Question 1

Carters, a marketer of children apparel, knows that as girls increase in age, their dress size tends to get larger. This is an example of a causal type of relationship.

Accepted Answer

The answer of Carters, a marketer of children apparel, knows...

Question 2

Which statistical technique should you use when you are attempting to answer the question: "Is there a significant relationship between the customers' disposable income (measured in dollars) and their repeat-buying behavior (measured by the number of rebuys in a twelve-month period)?"
A) Multiple regression analysis
B) Chi-Square test
C) Simple regression analysis
D) Pearson correlation coefficient
E) None of the above.

Accepted Answer

The answer of Which statistical technique should you use when...

Question 3

Which of the following statements about the interpretation of correlations is NOT true?
A) Just because two variables are correlated doesn't mean that one necessarily caused the other.
B) There is nothing in correlation analysis that can be used to establish causality.
C) When you obtain a statistically significant correlation coefficient between two variables, you can safely assume that one variable caused another.
D) All that analytical procedures can do is measure the nature and degree of association between variables.
E) Statements of causality must come from underlying knowledge and theories about the phenomena under investigation.

Accepted Answer

A statistically significant correlation does not imply causation; it only indicates a relationship between the variables. Causality requires further investigation beyond correlation analysis.

Question 4

In multiple regression analysis, t tests are used to test for the statistical significance of betas. If a beta is insignificant, it means that its respective independent variable plays no meaningful role in predicting the dependent variable and the independent variable should be "removed" from the model.

Accepted Answer

In multiple regression analysis, t tests are used to test for the statistical significance of betas. If a beta is insignificant, it means that its respective independent variable does not have a statistically significant effect on the dependent variable and can be removed from the model.

Question 5

If you have a significant and very strong (e.g., > 0.90 correlation coefficient), you may assume there is a causal relationship between the two variables.

Accepted Answer

A high correlation does not imply causation; it only indicates a strong association between two variables. Other factors or variables could be influencing the relationship.

Question 6

In the chi-square analysis, the greater the differences between the observed frequencies and the expected frequencies, the less likely it is that there will be a statistically significant relationship.

Accepted Answer

In chi-square analysis, greater differences between observed and expected frequencies increase the likelihood of a statistically significant relationship, indicating that the observed distribution is not due to chance.

Question 7

Let's assume we find in a study that the Pearson's correlation coefficient between number of years of education and cigarette smoking is -0.73. This means that as education level increases
A) smoking tends to increase.
B) smoking tends to decrease.
C) smoking changes 73%.
D) Education and smoking are unrelated.
E) An educated person smokes 73 cigarettes a day.

Accepted Answer

A Pearson's correlation coefficient of -0.73 indicates a strong negative correlation, meaning that as the number of years of education increases, cigarette smoking tends to decrease.

Question 8

If the F-value in ANOVA produces a significantly high p-value (a.k.a. "Sig." in SPSS) of 0.11 or more, then it is appropriate to proceed with a post-hoc test (e.g., Duncan).

Accepted Answer

A high p-value (e.g., 0.11) indicates that there is no statistically significant difference between group means, so a post-hoc test is not warranted. Post-hoc tests are only conducted when the ANOVA shows significant results (typically p < 0.05).

Question 9

Assume that a researcher determines that a p-value of 0.01 or below determines significance. Listed below are several correlation coefficients and their respective significance levels. Which correlation coefficient demonstrates______________an association not likely due to chance (i.e., significant)?
A) 0.22, 0.06
B) 0.75, 0.00
C) -0.32, 0.15
D) -0.76, 0.95
E) 0.26, 0.10

Accepted Answer

The correlation coefficient of 0.75 with a significance level of 0.00 demonstrates an association not likely due to chance (i.e., significant) because the p-value is less than 0.01.

Question 10

Let's assume there are sophomores, juniors, and seniors in your marketing research class and we want to know if their average GPAs differ. What is the proper statistical test?
A) t-test
B) z-test
C) Chi-square test
D) Analysis of variance (ANOVA)
E) None of the above.

Accepted Answer

ANOVA is used to compare the means of three or more groups, which is appropriate for comparing the average GPAs of sophomores, juniors, and seniors.

Question 11

The NFL office discovered data covering attendance at professional football games in the late 1940s and s. 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 should
A) accurately predict game attendance.
B) NOT predict game attendance accurately because the variable levels of today (i.e., population, Dow, etc.) are out of range of those used to build the regression model.
C) predict game attendance accurately because the variable levels of today (i.e., population, Dow, etc.) are out of range of those used to build the regression model.
D) predict game attendance accurately because the variable levels (i.e., population, Dow, etc.) are within range of those used to build the regression model.
E) None of the above.

Accepted Answer

The model is likely to be inaccurate because the variables used in the model (e.g., population, Dow) have changed significantly since the late 1940s and early 1950s, making them out of range for current predictions.

Question 12

Multiple regression analysis is useful when there are______________independent variable(s) and______________dependent variable(s).
A) more than one, one
B) one, more than one
C) more than one, more than one
D) one, one
E) None of the above.

Accepted Answer

Multiple regression analysis is used when there are multiple independent variables and one dependent variable.

Question 13

The manager of New England Patriots conducted a large survey. He wanted to know if there was an association between fans being "season ticket holders" vs. "non-season ticket holders" and whether they "bought" vs. "didn't buy" team merchandise at the game. Since his survey included these measurements, he used SPSS to run a Pearson correlation coefficient that turned out to be 0.88 with a Sig. value of .001. This meant there is a significant______________ relationship.

Accepted Answer

Pearson correlation is not appropriate for categorical data like "season ticket holders" vs. "non-season ticket holders" and "bought" vs. "didn't buy." A chi-square test would be more suitable for assessing the association between these categorical variables.

Question 14

If we were comparing the difference between the mean number of sports drinks consumed by male vs. female athletes during a typical week and we calculated a z value of 4.33, we would conclude that the probability of support of the
A) null hypothesis of no difference is less than < 0.01 because 4.33 is greater than 2.58.
B) alternative hypothesis of no difference is less than < 0.01 because 4.33 is greater than 2.58.
C) null hypothesis of no difference is less than < 0.01 because 4.33 is less than 2.58.
D) alternative hypothesis of no difference is less than < 0.01 because 4.33 is less than 2.58.
E) None of the above.

Accepted Answer

A z value of 4.33 is significantly greater than 2.58, indicating that the probability of the null hypothesis being true is less than 0.01, suggesting a significant difference between the groups.

Question 15

If a = 0.152 and b = 1.32, the simple regression equation is A) Y_i= 1.32 - 0.152 X_i B) Y_i= 0.152 + 1.32 X_i C) Y_i= 0.152 - 1.32 X_i D) Y_i= 0.152/1.32 + X_i E) None of the above.

Accepted Answer

In a simple regression equation of the form Y_i = a + bX_i, 'a' is the intercept and 'b' is the slope. Here, a = 0.152 and b = 1.32, so the equation is Y_i = 0.152 + 1.32X_i.

Question 16

In which of the following situations would it be useful to test for differences between two groups?
A) A retailer wishes to know if customer satisfaction is different between in-store vs. online shoppers.
B) A beverage company wants to know if a new beverage concept differs between users vs. nonusers of the current brand.
C) A department store wishes to know the differences between online catalogs vs. mail order catalog shoppers.
D) A state university wants to know is there is a significant difference in GPA between undergraduate and graduate students.
E) All of the above situations would benefit from tests for differences between two groups.

Accepted Answer

All the situations involve comparing two distinct groups to determine if there are significant differences in their characteristics or behaviors, making it useful to test for differences.

Question 17

Olive Garden restaurants know that customers drink beverages for lunch and alcoholic drinks at supper. This is an example of an associative type of relationship.

Accepted Answer

An associative relationship identifies patterns or connections between different variables, such as the time of day and the type of drink ordered.

Question 18

When a computed z-value (for a test for differences between two percentages), say 4.51, is larger than the standard z-value, say 1.96, then this amounts to
A) support for the null hypothesis; the two percentages are different.
B) no support for the null hypothesis; the two percentages are not different.
C) support for the null hypothesis; the two percentages is not different.
D) no support for the null hypothesis; the two percentages are different.
E) None of the above.

Accepted Answer

A computed z-value larger than the standard z-value indicates that the difference between the two percentages is statistically significant, thus providing no support for the null hypothesis.

Question 19

The multiple R, also called the coefficient of determination, in multiple regression ranges from 0.00 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

Accepted Answer

The multiple R, also called the coefficient of determination, in multiple regression ranges from 0.00 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

Question 20

Suppose you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi- Square" value of 82.123, df = 3 and the Asymp. Sig. = 0.000. This means
A) there is a significant association.
B) there is no significant association.
C) the difference is associative.
D) the means are not equal.
E) the variances are equal.

Accepted Answer

The Asymp. Sig. (p-value) of 0.000 is less than the typical significance level of 0.05, indicating a significant association between the variables.

Quiz 18: Analysis and Interpretation Multiple Variables Simultaneously

Carters, a marketer of children apparel, knows that as girls increase in age, their dress size tends to get larger. This is an example of a causal type of relationship.

Which statistical technique should you use when you are attempting to answer the question: "Is there a significant relationship between the customers' disposable income (measured in dollars) and their repeat-buying behavior (measured by the number of rebuys in a twelve-month period) ?"

Which of the following statements about the interpretation of correlations is NOT true?

If you have a significant and very strong (e.g., > 0.90 correlation coefficient), you may assume there is a causal relationship between the two variables.

In the chi-square analysis, the greater the differences between the observed frequencies and the expected frequencies, the less likely it is that there will be a statistically significant relationship.

Let's assume we find in a study that the Pearson's correlation coefficient between number of years of education and cigarette smoking is -0.73. This means that as education level increases

If the F-value in ANOVA produces a significantly high p-value (a.k.a. "Sig." in SPSS) of 0.11 or more, then it is appropriate to proceed with a post-hoc test (e.g., Duncan).

Let's assume there are sophomores, juniors, and seniors in your marketing research class and we want to know if their average GPAs differ. What is the proper statistical test?

Multiple regression analysis is useful when there areindependent variable(s) anddependent variable(s) .

If we were comparing the difference between the mean number of sports drinks consumed by male vs. female athletes during a typical week and we calculated a z value of 4.33, we would conclude that the probability of support of the

If a = 0.152 and b = 1.32, the simple regression equation is

In which of the following situations would it be useful to test for differences between two groups?

Olive Garden restaurants know that customers drink beverages for lunch and alcoholic drinks at supper. This is an example of an associative type of relationship.

When a computed z-value (for a test for differences between two percentages) , say 4.51, is larger than the standard z-value, say 1.96, then this amounts to

The multiple R, also called the coefficient of determination, in multiple regression ranges from 0.00 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

Suppose you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi- Square" value of 82.123, df = 3 and the Asymp. Sig. = 0.000. This means

Quiz 18: Analysis and Interpretation Multiple Variables Simultaneously

Carters, a marketer of children apparel, knows that as girls increase in age, their dress size tends to get larger. This is an example of a causal type of relationship.

Which statistical technique should you use when you are attempting to answer the question: "Is there a significant relationship between the customers' disposable income (measured in dollars) and their repeat-buying behavior (measured by the number of rebuys in a twelve-month period) ?"

Which of the following statements about the interpretation of correlations is NOT true?

If you have a significant and very strong (e.g., > 0.90 correlation coefficient), you may assume there is a causal relationship between the two variables.

In the chi-square analysis, the greater the differences between the observed frequencies and the expected frequencies, the less likely it is that there will be a statistically significant relationship.

Let's assume we find in a study that the Pearson's correlation coefficient between number of years of education and cigarette smoking is -0.73. This means that as education level increases

If the F-value in ANOVA produces a significantly high p-value (a.k.a. "Sig." in SPSS) of 0.11 or more, then it is appropriate to proceed with a post-hoc test (e.g., Duncan).

Let's assume there are sophomores, juniors, and seniors in your marketing research class and we want to know if their average GPAs differ. What is the proper statistical test?

Multiple regression analysis is useful when there are______________independent variable(s) and______________dependent variable(s) .

If we were comparing the difference between the mean number of sports drinks consumed by male vs. female athletes during a typical week and we calculated a z value of 4.33, we would conclude that the probability of support of the

If a = 0.152 and b = 1.32, the simple regression equation is

In which of the following situations would it be useful to test for differences between two groups?

Olive Garden restaurants know that customers drink beverages for lunch and alcoholic drinks at supper. This is an example of an associative type of relationship.

When a computed z-value (for a test for differences between two percentages) , say 4.51, is larger than the standard z-value, say 1.96, then this amounts to

The multiple R, also called the coefficient of determination, in multiple regression ranges from 0.00 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

Suppose you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi- Square" value of 82.123, df = 3 and the Asymp. Sig. = 0.000. This means

Multiple regression analysis is useful when there areindependent variable(s) anddependent variable(s) .