Expectation_ Variance_ and Covariance MCQs

Prepare for Expectation_ Variance_ and Covariance MCQs with verified questions, past-paper solutions, and conceptual explanations for CSS, PMS, FPSC, PPSC, and NTS examinations.

23 Solved Questions • 18 Mins Practice • Updated for 2026 Competitive Exams
MCQS
2026 Solved Edition · Updated October 2026 · Verified Editorial Standards

Welcome to the verified preparation module for Expectation_ Variance_ and Covariance MCQs 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 Probability Theory (Statistics).

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

Direct Definition & Conceptual Scope

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Expectation_ Variance_ and Covariance 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.

Solved Questions

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

23 MCQs Page 1

High-Yield Exam Revision Cheat Sheet & Mnemonics

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Topic Notes: Expectation_ Variance_ and Covariance

Quick Revision 3 min read 16 key points
Section 1 of 4

1 1. Expected Value: Fundamentals

4 points
  • Definition
    Expected Value E(X): The long-run average value of a random variable over many trials, calculated as the weighted average of all possible outcomes, where weights are their respective probabilities.
  • Theory
    Constant Expectation: For any non-random constant C, the expected value E(C) is simply C itself, as the value does not vary across trials.
  • Theory
    Linearity of Expectation: The expectation of a linear combination is the linear combination of expectations, expressed as E(aX + b) = aE(X) + b, regardless of whether X and Y are independent.
  • Theory
    Additive Property: The expected value of the sum of two random variables is the sum of their individual expected values: E(X + Y) = E(X) + E(Y).
Section 1 / 4
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1
How is the variance of a scaled random variable, Var(kY), mathematically expressed?
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2
What is the variance of the linear transformation 2X + 3, expressed in terms of the variance of X?
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3
What is the result of multiplying the number of units by the profit per unit and the probability of that outcome?
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4
What is the formula for the expected mean of a hypergeometric distribution?
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5
For a discrete random variable X, how is the expected value E(X) calculated?
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6
What is the expected value of the deviation of a random variable X from its mean?
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7
What is the variance of the linear transformation (a + bX), where 'a' and 'b' are constants and 'X' is a random variable?
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8
Given a random variable X, how is the variance Var(X) defined in terms of the expectation of squared deviations?
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9
Given daily product demands of 21, 19, and 22 units with associated probabilities of 0.29, 0.40, and 0.35 respectively, and a profit of $0.50 per unit, what are the expected profits for each scenario?
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10
What term describes the arithmetic mean of all potential outcomes in a probability distribution?
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Pedagogical Syllabus Blueprint

Expectation_ Variance_ and Covariance 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 Expectation_ Variance_ and Covariance MCQs consistently constitute a decisive 10% to 25% weightage of the Probability Theory 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 Expectation_ Variance_ and Covariance 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.