MKTG 323 Final Exam - FLASHCARDS - Session 11 and practice questions
(30 cards)
What type of regression has two variables: one dependent and one independent?
Simple bivariate regression
What type of scale are variables in a linear relationship in regression measured in?
Variables of interest are measured on interval or ratio scales
What is the formula for a straight line for regression with one individual variable?
𝑦 = 𝑎 + 𝑏𝑥 + 𝜀 Where,
Y - Dependent variable
a - Intercept (point where the straight line intersects the Y-axis when X = 0)
b - Slope (the change in Y for every 1 unit change in X)
X - Independent variable used to predict Y
𝜀 - Error for the prediction
What in regression analysis determines the best-fitting line by minimizing the vertical distances of all the points from the line?
Least squares procedure
What is the amount of variation in the dependent variable that cannot be accounted for by the combination of independent variables?
Unexplained variance
The value of Likely to Recommend would decrease, if
A. Food Taste increases
B. Food Temperature increases
C. Satisfaction increases
D. All of the above
E. None of the above
E. None of the above
You have collected customer satisfaction scores on a 1–7 scale from 20 respondents. You want to determine if the average score in this sample is different from a benchmark value of 4.0. Which statistical test is most appropriate to use in this situation?
A. One-sample t-test
B. Independent-samples t-test
C. Chi-square test
D. Pearson correlation
E. Simple linear regression
One-sample t-test
You want to compare the average test scores of two separate groups of students—Group A and Group B—on an interval-level measure. Which test will allow you to determine whether their means differ significantly?
A. Independent-samples t-test
B. One-sample t-test
C. Paired-samples t-test
D. Chi-square test
E. Correlation
Independent-samples t-test
You have collected categorical data on gender (male, female) and brand preference (Brand X, Brand Y). You wish to test whether there is a relationship between gender and brand preference. Which statistical test should you use?
A. Chi-square test of independence
B. Independent-samples t-test
C. One-sample t-test
D. Pearson correlation
E. Multiple regression
Chi-square test of independence
You are interested in measuring both the strength (how large) and the direction (positive or negative) of the linear association between two continuous variables—for example, customer income and amount spent. Which statistical analysis is appropriate?
A. Pearson correlation coefficient
B. One-sample t-test
C. Chi-square test
D. Independent-samples t-test
E. Cross-tabulation
Pearson correlation coefficient
You want to predict a customer’s likelihood to recommend a product (a continuous outcome variable, Y) based on their satisfaction score (a continuous predictor, X). Which analysis technique should you use?
A. Simple linear regression
B. Chi-square test
C. One-sample t-test
D. Independent-samples t-test
E. Crosstabulation
Simple linear regression
A bank surveys 15 very high-net-worth customers, records satisfaction scores (mean = 5.4, sd = 0.7), and conducts a one-sample t-test against a benchmark of 4.0. The calculated t-value is 5.136, and the critical t-value at α = 0.05 is 2.145. What decision should the bank make?
A. Reject the null hypothesis; the average satisfaction score is significantly different from 4.0
B. Fail to reject the null hypothesis; the average satisfaction score equals 4.0
C. Reject the null hypothesis; the average satisfaction score is lower than 4.0
D. Fail to reject the null hypothesis; the average satisfaction score is higher than 4.0
E. Cannot conclude without the exact p-value
Reject the null hypothesis; the average satisfaction score is significantly different from 4.0
You conduct an independent-samples t-test comparing junior versus senior account representative satisfaction. The calculated t-value is 1.231, but the critical t-value for df = 14 at α = 0.05 is 2.145. What conclusion do you draw?
A. Fail to reject the null hypothesis; no significant difference in satisfaction by rep type
B. Reject the null hypothesis; there is a significant difference
C. Reject the null hypothesis; junior reps have higher satisfaction
D. Fail to reject the null hypothesis; senior reps have higher satisfaction
E. Cannot conclude without the p-value
Fail to reject the null hypothesis; no significant difference in satisfaction by rep type
If an independent-samples t-test yields a two-tailed p-value of 0.009 for df = 32, at which significance levels can you reject H₀?
A. 1%, 5%, and 10%
B. 5% and 10% only
C. 1% only
D. None
E. Other
1%, 5%, and 10%
In a chi-square test, one cell has an expected frequency less than 5. What is the most appropriate next step?
A. Combine categories or collapse cells to increase expected counts
B. Proceed anyway, since only observed counts matter
C. Switch to a t-test
D. Increase α to 0.10
E. Increase sample size by collecting more data
Combine categories or collapse cells to increase expected counts
In the regression equation Y = a + bX + ε, what does the coefficient b represent?
A. The slope, or change in Y for each one-unit increase in X
B. The intercept, or value of Y when X = 0
C. The error term
D. The dependent variable
E. The independent variable
The slope, or change in Y for each one-unit increase in X
If a simple linear regression yields R² = 0.60, how should you interpret that?
A. 60% of the variance in Y is explained by X
B. The model is not significant
C. 40% of the variance in X is explained by Y
D. 60% of the variance in X is explained by Y
E. Cannot interpret without the p-value
60% of the variance in Y is explained by X
In a simple regression where the estimated slope is 0.74 with p = 0.00, what practical conclusion should you share?
A. For every one-unit increase in the predictor, the outcome increases by 0.74 on average
B. For every one-unit increase in the outcome, the predictor increases by 0.74
C. The predictor is not significant
D. The intercept is too low
E. You cannot interpret without the intercept
For every one-unit increase in the predictor, the outcome increases by 0.74 on average
If the p-value associated with the estimated slope is 0.19, what do you conclude about that predictor?
A. It is not a statistically significant predictor
B. It is a highly significant predictor
C. You should rerun a chi-square test instead
D. You should increase α to 0.10 to test significance
E. You should remove the intercept
It is not a statistically significant predictor
In a multiple regression model for “Likely to Recommend,” the coefficient for Food Temperature is –0.100 with p = 0.048. What does this tell you?
A. Higher perceived temperature is associated with a slight decrease in recommendations
B. Temperature is not a significant predictor
C. The sample size for temperature ratings is too small
D. Food taste is more important than temperature
E. The negative intercept is invalid
Higher perceived temperature is associated with a slight decrease in recommendations
When adding two new predictors raises adjusted R² from 0.60 to 0.64, what’s the correct interpretation?
A. Including those variables improves the model’s explanatory power
B. The model is overfitting the data
C. The regression assumptions are violated
D. The error variance increased
E. The dependent variable’s mean decreased
Including those variables improves the model’s explanatory power
If one predictor has coefficient 0.686 (p < 0.001) and another has 0.299 (p < 0.000001), which should you prioritize for action?
A. The predictor with coefficient 0.686, as it has the largest practical impact and highest significance
B. The predictor with coefficient 0.299, because it has the smallest p-value
C. The intercept, since it shifts the baseline
D. The error term, to reduce residual variance
E. Adjusted R², to maximize explained variance
The predictor with coefficient 0.686, as it has the largest practical impact and highest significance
A chi-square test shows females travel farther for a restaurant visit (p < 0.05). What marketing tactic would you recommend?
A. Develop promotions targeted at females willing to travel farther (e.g., social-media coupons valid at distant locations)
B. Lower prices only for female customers
C. Ignore gender differences and run generic ads
D. Focus solely on local advertising
E. Relocate the restaurant nearer to female customers
Develop promotions targeted at females willing to travel farther (e.g., social-media coupons valid at distant locations)
You find a Pearson correlation r = 0.50 (p < 0.05) between household income and purchase likelihood. What strategy does this support?
A. Target higher-income segments with premium offerings and messaging
B. Lower prices for all customers
C. Halt all advertising efforts
D. Switch to nominal-scale measures
E. Conduct a one-sample t-test instead
Target higher-income segments with premium offerings and messaging
