of the data points on the scatter plot.

Question 12

In bivariate regression analysis, the independent variable is one that is:
A) used to predict the dependent variable, and it is the x in the regression formula
B) used to predict the dependent variable, and it is the y in the regression formula
C) predicted, and it is the x in the regression formula
D) predicted, and it is the y in the regression formula
E) used to predict the dependent variable, and it is the b in the regression formula

Accepted Answer

In bivariate regression analysis, the independent variable is the variable that is used to predict the dependent variable, and it is represented as the x in the regression formula. The dependent variable is the variable that is predicted, and it is represented as the y in the regression formula. Therefore, choice A is the correct answer.

Question 13

Extrapolation is a process of making predictions by:
A) using surveys as a means of predicting the future
B) using external factors as a means of predicting the future
C) using past experience as a means of predicting the future
D) identifying correlations
E) taking the mean of several possible scenarios

Accepted Answer

Extrapolation involves using past data and trends to make predictions about future outcomes. This means that choice C, using past experience as a means of predicting the future, is the best choice. Surveys (choice A) and external factors (choice B) may be used as part of the extrapolation process, but they are not the primary method. Identifying correlations (choice D) can also be helpful in the extrapolation process, but again, it is not the primary method. Taking the mean of several possible scenarios (choice E) is not necessarily a part of extrapolation.

Question 14

In bivariate regression analysis, the higher the Adjusted R Square value, the:
A) lower the predictive power of the analysis
B) the better the straight line's fit to the scatter points
C) the worse the straight line's fit to the scatter points
D) closer to 0 it will be
E) None of the above; there is no Adjusted R Square value in regression analysis.

Accepted Answer

The Adjusted R Square value measures the goodness-of-fit of the regression line to the scatter points, indicating how well the model fits the data. A higher Adjusted R Square value means that the model explains more variation in the data, indicating a better fit of the line to the points. Therefore, the best choice is B, which states that the higher the Adjusted R Square value, the better the straight line's fit to the scatter points.

Question 15

Whose paper entitled "Regression toward mediocrity in hereditary stature" began the work that gave us linear regression?
A) Sir Francis Galton
B) Sir Isaac Newton
C) Adam Smith
D) John Law
E) Edward Bernays

Accepted Answer

The paper entitled "Regression toward mediocrity in hereditary stature" was written by Sir Francis Galton in 1885 and introduced the concept of regression analysis, including the method of least squares for fitting a line to data. This work laid the foundation for the development of linear regression as a statistical technique.

Question 16

The "goodness" of predictions refers to:
A) accuracy
B) reliability
C) the way the prediction is written
D) identification
E) consistency

Accepted Answer

The "goodness" of predictions refers to their accuracy, i.e. how closely they match with the actual outcome. While reliability, consistency, and identification are all important factors, none of them explicitly capture the accuracy of the prediction. The way the prediction is written may impact its clarity, but it does not necessarily determine its goodness.

Question 17

You might be using extrapolation as an approach for stating that:
A) since your professor's exam was easy today, the last one must have been easy too
B) you will have to study more for your exams
C) your professor's exam will have to be more difficult than the last two easy ones
D) since your professor's last two exams were easy, the next should be easy as well
E) your professor will not give another exam this semester

Accepted Answer

Extrapolation involves making predictions based on trends or patterns observed in past data. In this case, the trend is that the professor's last two exams were easy, so it is extrapolated that the next exam should also be easy. Therefore, choice D is the correct answer.

Question 18

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?
A) determining if the two variables have any covariation
B) determining if the two variables vary together
C) determining if the two variables belong in the same regression matrix
D) determining if the two variables are isotonic
E) determining if there is statistical significance

Accepted Answer

The answer of In evaluating your bivariate regression analysis findings...

Question 19

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:
A) determine the significance of the intercept and the slope
B) determine the significance of the covariation
C) determine if the two variables vary together
D) determine if the two variables belong in the same regression matrix
E) determine if the two variables predict the intercept and the slope

Accepted Answer

In evaluating bivariate regression analysis findings, determining the significance of the intercept and slope is crucial in assessing the strength and direction of the linear relationship between the independent and dependent variables. It helps to interpret the coefficients of the regression equation, which can reveal whether the relationship is positive or negative and how much variance in the dependent variable is explained by the independent variable. The other options (B, C, D, E) are all related but secondary considerations in assessing bivariate regression findings.

Question 20

A predictive model is defined as an approach to prediction that:
A) relates the conditions expected to be in place influencing the factor that is being predicted
B) observes a consistent pattern over time
C) identifies a pattern and projects it into the future
D) uses past experience to predict the future
E) uses current experience to explain the past

Accepted Answer

A predictive model relates the conditions expected to be in place influencing the factor that is being predicted.

Quiz 19: Regression Analysis in Marketing Research

Bivariate regression analysis is defined as a predictive analysis technique in which:

Which of the following residuals shows an exact prediction?

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

In the formula for a straight line, the intercept is known as:

Which of the following is NOT true of prediction?

Which of the following SPSS commands allows you to run bivariate regression?

When you compare how far the predicted values are from the actual or observed values, you are performing:

A good way to assess a predictive model's predictive accuracy is through:

In the formula for a straight line, the slope is defined as:

What are two ways of making a prediction?

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

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

Extrapolation is a process of making predictions by:

In bivariate regression analysis, the higher the Adjusted R Square value, the:

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

The "goodness" of predictions refers to:

You might be using extrapolation as an approach for stating that:

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?

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:

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