WGU C955 - MODULE 5: DESCRIPTIVE
STATISTICS FOR TWO VARIABLES EXAM 2026/
QUESTIONS AND VERIFIED ANSWERS
10 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Interpret scatterplots to identify direction, form, strength, and outliers
2 Compute and interpret covariance and Pearson correlation coefficient
3 Fit and interpret simple linear regression models, including slope, intercept, and residuals
4 Evaluate model fit using r-squared and residual plots
5 Apply bivariate descriptive methods to novel contexts and detect common pitfalls
6 WGU C955
7 Module 5
8 Descriptive Statistics for Two Variables Exam 2026
9 Questions and Verified Answers
10 Foundations of Statistics (Descriptive Statistics for Two Variables)
ABSTRACT
This study document brings together 10 carefully worded exam questions drawn from WGU C955 -
Module 5: Descriptive Statistics for Two Variables Exam 2026/ Questions and Verified Answers,
with the strongest emphasis placed on Interpret scatterplots to identify direction, form, strength,
and outliers, Compute and interpret covariance and Pearson correlation coefficient, Fit and
interpret simple linear regression models, including slope, intercept and and residuals. Every item
follows the wording style and level of reasoning you meet in the real paper, and each one is paired
with a clear rationale so the correct choice is never a guess. Work through the set at your own
pace, mark the questions that slow you down, then come back to them until the reasoning feels
automatic. Learners who revise this way walk into the exam room recognising the pattern behind
the questions instead of meeting them for the first time. Keep going - steady, honest practice is
what turns a difficult paper into a comfortable pass.
Page 1
, Q1 INTERPRET SCATTERPLOTS TO IDENTIFY DIRECTION, FORM, STRENGTH, AND
OUTLIERS
A scatterplot of two variables shows a strong nonlinear (curved) relationship that
is monotonically increasing. Which statement best describes the Pearson
correlation coefficient for these data?
A. It will be close to +1 because the relationship is strong and increasing.
B. It will be close to 0 because the relationship is not linear.
C. It will be positive but may underestimate the strength due to nonlinearity. CORRECT
D. It will be negative because the curve causes a reversal in direction.
RATIONALE: Pearson's r measures linear association; a monotonic nonlinear relationship can
still yield a positive r, but its magnitude may not fully capture the strength. Option A is incorrect
because r is not designed for nonlinearity; B is too extreme; D is wrong because the relationship
is increasing, not decreasing.
Q2 COMPUTE AND INTERPRET COVARIANCE AND PEARSON CORRELATION COEFFICIENT
In a simple linear regression analysis, the residual plot displays a clear U-shaped
pattern. What does this indicate about the model?
A. The linear model is appropriate because residuals are randomly scattered.
B. The linear model is inadequate; a nonlinear term may be needed. CORRECT
C. There is heteroscedasticity, but the linear form is still valid.
D. The residuals are normally distributed, confirming model validity.
RATIONALE: A U-shaped residual pattern suggests systematic lack of fit, meaning the linear
model does not capture the relationship. Option A is false because random scatter is absent; C
misinterprets heteroscedasticity; D is irrelevant to the pattern.
Page 2
STATISTICS FOR TWO VARIABLES EXAM 2026/
QUESTIONS AND VERIFIED ANSWERS
10 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Interpret scatterplots to identify direction, form, strength, and outliers
2 Compute and interpret covariance and Pearson correlation coefficient
3 Fit and interpret simple linear regression models, including slope, intercept, and residuals
4 Evaluate model fit using r-squared and residual plots
5 Apply bivariate descriptive methods to novel contexts and detect common pitfalls
6 WGU C955
7 Module 5
8 Descriptive Statistics for Two Variables Exam 2026
9 Questions and Verified Answers
10 Foundations of Statistics (Descriptive Statistics for Two Variables)
ABSTRACT
This study document brings together 10 carefully worded exam questions drawn from WGU C955 -
Module 5: Descriptive Statistics for Two Variables Exam 2026/ Questions and Verified Answers,
with the strongest emphasis placed on Interpret scatterplots to identify direction, form, strength,
and outliers, Compute and interpret covariance and Pearson correlation coefficient, Fit and
interpret simple linear regression models, including slope, intercept and and residuals. Every item
follows the wording style and level of reasoning you meet in the real paper, and each one is paired
with a clear rationale so the correct choice is never a guess. Work through the set at your own
pace, mark the questions that slow you down, then come back to them until the reasoning feels
automatic. Learners who revise this way walk into the exam room recognising the pattern behind
the questions instead of meeting them for the first time. Keep going - steady, honest practice is
what turns a difficult paper into a comfortable pass.
Page 1
, Q1 INTERPRET SCATTERPLOTS TO IDENTIFY DIRECTION, FORM, STRENGTH, AND
OUTLIERS
A scatterplot of two variables shows a strong nonlinear (curved) relationship that
is monotonically increasing. Which statement best describes the Pearson
correlation coefficient for these data?
A. It will be close to +1 because the relationship is strong and increasing.
B. It will be close to 0 because the relationship is not linear.
C. It will be positive but may underestimate the strength due to nonlinearity. CORRECT
D. It will be negative because the curve causes a reversal in direction.
RATIONALE: Pearson's r measures linear association; a monotonic nonlinear relationship can
still yield a positive r, but its magnitude may not fully capture the strength. Option A is incorrect
because r is not designed for nonlinearity; B is too extreme; D is wrong because the relationship
is increasing, not decreasing.
Q2 COMPUTE AND INTERPRET COVARIANCE AND PEARSON CORRELATION COEFFICIENT
In a simple linear regression analysis, the residual plot displays a clear U-shaped
pattern. What does this indicate about the model?
A. The linear model is appropriate because residuals are randomly scattered.
B. The linear model is inadequate; a nonlinear term may be needed. CORRECT
C. There is heteroscedasticity, but the linear form is still valid.
D. The residuals are normally distributed, confirming model validity.
RATIONALE: A U-shaped residual pattern suggests systematic lack of fit, meaning the linear
model does not capture the relationship. Option A is false because random scatter is absent; C
misinterprets heteroscedasticity; D is irrelevant to the pattern.
Page 2
