Discriminant Analysis MCQs

Direct Definition & Conceptual Scope

AEO Verified

Discriminant Analysis constitutes an essential examination topic within Statistics, evaluating theoretical principles, historical context, and practical problem-solving applications. On MCQsMind, candidates practice verified past-paper solutions, conceptual breakdowns, and syllabus-aligned questions tailored for CSS, PMS, and competitive screening examinations.

Welcome to the verified preparation module for Multivariate Statistics on McqsMind. This comprehensive set offers solved multiple-choice questions with answers, detailed explanations, and authentic past-paper style questions aligned with the latest syllabus of Multivariate Statistics (Statistics).

100% Verified Answer Keys CSS, PMS, FPSC, PPSC, NTS Ready 2026/2027 Syllabus Guidelines

Solved Questions

Practice with answers, explanations, and exam-focused revision notes.

15 MCQs Page 1

High-Yield Exam Revision Cheat Sheet & Mnemonics

Essential exam standard definitions and formulas for rapid 2-minute revision

High-Frequency Articles

Art. 6: High Treason
Art. 9: Security of Person
Art. 19-A: Right to Information
Art. 25-A: Free Education (5–16 yrs)

Federalism & Provinces

Art. 140-A: Local Government
Art. 153: Council of Common Interests
Art. 160: National Finance Commission
Art. 175-A: Judicial Commission (JCP)

Islamic Provisions

Art. 2: Islam as State Religion
Art. 2A: Objectives Resolution Substantive
Art. 203: Federal Shariat Court (FSC)
Art. 228: Council of Islamic Ideology

Topic Notes: Discriminant Analysis

Quick Revision 2 min read 14 key points CSS, PMS, FPSC, PPSC

Section 1 of 4

Section:

1 1. Background and Meaning

3 points

Definition

Discriminant Analysis (DA): A multivariate technique used to classify observations into predefined groups based on a set of independent variables, maximizing the separation between group means.
Theory

Primary Objective: To find a linear combination of independent variables that minimizes within-group variance while maximizing between-group variance, effectively creating a discriminant function.
Definition

Discriminant Function: A mathematical equation (similar to multiple regression) that assigns a score to each case, allowing for the prediction of group membership.

3 3. Statistical Metrics and Eigenvalues

4 points

Definition

Wilks' Lambda: A measure of group separation; it represents the proportion of total variance not explained by the discriminant functions. Lower values indicate better group discrimination.
Definition

Eigenvalue: Represents the ratio of between-group sum of squares to within-group sum of squares for a discriminant function; higher values indicate higher discriminatory power.
Definition

Structure Coefficients: These measure the correlation between the independent variables and the discriminant function, helping researchers identify which variables contribute most to the group separation.
Theory

Function Limits: In an analysis with 'g' groups, the maximum number of discriminant functions that can be extracted is the lesser of (g-1) or the number of independent variables.

4 4. Advanced Techniques and Comparisons

4 points

Comparison

LDA vs. PCA: LDA is a supervised learning technique focusing on maximizing class separability, whereas PCA is an unsupervised technique focusing on maximizing total variance captured.
Exam Trap

Mahalanobis Distance: Unlike Euclidean distance, this metric accounts for the covariance structure of the data, making it the preferred distance measure in Discriminant Analysis.
Theory

Stepwise Discriminant Analysis: Employed when there are many independent variables; it iteratively selects variables that provide the most significant contribution to the discrimination, discarding redundant ones.
Exam Trap

Classification vs. Prediction: Discriminant analysis is primarily for classification (group assignment) and dimensionality reduction, not for predicting continuous dependent variables (which is the domain of Multiple Regression).

Section 1 / 4

Swipe or tap Prev / Next to revise all sections.

Want adaptive tracking for this topic?

Session Progress 0 / 0 Solved

Reveal answers to start tracking your session progress!

Which condition makes Discriminant Analysis highly sensitive to outliers?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A The requirement for large sample sizes B The assumption of categorical dependent variables C The use of means and variances in the calculation of the discriminant function D The use of non-parametric estimation methods

In Discriminant Analysis, what is the 'Prior Probability'?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A The probability that the covariance matrices are equal B The probability of correctly classifying an observation C The probability that the model is statistically significant D The probability of group membership before observing the predictor variables

What does a standardized discriminant function coefficient indicate?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A The absolute change in the dependent variable for a unit change in the predictor B The statistical significance of the predictor variable C The correlation between the predictor and the group membership D The relative contribution of each variable to the discriminant function, independent of scale

Which of the following is a common method for validating a Discriminant Analysis model?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

What is the 'Centroid' in the context of Discriminant Analysis?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A The variance of the discriminant scores within a group B The point where the discriminant function equals zero C The global mean of all observations in the dataset D The mean vector of the independent variables for a specific group

When is Stepwise Discriminant Analysis typically employed?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A When the data is non-normally distributed B When the number of groups is greater than the number of variables C When the goal is to visualize data in three dimensions D When there are many independent variables and the goal is to select the most predictive subset

What is the significance of the eigenvalue in Discriminant Analysis?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A It indicates the total number of observations in the dataset B It defines the threshold for classification error C It represents the ratio of between-group to within-group sum of squares for that function D It measures the standard deviation of the discriminant scores

In Discriminant Analysis, what are the 'Structure Coefficients'?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A The eigenvalues of the covariance matrix B The weights assigned to the variables in the discriminant function C The probabilities of group membership for each observation D The correlations between the independent variables and the discriminant functions

What is the primary difference between LDA and Principal Component Analysis (PCA)?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

A LDA is a supervised technique, while PCA is an unsupervised technique B LDA reduces dimensions, while PCA increases dimensions C LDA assumes non-linear relationships, while PCA assumes linear relationships D LDA is used for regression, while PCA is used for classification

Which distance metric is commonly used in Discriminant Analysis to account for the covariance structure of the data?

Moderate · 64% Accuracy

Concept: #DiscriminantAnalysis #MultivariateStatistics #Statistics

Pedagogical Syllabus Blueprint

Discriminant Analysis MCQs: Preparation Strategy & Exam Blueprint

Examination Weightage & Commission Distribution

In Pakistan's premier competitive examinations—including the Federal Public Service Commission (FPSC General Recruitment & CSS MPT), Punjab Public Service Commission (PPSC Lecturer, Tehsildar & PMS Screening), KPPSC, SPSC, and BPSC—questions derived from Discriminant Analysis MCQs consistently constitute a decisive 10% to 25% weightage of the Multivariate Statistics paper. Testing agencies frequently evaluate both foundational factual recall and deeper analytical comprehension, requiring aspirants to master key dates, statutory articles, constitutional clauses, and thematic definitions.

High-Yield Conceptual Pillars & Distractor Analysis

Historical trend analysis across past five-year examination cycles demonstrates that examiner traps frequently involve closely matched numerical alternatives, chronologically contiguous treaties, and semantic subtleties in statutory phraseology. When preparing Discriminant Analysis MCQs, candidates must deliberately review the underlying rationale provided for every question rather than merely memorizing raw answer options. Understanding why incorrect distractors fail is the most reliable predictor of achieving top percentiles in computerized and optical mark recognition (OMR) screening tests.

Negative Marking Mitigation & Time Management Protocol

For examinations enforcing negative marking penalties (such as PPSC's 0.25-mark deduction per erroneous response or NTS gatekeeper quotas), an aggressive elimination technique must be deployed. Candidates should systematically rule out improbable options first to elevate probability thresholds from 25% to 50% or 100%. Allocating an average of 45 to 60 seconds per item during mock trials builds the psychological stamina and cognitive precision required to complete 100-question papers within official 90-minute time limits.

Recommended Reference Sources & Statutory Reading

To achieve definitive conceptual mastery in Statistics, candidates are advised to cross-reference our solved explanations with primary gazettes, statutory texts, and canonical academic textbooks. Verifying controversial questions against authentic official repositories guarantees that prospective civil servants and professional candidates present incontestable answers during both optical paper evaluations and viva voce departmental interviews.

Solved Questions

High-Yield Exam Revision Cheat Sheet & Mnemonics

Topic Notes: Discriminant Analysis

1 1. Background and Meaning

2 2. Core Assumptions and Requirements

3 3. Statistical Metrics and Eigenvalues

4 4. Advanced Techniques and Comparisons

Discriminant Analysis MCQs: Preparation Strategy & Exam Blueprint

Examination Weightage & Commission Distribution

High-Yield Conceptual Pillars & Distractor Analysis

Negative Marking Mitigation & Time Management Protocol

Recommended Reference Sources & Statutory Reading

Browse categories

Mock Test

Subject Directory

Popular Categories