WGU D600 - STATISTICAL DATA MINING TASK 3 | PRINCIPAL
COMPONENT ANALYSIS (PCA) & REGRESSION | COMPLETE
PERFORMANCE ASSESSMENT WITH PYTHON, SCREE PLOT,
LOADINGS & MODEL EVALUATION | 2026 UPDATED
147 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
WGU D600 - STATISTICAL DATA MINING TASK 3 | PRINCIPAL COMPONENT ANALYSIS (PCA) &
REGRESSION | COMPLETE PERFORMANCE ASSESSMENT WITH PYTHON, SCREE PLOT, LOADINGS &
MODEL EVALUATION | 2026 UPDATED. It contains 147 carefully selected questions that reflect the most current
exam content and testing strategies. Each question is accompanied by a correct answer and a detailed rationale
that explains the underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 147 Questions
Foundations - Application - WGU D600 Statistical DATA Mining TASK 3 Principal Component Analysis PCA
& Regression Complete Performance Assessment WITH Python Scree PLOT Loadings & Model Evaluation
2026 Updated Statistical DATA Mining Principal Component Analysis & Regression Undergraduate YEAR 3 /
Applied DATA Science
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Principal Component 1-25 Components, Regression, Features, Retained, Model
Analysis PCA Theory
Assumptions AND
Dimensionality Reduction
DATA Preprocessing FOR 26-50 Model, Regression, Components, Variance, Features
PCA Standardization Scaling
AND Handling Missing
Values
PCA Implementation IN 51-75 Regression, Model, Components, Training, Retained
Python Scikit-learn Numpy
AND Pandas Workflows
Scree PLOT Interpretation 76-100 Regression, Components, Model, Features, Predictors
AND Component Retention
Criteria Eigenvalues
Explained Variance
PCA Loadings Component 101-125 Regression, Loadings, Model, Components, Variance
Interpretation AND Feature
Contribution Analysis
Regression Modeling 126-147 Regression, Components, Appropriate, Model, Dataset
Fundamentals Simple
Multiple AND Polynomial
Regression
TOTAL 147 All questions include answers and detailed rationales
,Section A - Principal Component Analysis PCA Theory
Assumptions AND Dimensionality Reduction
Q1.
A dataset has one variable measured in dollars (range 10-90,000) and another measured
as a proportion (0-1). Why must variables be standardized before running PCA?
A. Standardization removes multicollinearity B. PCA requires all variables to be normally
among the original variables. distributed.
C. Without scaling, the large-variance dollar D. Standardization increases the number of
variable dominates the components. extractable principal components.
Correct: C - Without scaling, the large-variance dollar variable dominates the components.
Rationale:PCA maximizes variance, so a variable with a much larger scale contributes
disproportionately to the components unless it is placed on a common scale. Standardizing to
z-scores gives each variable unit variance, so components reflect correlation structure rather
than measurement units. Multicollinearity and normality are not prerequisites for PCA, and
scaling does not change the number of components.
Why the other answers are wrong:
A. Standardization does not eliminate multicollinearity; correlated variables remain correlated
after scaling.
B. PCA is a variance-based linear technique and does not require normality of inputs.
D. The number of components equals the number of original variables regardless of scaling.
Reference: James, G. et al. (2023). An Introduction to Statistical Learning, 2nd Ed., Ch. 12.
Q2.
A scree plot shows eigenvalues of 4.8, 2.1, 1.3, 0.9, 0.5, 0.4. Using the Kaiser criterion
(eigenvalue > 1), how many components should be retained?
A. 2 B. 3
C. 4 D. 6
Correct: B - 3
Rationale:The Kaiser criterion retains components whose eigenvalue exceeds 1. Here 4.8,
2.1, and 1.3 qualify, giving three components. The fourth (0.9) falls below the threshold, so
options A and C are incorrect, and D would retain all components regardless of variance
explained.
Why the other answers are wrong:
A. Two components would ignore the eigenvalue of 1.3, which exceeds the Kaiser threshold.
C. The fourth eigenvalue (0.9) is below 1, so it is not retained under Kaiser.
Page 3
, Section A - Principal Component Analysis PCA Theory Assumptions AND Dimensionality Reduction
D. Retaining all six components defeats the purpose of dimensionality reduction.
Reference: Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational
and Psychological Measurement.
Q3.
In a PCA loadings table, PC1 has loadings of 0.62 on 'income', 0.58 on 'home_value', and
0.05 on 'age'. Which interpretation of PC1 is most defensible?
A. PC1 represents a socioeconomic/wealth B. PC1 primarily captures respondent age
dimension driven by income and home because age is orthogonal.
value.
C. PC1 is uninterpretable because loadings D. PC1 reflects measurement error since
are not all positive. loadings are below 0.70.
Correct: A - PC1 represents a socioeconomic/wealth dimension driven by income and
home value.
Rationale:High, similar loadings on income and home value indicate those variables move
together along PC1, supporting a wealth/socioeconomic interpretation. The near-zero loading
on age means age contributes little to this component, and loadings need not exceed 0.70 to
be meaningful.
Why the other answers are wrong:
B. The loading on age is only 0.05, so age contributes almost nothing to PC1.
C. Loadings may be negative or positive; interpretation depends on the pattern, not the sign
alone.
D. Loadings of ~0.6 are commonly considered strong; there is no universal 0.70 cutoff for error.
Reference: Jolliffe, I.T. & Cadima, J. (2016). Principal component analysis: a review. Phil. Trans. R. Soc.
A.
Q4.
In a multiple regression predicting salary from years_experience and education_level, the
coefficient for years_experience is 2,400 with p = 0.03. What is the correct interpretation?
A. Salary increases by $2,400 for every year B. Experience explains 2,400% of salary
of experience, holding education_level variance.
constant.
C. The average salary is $2,400 when D. There is a 3% chance the coefficient is
experience is zero. zero.
Correct: A - Salary increases by $2,400 for every year of experience, holding
education_level constant.
Page 4
COMPONENT ANALYSIS (PCA) & REGRESSION | COMPLETE
PERFORMANCE ASSESSMENT WITH PYTHON, SCREE PLOT,
LOADINGS & MODEL EVALUATION | 2026 UPDATED
147 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
WGU D600 - STATISTICAL DATA MINING TASK 3 | PRINCIPAL COMPONENT ANALYSIS (PCA) &
REGRESSION | COMPLETE PERFORMANCE ASSESSMENT WITH PYTHON, SCREE PLOT, LOADINGS &
MODEL EVALUATION | 2026 UPDATED. It contains 147 carefully selected questions that reflect the most current
exam content and testing strategies. Each question is accompanied by a correct answer and a detailed rationale
that explains the underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 147 Questions
Foundations - Application - WGU D600 Statistical DATA Mining TASK 3 Principal Component Analysis PCA
& Regression Complete Performance Assessment WITH Python Scree PLOT Loadings & Model Evaluation
2026 Updated Statistical DATA Mining Principal Component Analysis & Regression Undergraduate YEAR 3 /
Applied DATA Science
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Principal Component 1-25 Components, Regression, Features, Retained, Model
Analysis PCA Theory
Assumptions AND
Dimensionality Reduction
DATA Preprocessing FOR 26-50 Model, Regression, Components, Variance, Features
PCA Standardization Scaling
AND Handling Missing
Values
PCA Implementation IN 51-75 Regression, Model, Components, Training, Retained
Python Scikit-learn Numpy
AND Pandas Workflows
Scree PLOT Interpretation 76-100 Regression, Components, Model, Features, Predictors
AND Component Retention
Criteria Eigenvalues
Explained Variance
PCA Loadings Component 101-125 Regression, Loadings, Model, Components, Variance
Interpretation AND Feature
Contribution Analysis
Regression Modeling 126-147 Regression, Components, Appropriate, Model, Dataset
Fundamentals Simple
Multiple AND Polynomial
Regression
TOTAL 147 All questions include answers and detailed rationales
,Section A - Principal Component Analysis PCA Theory
Assumptions AND Dimensionality Reduction
Q1.
A dataset has one variable measured in dollars (range 10-90,000) and another measured
as a proportion (0-1). Why must variables be standardized before running PCA?
A. Standardization removes multicollinearity B. PCA requires all variables to be normally
among the original variables. distributed.
C. Without scaling, the large-variance dollar D. Standardization increases the number of
variable dominates the components. extractable principal components.
Correct: C - Without scaling, the large-variance dollar variable dominates the components.
Rationale:PCA maximizes variance, so a variable with a much larger scale contributes
disproportionately to the components unless it is placed on a common scale. Standardizing to
z-scores gives each variable unit variance, so components reflect correlation structure rather
than measurement units. Multicollinearity and normality are not prerequisites for PCA, and
scaling does not change the number of components.
Why the other answers are wrong:
A. Standardization does not eliminate multicollinearity; correlated variables remain correlated
after scaling.
B. PCA is a variance-based linear technique and does not require normality of inputs.
D. The number of components equals the number of original variables regardless of scaling.
Reference: James, G. et al. (2023). An Introduction to Statistical Learning, 2nd Ed., Ch. 12.
Q2.
A scree plot shows eigenvalues of 4.8, 2.1, 1.3, 0.9, 0.5, 0.4. Using the Kaiser criterion
(eigenvalue > 1), how many components should be retained?
A. 2 B. 3
C. 4 D. 6
Correct: B - 3
Rationale:The Kaiser criterion retains components whose eigenvalue exceeds 1. Here 4.8,
2.1, and 1.3 qualify, giving three components. The fourth (0.9) falls below the threshold, so
options A and C are incorrect, and D would retain all components regardless of variance
explained.
Why the other answers are wrong:
A. Two components would ignore the eigenvalue of 1.3, which exceeds the Kaiser threshold.
C. The fourth eigenvalue (0.9) is below 1, so it is not retained under Kaiser.
Page 3
, Section A - Principal Component Analysis PCA Theory Assumptions AND Dimensionality Reduction
D. Retaining all six components defeats the purpose of dimensionality reduction.
Reference: Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational
and Psychological Measurement.
Q3.
In a PCA loadings table, PC1 has loadings of 0.62 on 'income', 0.58 on 'home_value', and
0.05 on 'age'. Which interpretation of PC1 is most defensible?
A. PC1 represents a socioeconomic/wealth B. PC1 primarily captures respondent age
dimension driven by income and home because age is orthogonal.
value.
C. PC1 is uninterpretable because loadings D. PC1 reflects measurement error since
are not all positive. loadings are below 0.70.
Correct: A - PC1 represents a socioeconomic/wealth dimension driven by income and
home value.
Rationale:High, similar loadings on income and home value indicate those variables move
together along PC1, supporting a wealth/socioeconomic interpretation. The near-zero loading
on age means age contributes little to this component, and loadings need not exceed 0.70 to
be meaningful.
Why the other answers are wrong:
B. The loading on age is only 0.05, so age contributes almost nothing to PC1.
C. Loadings may be negative or positive; interpretation depends on the pattern, not the sign
alone.
D. Loadings of ~0.6 are commonly considered strong; there is no universal 0.70 cutoff for error.
Reference: Jolliffe, I.T. & Cadima, J. (2016). Principal component analysis: a review. Phil. Trans. R. Soc.
A.
Q4.
In a multiple regression predicting salary from years_experience and education_level, the
coefficient for years_experience is 2,400 with p = 0.03. What is the correct interpretation?
A. Salary increases by $2,400 for every year B. Experience explains 2,400% of salary
of experience, holding education_level variance.
constant.
C. The average salary is $2,400 when D. There is a 3% chance the coefficient is
experience is zero. zero.
Correct: A - Salary increases by $2,400 for every year of experience, holding
education_level constant.
Page 4
