Question 1

What are the possible values in correlation analysis?
A)0 to 1
B)-1 to +1
C)-1 to 0
D)-.99 to + .99
E)-2 to +2

Accepted Answer

In correlation analysis, the possible values range from -1 to +1. A value of +1 indicates a perfect positive correlation, 0 indicates no correlation, and -1 indicates a perfect negative correlation. Therefore, option B is the correct answer. Options A, C, D, and E do not accurately represent the range of values for correlation analysis.

Question 2

Which test statistic is used to test the significance of the results of a regression analysis?
A)F
B)t
C)Z
D)chi-square
E)least square

Accepted Answer

The F-statistic is commonly used to test the overall significance of a regression model and its individual coefficients. It compares the variance explained by the regression model to the residual variation. A significant F-test suggests that at least one of the independent variables is significantly related to the dependent variable.

Question 3

In regression,the higher the _________ value,the more likely the relationship between variables is significant.
A)F
B)p
C)Z
D)t
E)none of these

Accepted Answer

The F-value in regression analysis indicates the overall significance of the regression model. A higher F-value suggests that the model is more likely to be significant, meaning there is a stronger relationship between the dependent and independent variables.

Question 4

____________ is the mathematical technique for fitting a line that best describes the relationship between two variables.
A)Pearson's product-moment correlation
B)Least squares procedure
C)Chi-square test
D)Significance test
E)none of these

Accepted Answer

The least squares procedure involves minimizing the sum of the squared differences between the observed values and the predicted values of a linear regression model, in order to find the line of best fit that describes the relationship between two variables.

Question 5

If a marketer wants to correlate an ordinal ranking of TV sets with the nominal scale variable gender,the marketer would have to use_____________.
A)ANOVA
B)Pearson's product-moment correlation
C)z-test
D)chi-square test
E)none of these

Accepted Answer

The answer of If a marketer wants to correlate an...

Question 6

The explained variation by the regression is referred to as the:
A)total variation.
B)bivariate.
C)sum of squares due to regression .
D)none of these

Accepted Answer

The sum of squares due to regression represents the portion of the total variation in the dependent variable that is explained by the regression model. Therefore, option C is the correct answer. Option A refers to the overall variability in the dependent variable. Option B refers to the analysis of the relationship between two variables, which is not specific to regression. Option D is incorrect as there is a defined answer.

Question 7

An R² of .05 would indicate:
A)a significant relationship between X and Y.
B)a negative relationship between X and Y.
C)a very weak relationship between X and Y.
D)none of these

Accepted Answer

An R² of .05 indicates a very weak relationship between X and Y as it only explains 5% of the variance in Y. It does not indicate a significant relationship or a negative relationship between X and Y.

Question 8

When the value of one variable increases at exactly the same rate as another variable decreases,this is said to be a ___________________relationship.
A)strong positive linear
B)positive linear
C)perfect negative linear
D)parabolic
E)strong curvilinear association

Accepted Answer

In a perfect negative linear relationship, the value of one variable increases at the exact same rate as the other variable decreases, resulting in a straight line with a negative slope. This is the only option that fits the given description.

Question 9

Research on fear appeal in advertising suggests that a moderate amount of fear is most effective at achieving persuasion.That is,using not enough fear or too much fear in advertising will not be effective.This is an example of what type of relationship that regression might find?
A)positive linear
B)negative linear
C)curvilinear
D)no relationship
E)none of these

Accepted Answer

A curvilinear relationship suggests that there is an optimal level of fear appeal that is most persuasive, while using too little or too much fear is less effective. This is consistent with the research on fear appeal in advertising.

Question 10

When two variables are not correlated at all,the correlation coefficient would be _______.
A)-1
B)0
C)1
D)-2
E)0.5

Accepted Answer

A correlation coefficient of 0 indicates no correlation between the two variables. -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and -2 and 0.5 are not valid options for a correlation coefficient.

Question 11

Which of the following is the appropriate technique for correlation analysis,which involves metric (interval or ratio)data?
A)Spearman's rank-order correlation
B)Jensen's analysis of covariance
C)Kendall's coefficient of concordance
D)Pearson's product-moment correlation
E)Edward's metric analysis

Accepted Answer

Pearson's product-moment correlation is the appropriate technique for correlation analysis with metric (interval or ratio) data. This technique examines the linear relationship between two continuous variables and provides a coefficient, also known as a correlation coefficient, which ranges from -1 to 1. This coefficient represents the strength and direction of the relationship between the two variables.

Question 12

In a regression equation,sales would typically be the ______________ variable.
A)independent
B)coefficient
C)dependent
D)none of these

Accepted Answer

In a regression equation, the dependent variable is the one being predicted or explained by the independent variable(s). In this case, sales would typically be the variable being predicted, making it the dependent variable. Therefore, the answer is C.

Question 13

In a regression output in SPSS,R² represents:
A)the independent variable.
B)the dependent variable.
C)the estimated slope of the regression line.
D)the coefficient of determination.

Accepted Answer

R² represents the coefficient of determination, which is a measure of how much of the variation in the dependent variable (Y) can be explained by the independent variable (X). It is a value between 0 and 1, where 0 indicates no correlation and 1 indicates perfect correlation. It is a common metric used to evaluate the goodness of fit of a regression model.

Question 14

A)perfect linear association
B)strong exponential association
C)strong parabolic association
D)no association
E)strong curvilinear association

Accepted Answer

Without any context or information about the data, it is impossible to determine any association between the variables. Therefore, there is no association.

Question 15

If you are studying the impact of training on salesperson performance as indicated on an exam taken by salespersons at the end of training,salesperson sales would be the ____________ variable,and the salesperson exam score would be the _____________ variable.
A)criterion, predictor
B)covariate, independent
C)independent, predictor
D)dependent, independent
E)covariate, dependent

Accepted Answer

Salesperson sales would be the dependent variable as it is the outcome being measured, while the salesperson exam score would be the independent variable as it is the factor being tested for its impact on sales performance.

Question 16

Which of the following techniques requires that both variables are ratio or interval data?
A)bivariate regression
B)ANOVA
C)t test
D)Pearson's product-moment correlation
E)All are appropriate.

Accepted Answer

The answer of Which of the following techniques requires that...

Question 17

The percentage of total variation in the dependent variable that is described by the independent variable is expressed by_________.
A)coefficient of determination
B)correlation coefficient
C)coefficient of covariation
D)regression coefficient
E)none of these

Accepted Answer

The percentage of total variation in the dependent variable that is described by the independent variable is the coefficient of determination, also known as R-squared. It is a measure of how well the regression line fits the observed data. The correlation coefficient measures the strength and direction of the linear relationship between two variables, but it does not provide any information about how much of the variation in the dependent variable can be explained by the independent variable. The coefficient of covariation is a measure of the relative variability of two variables, and the regression coefficient is the slope of the regression line.

Question 18

A correlation analysis between sales and sales training scores results in R = +.98.Which of the following best interprets the relationship between sales and sales training?
A)98% of the salespeople taking the test have higher sales.
B)The correlation between sales and sales training is very weak and insignificant.
C)The correlation between sales and sales training is strong and positive, indicating that higher sales training scores are closely associated with higher sales and vice-versa.
D)98% of the variation in sales is explained by variations in sales training scores.
E)none of these

Accepted Answer

The answer of A correlation analysis between sales and sales...

Question 19

A regression equation that estimates the number of hours of study that impact a test score would most likely produce what type of relationship?
A)positive
B)negative
C)curvilinear
D)none of these

Accepted Answer

A positive relationship would indicate that as the number of hours of study increases, the test score also increases. This makes intuitive sense as more studying typically leads to a better understanding of the material, resulting in a higher score.

Question 20

Bivariate regression can not demonstrate:
A)when the two variables are linear.
B)when the two variables are strongly inversely related.
C)when the two variables are strongly positively related.
D)when the two variables are causally related.

Accepted Answer

Bivariate regression can establish a relationship between two variables, but it cannot demonstrate causation between them. Additional research methods, such as experimental design, are required to establish causality.

Quiz 17: Bivariate Correlation and Regression

What are the possible values in correlation analysis?

Which test statistic is used to test the significance of the results of a regression analysis?

In regression,the higher the _________ value,the more likely the relationship between variables is significant.

____________ is the mathematical technique for fitting a line that best describes the relationship between two variables.

If a marketer wants to correlate an ordinal ranking of TV sets with the nominal scale variable gender,the marketer would have to use_____________.

The explained variation by the regression is referred to as the:

An R² of .05 would indicate:

When the value of one variable increases at exactly the same rate as another variable decreases,this is said to be a ___________________relationship.

Research on fear appeal in advertising suggests that a moderate amount of fear is most effective at achieving persuasion.That is,using not enough fear or too much fear in advertising will not be effective.This is an example of what type of relationship that regression might find?

When two variables are not correlated at all,the correlation coefficient would be _______.

Which of the following is the appropriate technique for correlation analysis,which involves metric (interval or ratio) data?

In a regression equation,sales would typically be the ______________ variable.

In a regression output in SPSS,R² represents:

If you are studying the impact of training on salesperson performance as indicated on an exam taken by salespersons at the end of training,salesperson sales would be the variable,and the salesperson exam score would be the _ variable.

Which of the following techniques requires that both variables are ratio or interval data?

The percentage of total variation in the dependent variable that is described by the independent variable is expressed by_________.

A correlation analysis between sales and sales training scores results in R = +.98.Which of the following best interprets the relationship between sales and sales training?

A regression equation that estimates the number of hours of study that impact a test score would most likely produce what type of relationship?

Bivariate regression can not demonstrate: