MATH 534 Week 7 Course Project,
Final Part C; Regression and
Correlation Analysis
Course
MATH 534 (MATH534)
1. What is the primary purpose of regression analysis?
A. To classify qualitative data only
B. To examine and model the relationship between variables for prediction and explanation
C. To calculate probabilities only
D. To organize categorical data
Answer: B
Solution: Regression analysis examines how one or more independent variables influence a
dependent variable and is commonly used for prediction and inference.
2. In simple linear regression, the dependent variable is also called the:
A. Predictor variable
B. Independent variable
C. Response variable
D. Explanatory variable
Answer: C
Solution: The dependent (response) variable is the outcome the model seeks to predict or
explain.
3. Which variable is used to predict the dependent variable?
A. Response variable
B. Independent variable
C. Outcome variable
D. Residual
Answer: B
Solution: The independent (predictor) variable explains variation in the dependent variable.
,4. Which statistic measures the strength and direction of a linear relationship between two
quantitative variables?
A. Standard deviation
B. Pearson correlation coefficient (r)
C. Variance
D. Chi-square statistic
Answer: B
Solution: Pearson's correlation coefficient ranges from -1 to +1 and measures the strength and
direction of a linear association.
5. A correlation coefficient of +1 indicates:
A. No relationship
B. Perfect positive linear relationship
C. Perfect negative relationship
D. Random association
Answer: B
Solution: An r value of +1 indicates that all observations fall exactly on a positively sloped line.
6. A correlation coefficient of -1 indicates:
A. Weak positive relationship
B. Perfect negative linear relationship
C. No correlation
D. Curvilinear relationship
Answer: B
Solution: An r value of -1 represents a perfect inverse linear relationship.
7. A correlation coefficient close to zero indicates:
A. Strong positive relationship
B. Strong negative relationship
, C. Little or no linear relationship
D. Perfect prediction
Answer: C
Solution: Values near zero indicate little or no linear association between variables.
8. Which graph is most appropriate for evaluating the relationship between two quantitative
variables?
A. Pie chart
B. Scatterplot
C. Histogram
D. Bar chart
Answer: B
Solution: Scatterplots visually display the direction, strength, and form of relationships.
9. In the regression equation ŷ = b₀ + b₁x, what does b₀ represent?
A. Correlation coefficient
B. Y-intercept
C. Residual error
D. Slope
Answer: B
Solution: The intercept is the predicted value of Y when X equals zero.
10. In the regression equation ŷ = b₀ + b₁x, b₁ represents the:
A. Standard deviation
B. Variance
C. Slope of the regression line
D. Correlation coefficient
Answer: C
Solution: The slope indicates the expected change in Y for each one-unit increase in X.
Final Part C; Regression and
Correlation Analysis
Course
MATH 534 (MATH534)
1. What is the primary purpose of regression analysis?
A. To classify qualitative data only
B. To examine and model the relationship between variables for prediction and explanation
C. To calculate probabilities only
D. To organize categorical data
Answer: B
Solution: Regression analysis examines how one or more independent variables influence a
dependent variable and is commonly used for prediction and inference.
2. In simple linear regression, the dependent variable is also called the:
A. Predictor variable
B. Independent variable
C. Response variable
D. Explanatory variable
Answer: C
Solution: The dependent (response) variable is the outcome the model seeks to predict or
explain.
3. Which variable is used to predict the dependent variable?
A. Response variable
B. Independent variable
C. Outcome variable
D. Residual
Answer: B
Solution: The independent (predictor) variable explains variation in the dependent variable.
,4. Which statistic measures the strength and direction of a linear relationship between two
quantitative variables?
A. Standard deviation
B. Pearson correlation coefficient (r)
C. Variance
D. Chi-square statistic
Answer: B
Solution: Pearson's correlation coefficient ranges from -1 to +1 and measures the strength and
direction of a linear association.
5. A correlation coefficient of +1 indicates:
A. No relationship
B. Perfect positive linear relationship
C. Perfect negative relationship
D. Random association
Answer: B
Solution: An r value of +1 indicates that all observations fall exactly on a positively sloped line.
6. A correlation coefficient of -1 indicates:
A. Weak positive relationship
B. Perfect negative linear relationship
C. No correlation
D. Curvilinear relationship
Answer: B
Solution: An r value of -1 represents a perfect inverse linear relationship.
7. A correlation coefficient close to zero indicates:
A. Strong positive relationship
B. Strong negative relationship
, C. Little or no linear relationship
D. Perfect prediction
Answer: C
Solution: Values near zero indicate little or no linear association between variables.
8. Which graph is most appropriate for evaluating the relationship between two quantitative
variables?
A. Pie chart
B. Scatterplot
C. Histogram
D. Bar chart
Answer: B
Solution: Scatterplots visually display the direction, strength, and form of relationships.
9. In the regression equation ŷ = b₀ + b₁x, what does b₀ represent?
A. Correlation coefficient
B. Y-intercept
C. Residual error
D. Slope
Answer: B
Solution: The intercept is the predicted value of Y when X equals zero.
10. In the regression equation ŷ = b₀ + b₁x, b₁ represents the:
A. Standard deviation
B. Variance
C. Slope of the regression line
D. Correlation coefficient
Answer: C
Solution: The slope indicates the expected change in Y for each one-unit increase in X.
