Start Learning Free

Mathematics • Class 11

Probability - Class 11

Master Probability — Fundamentals through focused practice that strengthens conceptual understanding, problem-solving skills, and the ability to recognize common traps and misconceptions. Explore challenging questions designed to develop deeper reasoning and confident application of key ideas.

Overview

This chapter introduces Probability, covering the basic rules, conditional probability, and Bayes' theorem. Learn to analyze random events and calculate probabilities effectively.

Why This Chapter Matters

  • Probability is a cornerstone of mathematics, crucial for understanding uncertainty and risk in various fields such as finance, science, and engineering. It provides tools for decision-making based on data and randomness.

Real-Life Applications

  • Predicting outcomes in finance, such as stock market trends and insurance risks.
  • Analyzing experimental results in scientific research.
  • Making decisions in situations involving uncertainty in everyday life.

Skills You Will Learn

  • Understanding basic probability rules and concepts.
  • Applying conditional probability and Bayes' theorem.
  • Solving problems involving random experiments and probability distributions.

Core Concepts to Master

Sample Space Construction

Differentiating between outcomes that are equiprobable versus those that are not; identifying hidden constraints in set definitions.

Axiomatic Probability Constraints

Rigorous application of Kolmogorov’s axioms where P(S)=1 and P(A) is within [0,1]; vital for impossible event detection.

Algebra of Events

Mapping set-theoretic operations (Union, Intersection, Complement) to logical connectives (OR, AND, NOT) in real-world scenarios.

Mutually Exclusive vs Independent

Crucial distinction: Mutually Exclusive implies P(A∩B)=0; Independent implies P(A∩B)=P(A)P(B). Confusing these is the primary failure point.

Equiprobability Fallacy

Assuming outcomes are equally likely when they are not, often triggered by biased dice or non-random selection processes.

Combinatorial Counting (P&C Integration)

Mastery of permutations vs combinations in probability; selecting order-dependent vs order-independent sets of outcomes.

Complementary Counting

Using 1-P(A') to simplify complex problems; essential when calculating 'at least one' type probability problems.

Conditional Probability Foundations

Understanding the reduction of the sample space; P(A|B) requires shifting the denominator to the condition B.

Multiplication Theorem

Applying P(A∩B) = P(A)P(B|A) in sequential events where the second event depends on the outcome of the first.

De Morgan’s Laws in Probability

Transforming complex negation statements like 'neither A nor B' into union/intersection forms for easier calculation.

Inclusion-Exclusion Principle

Correct calculation for P(A∪B∪C) avoiding double counting intersections between multiple non-mutually exclusive events.

Exhaustive Events Concept

Ensuring the sum of probabilities of a partition equals 1; common trap in multi-stage game theory problems.

Random Variable Mapping

Assigning numerical values to qualitative outcomes; the transition point to probability distributions in higher math.

Symmetry and Invariance

Using rotational or reflectional symmetry in sample spaces to eliminate tedious counting; a hallmark of high-ranker intuition.

Indicator Function Logic

Using binary 0/1 variables to represent event occurrence, simplifying expected value calculations in complex series.

Concept Collisions Used by Examiners

Competitive examinations rarely assess concepts in isolation. Instead, examiners intentionally combine multiple concepts from this and related chapters to create higher-order questions that test conceptual reasoning, analytical thinking, and problem-solving ability. The concept collision patterns below represent the combinations most frequently used while designing questions for this chapter.

Sample Space Construction
Bernoulli Trials Logic
Scalar Triple Product Volume Logic
Angle between two planes
Sample Space Construction
Derangements (Inclusion-Exclusion)
Closed vs Open Intervals
Binary Operation Closure
Sample Space Construction
Multiplication Theorem
Logarithmic Differentiation Domain
Limit Existence at Discontinuity
Sample Space Construction
Sample Space Partitioning
Symmetry Utilization
Even and Odd Function Symmetry
Axiomatic Probability Constraints
Division into Groups
Unbounded Region Trap
Domain Restrictions
Axiomatic Probability Constraints
Independent vs Disjoint Events
Equiprobability Fallacy
Axiomatic Probability Constraints
Inequalities in Definite Integrals
Closed vs Open Intervals
Global Extrema on Intervals
Axiomatic Probability Constraints
Infinite GP Convergence
The Telescopic Series Trap
Axiomatic Probability Constraints
Leibniz Rule for Differentiation under Integral
Principal Value Branch Boundaries
Axiomatic Probability Constraints
Scalar Triple Product Volume Logic
Dot Product Projection Sign
Axiomatic Probability Constraints
Skew-Symmetric Determinant
System of Linear Equations (Cramer/Inverse)
Algebra of Events
Adjoint-Determinant Relation
Redundant Constraints
Algebra of Events
Binomial Distribution Tail-Ends
Polynomial Function Parity
Algebra of Events
Collinearity vs Coplanarity
Dot Product Projection Sign
Algebra of Events
Combinatorial Counting (P&C Integration)
Circular Permutations Constraint
Algebra of Events
Distributing Identical Items (Stars and Bars)
Triangle Inequality Mind Games
Algebra of Events
Properties of Binomial Coefficients
Even Multiplicity Roots
Algebra of Events
Sample Space Partitioning
Step Function Discontinuities
Algebra of Events
Skew-Symmetric Determinant
Even Multiplicity Roots
Even and Odd Function Symmetry
Mutually Exclusive vs Independent
Bernoulli Trials Logic
General Term Formulation
Mutually Exclusive vs Independent
Binomial Coefficient Summation Properties
The Empty Set Trap
Mutually Exclusive vs Independent
Differentiability vs Continuity
Composition Continuity
Mutually Exclusive vs Independent
Homogeneous Function Homogeneity
Implicit to Explicit Conversion
Mutually Exclusive vs Independent
Indicator Function Logic
Sign Flipping Rule
Mutually Exclusive vs Independent
Sample Space Partitioning
Venn Diagram Overlap Logic
Mutually Exclusive vs Independent
Surjective Mapping (Onto)
Greatest Integer Function (GIF)
Mutually Exclusive vs Independent
Symmetry Utilization
Horizontal vs Vertical Strips
Mutually Exclusive vs Independent
Transpose of Product
Determinant Scaling Property
Mutually Exclusive vs Independent
Vandermonde's Identity
Binomial Coefficient Summation Properties
Equiprobability Fallacy
Bayes' Theorem Reversal
The Zero-Factor Cancellation Trap
Equiprobability Fallacy
Distributing Identical Items (Stars and Bars)
Cross Multiplication Trap
Equiprobability Fallacy
Probability Mass Function (PMF)
Signum Function Discontinuity
Equiprobability Fallacy
Random Variable Mapping
Second Derivative Test Limitations
Equiprobability Fallacy
Rank of a Word
Cartesian Product Cardinality
Equiprobability Fallacy
Sample Space Construction
Circular Permutations Constraint
Equiprobability Fallacy
Scalar Triple Product Volume Logic
Triangle Inequality Mind Games
Equiprobability Fallacy
Total Selection (at least one)
Difference vs. Symmetric Difference
Combinatorial Counting (P&C Integration)
Bayes' Theorem Reversal
The Empty Set Trap
Inclusion-Exclusion Principle
Combinatorial Counting (P&C Integration)
Injective Mapping (One-One)
Domain Restrictions
Domain Constraint Intersections
Combinatorial Counting (P&C Integration)
Scalar Triple Product Volume Logic
Symmetric vs Non-symmetric form
Position Vector Origin Dependence
Combinatorial Counting (P&C Integration)
Vandermonde's Identity
Binomial Coefficient Summation Properties
Numerically Greatest Term
Combinatorial Counting
Bayes' Theorem Reversal
Polynomial Continuity
Closed vs Open Intervals
Combinatorial Counting
Factor Theorem in Determinants
Triangular Matrix Shortcut
The Modulus Trap
Combinatorial Counting
Gap Method
Distance Formula in 3D
Angle between line and plane
Combinatorial Counting
Roots of Unity Properties
Conjugate Root Theorem
Matrix Multiplication Non-Commutativity
Combinatorial Counting
Surjective Mapping
Bijectivity and Inverse
Denominator Restriction
Complementary Counting
Combinatorial Counting (P&C Integration)
Exponent of Prime in n!
Greatest Binomial Coefficient
Complementary Counting
Combinatorial Probability
Linear Independence vs Dependence
Logarithmic Integration Modulus
Complementary Counting
De Morgan's Laws in Probability
Domain and Range Nuance
Complementary Counting
Distance from point to plane
Integrating Factor (IF) Sign Sensitivity
Point of Inflection Conditions
Complementary Counting
Division into Groups
Step Function Discontinuities
Complementary Counting
Exhaustive Events Concept
Geometry Constraint Modeling
Critical Point vs Stationary Point
Complementary Counting
Independent vs Disjoint Events
Minors and Cofactors Sign Convention
Row/Column Exchange Parity
Complementary Counting
Random Variable Expectation
Second Derivative Test Limitations
Complementary Counting
Sample Space Partitioning
Nilpotent Matrix Logic
Complementary Counting
String Method (Adjacency)
Geometrical Interpretation of Modulus
Conditional Probability Foundations
Arbitrary Constants vs Parameters
Variable Separable Constraints
Conditional Probability Foundations
Bayes' Theorem Reversal
Modulus Function Symmetry
Conditional Probability Foundations
Binomial Distribution Tail-Ends
Properties of Binomial Coefficients
Conditional Probability Foundations
Combinatorial Counting (P&C Integration)
Cardinality Inclusion-Exclusion
Conditional Probability Foundations
Complementary Counting
Cardinality Inclusion-Exclusion
Conditional Probability Foundations
Geometry Constraint Modeling
Distance from point to plane
Unit Vector Normalization
Conditional Probability Foundations
Independent vs Disjoint Events
Matrix Multiplication Non-Commutativity
Conditional Probability Foundations
Orthogonal projection of a line
Unit Vector Normalization
Conditional Probability Foundations
Singular vs. Non-Singular Threshold
Greatest Integer Function (GIF)
Conditional Probability Foundations
The 1^∞ Form Shortcut
Wavy Curve (Method of Intervals)
Multiplication Theorem
Coefficient of x^r in Expansion
The Empty Set Trap
Rational Function Asymptotes
Multiplication Theorem
Complementary Counting
Skew-Symmetric Determinant
Differentiation of Determinants
Multiplication Theorem
Determinant as Geometric Area/Volume
Parametric Derivative Endpoint Trap
Domain Restrictions
Multiplication Theorem
Leibniz Rule for Differentiation under Integral
Second Order Derivative Continuity
The Sign-Consistency Principle
Multiplication Theorem
Linear Independence vs Dependence
Linear Dependency of Rows
Multiplication Theorem
Random Variable Mapping
Differentiability vs Continuity
Multiplication Theorem
Sampling With/Without Replacement
Binomial Distribution Tail-Ends
Multiplication Theorem
Vandermonde's Identity
Binomial Coefficient Summation Properties
De Morgan’s Laws in Probability
Bijectivity and Inverse
Function Definition (The 'One-to-One' Constraint)
De Morgan’s Laws in Probability
Conditional Probability Foundations
Logarithmic Integration Modulus
De Morgan’s Laws in Probability
Coplanarity Condition
Collinearity vs Coplanarity
De Morgan’s Laws in Probability
Determinant Scaling Property
Adjoint-Determinant Relation
De Morgan’s Laws in Probability
Even and Odd Function Symmetry
Symmetry Utilization
De Morgan’s Laws in Probability
Global Extrema on Intervals
Second Derivative Test Limitations
De Morgan’s Laws in Probability
String Method (Adjacency)
Modulus Inequality Logic
De Morgan’s Laws in Probability
Surjective Mapping (Onto)
Arbitrary Constants vs Parameters
Inclusion-Exclusion Principle
Independent vs Disjoint Events
Mutually Exclusive vs Independent
Inclusion-Exclusion Principle
Injective Mapping (One-One)
Equivalence Class Partitioning
Inclusion-Exclusion Principle
Multinomial Theorem
Properties of Binomial Coefficients
Inclusion-Exclusion Principle
Restricted Selection (At least one common)
Division into Groups
Inclusion-Exclusion Principle
Row/Column Exchange Parity
Transitivity of Identity
Exhaustive Events Concept
Differentiation of Determinants
Matrix Multiplication Non-Commutativity
Exhaustive Events Concept
Linear Independence vs Dependence
Dot Product Projection Sign
Exhaustive Events Concept
Newton-Leibniz Formula with variable limits
Trigonometric Standard Limits
Exhaustive Events Concept
Sigma Notation Properties
Modulus Function Breakdown
Exhaustive Events Concept
String Method (Adjacency)
Integral Part and Fractional Part
Random Variable Mapping
Coefficient of x^r in Expansion
Step Function Discontinuities
Random Variable Mapping
Definite Integral Properties (King's Property)
Limit Existence at Discontinuity
Random Variable Mapping
Determinant Product Rule
Row/Column Exchange Parity
Random Variable Mapping
Equiprobable Outcomes
Modulus Sign Dependency
Symmetry and Invariance
Properties of Binomial Coefficients
The Modulus Trap
Symmetry and Invariance
Random Variable Expectation
Mean Value Theorem Applicability
Symmetry and Invariance
Roots of Unity Properties
Argument and Principal Value
Symmetry and Invariance
Symmetric and Skew-Symmetric Decomposition
Triangular/Diagonal Matrix Shortcut
Indicator Function Logic
Dot Product Projection Sign
Modulus Sign Dependency
Indicator Function Logic
Logarithmic Integration Modulus
System of Inequalities
Indicator Function Logic
Sigma Notation Properties
Integral Part and Fractional Part
Indicator Function Logic
String Method (Adjacency)
Symmetry vs. Antisymmetry

Exam Coverage

Understand how this chapter is tested across major competitive examinations.

JEE Advanced

India · Competitive Exam · Undergraduate

JEE Main

India · Competitive Exam · Undergraduate

BITSAT

India · Competitive Exam · Undergraduate

IISER Aptitude Test (IAT)

India · Competitive Exam · Undergraduate

NEST

India · Competitive Exam · Undergraduate

ISI Admission Test

India · Competitive Exam · Undergraduate

CMI Entrance

India · Competitive Exam · Undergraduate

IOQM / INMO

India · Olympiad · High School

SAT Math

United States · Curriculum · High School

A-Level Mathematics

United Kingdom · Curriculum · Advanced Level

A-Level Further Mathematics

United Kingdom · Curriculum · Advanced Level

IB Mathematics AA HL

International · Curriculum · IB Diploma

Singapore A-Level Mathematics

Singapore · Curriculum · Advanced Level

Cambridge International A-Level Mathematics

International · Curriculum · Cambridge

International Mathematical Olympiad (IMO)

International · Olympiad · International

Practice Questions

Strengthen your understanding by solving carefully selected questions from this chapter.

50 Questions
1

Let S={1,2,3,4,5,6,7,8}S = \{1, 2, 3, 4, 5, 6, 7, 8\} and A=(1,6)∩ZA = (1, 6) \cap \mathbb{Z}. Find the total number of bijective functions f:S→Sf: S \to S satisfying the following two conditions:

  1. f(x)≠xf(x) \neq x for all x∈Sx \in S.
  2. The image set f(A)f(A) is closed under the binary operation a⊕b=∣a−b∣+1a \oplus b = |a - b| + 1.
2

Let XX be a continuous random variable with probability density function f(x)=k(x2+∣x∣)f(x) = k(x^2 + |x|) for x∈[−3,3]x \in [-3, 3] and f(x)=0f(x) = 0 otherwise. Let EE be the event that the random variable XX satisfies the inequality: ∫−XX(t3cos⁡t+t2sin⁡3t+∣t∣−1)dt>0\int_{-X}^{X} \left( t^3 \cos t + t^2 \sin^3 t + |t| - 1 \right) dt > 0 Find the probability of event EE, P(E)P(E).

3

Let f(x)=(sin⁡2x)sin⁡2xf(x) = (\sin 2x)^{\sin 2x} be defined on its maximal domain D⊂(0,2π)D \subset (0, 2\pi) where the base is strictly positive. Let A={x∈D:f′(x)=0}A = \{x \in D : f'(x) = 0\} and B={x∈(0,2π)∖D:x is a limit point of D and lim⁡t→x,t∈Df(t) exists finitely}B = \{x \in (0, 2\pi) \setminus D : x \text{ is a limit point of } D \text{ and } \lim_{t \to x, t \in D} f(t) \text{ exists finitely}\}. A sample space is defined as the set of discrete points S=A∪BS = A \cup B. If three distinct elements are chosen at random from SS, what is the probability that exactly one of them belongs to AA?

4

Let SS be the set of all ordered triplets (x,y,z)(x,y,z) of non-negative integers such that x+y+z≤20x+y+z \le 20. The sample space Ω\Omega is defined as the subset of SS for which the expression 1x−y\frac{1}{x-y} is a well-defined real number. If a triplet is chosen uniformly at random from Ω\Omega, what is the probability that z>x+yz > x + y?

5

In a geometric probability model, the probability of an event EE is given by the formula P(E)=110[u⃗ v⃗ w⃗]P(E) = \frac{1}{10} [\vec{u}\ \vec{v}\ \vec{w}], where [u⃗ v⃗ w⃗][\vec{u}\ \vec{v}\ \vec{w}] denotes the scalar triple product u⃗⋅(v⃗×w⃗)\vec{u} \cdot (\vec{v} \times \vec{w}). Let u⃗=xi^+3j^−k^\vec{u} = x\hat{i} + 3\hat{j} - \hat{k}, v⃗=i^−2j^+k^\vec{v} = \hat{i} - 2\hat{j} + \hat{k}, and w⃗=2i^+xj^+2k^\vec{w} = 2\hat{i} + x\hat{j} + 2\hat{k}. If xx is an integer, what is the sum of all possible values of xx for which P(E)P(E) satisfies the axioms of probability?

6

A continuous random variable XX takes values in the interval [0,π][0, \pi]. Its probability density function is given by f(x)=K(F′′(x)+F(x))f(x) = K(F''(x) + F(x)), where KK is a positive constant and F(x)=∫0xsin⁡(x−t)arccos⁡(cos⁡2t)dtF(x) = \int_{0}^{x} \sin(x-t) \arccos(\cos 2t) dt What is the variance of XX?

7

Let AA and BB be two events associated with a random experiment. The probability of event AA is given by P(A)=det⁡(M)P(A) = \det(M), where MM is the 3×33 \times 3 matrix: M=(∫−π/4π/4ln⁡(1−sin⁡x1+sin⁡x)dxα2+1β−(α2+1)∑k=159(cos⁡kπ2−cos⁡−kπ2)γ−β−γlim⁡x→0ex2−1−x2x4−12)M = \begin{pmatrix} \int_{-\pi/4}^{\pi/4} \ln\left(\frac{1-\sin x}{1+\sin x}\right) dx & \alpha^2+1 & \beta \\ -(\alpha^2+1) & \sum_{k=1}^{59} \left( \cos\frac{k\pi}{2} - \cos\frac{-k\pi}{2} \right) & \gamma \\ -\beta & -\gamma & \lim_{x \to 0} \frac{e^{x^2} - 1 - x^2}{x^4} - \frac{1}{2} \end{pmatrix} for some real constants α,β,γ\alpha, \beta, \gamma. Event BB is defined as the event that a number tt chosen at random from the sample space S={t∈Z:−59≤t≤283}S = \{t \in \mathbb{Z} : -59 \le t \le 283\} satisfies the inequality: (t−59)2(t−283)<0(t-59)^2 (t-283) < 0 Find the value of 343×P(Ac∩B)343 \times P(A^c \cap B).

8

Let M=[1−1−1021001]M = \begin{bmatrix} 1 & -1 & -1 \\ 0 & 2 & 1 \\ 0 & 0 & 1 \end{bmatrix}. Let Ω\Omega be the region in R3\mathbb{R}^3 defined by the system of inequalities: x≥0,y≥0,z≥0x \ge 0, y \ge 0, z \ge 0, x+y+z≤6x + y + z \le 6, and 2x+y+z≤132x + y + z \le 13. A point v=[xyz]\mathbf{v} = \begin{bmatrix} x \\ y \\ z \end{bmatrix} is chosen uniformly at random from Ω\Omega. Let EE be the event that the vector u=adj(M)v\mathbf{u} = \text{adj}(M) \mathbf{v} also lies in Ω\Omega. Find the probability of the event EE.

9

A biased coin with probability of showing heads p∈(0,1)p \in (0, 1) is tossed 66 times independently. Let AA be the event that the first toss shows heads, and SS be the event that the total number of heads obtained in the 66 tosses is either 22 or 33. If AA and SS are independent events, then the value of pp is:

10

Let A,B,CA, B, C be three events in a probability space such that AA and CC are mutually exclusive. The events AA and BB are independent, and BB and CC are independent. Given that P(A∪B)=34P(A \cup B) = \frac{3}{4}, P(B∪C)=58P(B \cup C) = \frac{5}{8}, and P(A∪C)=34P(A \cup C) = \frac{3}{4}. Let p1p_1 be the probability that exactly one of the events A,B,CA, B, C occurs, and p2p_2 be the probability that exactly two of the events A,B,CA, B, C occur. The value of p1p2\frac{p_1}{p_2} is:

11

Let f(x) = \\begin{cases} ax + b & \\text{if } x \\ge 0 \\\\ cx^2 + d & \\text{if } x < 0 \\end{cases} and g(x)=x2−1g(x) = x^2 - 1. A 4-tuple (a,b,c,d)(a, b, c, d) is chosen uniformly at random from the set S=−2,−1,0,1,24S = \\{-2, -1, 0, 1, 2\\}^4. Let AA be the event that the composite function h(x)=f(g(x))h(x) = f(g(x)) is continuous at x=1x = 1, and BB be the event that h(x)h(x) is differentiable at x=1x = 1. Which of the following statements is correct?

12

Let SS be the set of all 3×33 \times 3 diagonal matrices whose principal diagonal elements are chosen from the set {0,1}\{0, 1\}. Two matrices AA and BB are chosen randomly and independently from SS with replacement. Let EE be the event that det⁡(2ATB)=0\det(2A^T B) = 0 and FF be the event that det⁡(A−B)=0\det(A - B) = 0. The probability P(E∩F)P(E \cap F) is equal to

13

Let SS be a set containing 5 distinct elements. Let AA be the family of all subsets of SS having an even number of elements, and BB be the family of all subsets of SS having an odd number of elements. A random sample of 16 subsets is chosen without replacement from the power set of SS. Let EE be the event that the sample contains an even number of subsets from AA, and FF be the event that the sample contains an even number of subsets from BB. What is the probability of E∪FE \cup F?

14

Four identical balls are distributed randomly into three distinct boxes, such that each ball is independently placed into any of the boxes with equal probability. Let P0P_0 be the probability that the first box is empty, and P2P_2 be the probability that the first box contains exactly two balls. Find the sum of all integers x∈[1,10]x \in [1, 10] that satisfy the inequality: x2−11x+28P0−P2≥0\frac{x^2 - 11x + 28}{P_0 - P_2} \ge 0

15

Urn U1U_1 contains x−2x-2 red balls and 55 black balls. Urn U2U_2 contains x−2x-2 red balls and 11 black ball, where x≥2x \ge 2 is an integer. An urn is selected by rolling a fair standard die. If the die shows a number less than 3, Urn U1U_1 is chosen; otherwise, Urn U2U_2 is chosen. A ball is drawn at random from the selected urn and is found to be red. Given that the drawn ball is red, the probability that it came from Urn U1U_1 is 310\frac{3}{10}. Find the sum of all possible valid values of xx.

16

Let a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} be three non-coplanar vectors such that the volume of the parallelepiped formed by them is 325325. A point PP with position vector r⃗=xa⃗+yb⃗+zc⃗\vec{r} = x\vec{a} + y\vec{b} + z\vec{c} is chosen uniformly at random inside this parallelepiped. What is the volume of the region consisting of all such points PP for which line segments of lengths x,y,x, y, and zz can form an acute-angled triangle?

17

A discrete random variable XX takes values in the set S={−3,−2,−1,0,1,2,3}S = \{-3, -2, -1, 0, 1, 2, 3\}. The probability mass function of XX is given by P(X=k)=C(5−∣k∣+λ⋅sgn(k3−k))P(X=k) = C \left( 5 - |k| + \lambda \cdot \text{sgn}(k^3 - k) \right) where CC and λ\lambda are real constants, and sgn(⋅)\text{sgn}(\cdot) denotes the signum function. If the expected value of XX is E(X)=2023E(X) = \frac{20}{23}, then the probability P(X≥1)P(X \ge 1) is equal to

18

Let WW be the 80th80^{\text{th}} word in the dictionary of all unique permutations of the letters A, A, B, C, D, E\text{A, A, B, C, D, E}. A fair 6-sided die has its faces marked with the letters A, A, B, C, D, E\text{A, A, B, C, D, E}. A 6-letter word XX is generated by rolling this die 6 times and recording the letters in the order they appear. What is the probability that XX is a permutation of the letters of WW, and has the same starting letter and the same ending letter as WW?

19

Let A={1,2,3,4,5,6}A = \{1, 2, 3, 4, 5, 6\} and B={x∈N:x≤5 and x−3x2−5x+4∈R}B = \left\{ x \in \mathbb{N} : x \le 5 \text{ and } \frac{x-3}{x^2-5x+4} \in \mathbb{R} \right\}. Find the number of surjective functions f:A→Bf: A \to B such that f(1)+f(2)=7f(1) + f(2) = 7.

20

Let S={z∈C:z12=1}S = \{z \in \mathbb{C} : z^{12} = 1\}. Find the number of matrices A=(abcd)A = \begin{pmatrix} a & b \\ c & d \end{pmatrix} with entries from SS such that AAˉ≠AˉAA \bar{A} \neq \bar{A} A, where Aˉ\bar{A} is the complex conjugate of AA.

21

A light ray travels along the line L:x3=y4=z12L: \frac{x}{3} = \frac{y}{4} = \frac{z}{12} and intersects a series of parallel glass plates. The surfaces of the plates lie on the planes Πk:3x+4y=25k\Pi_k: 3x + 4y = 25k for k=1,2,…,40k = 1, 2, \dots, 40. Let the intersection points of the ray with these planes be P1,P2,…,P40P_1, P_2, \dots, P_{40} respectively. A subset of 7 points is to be chosen from these 40 points such that the spatial distance between the orthogonal projections of any two chosen points on the plane Π:3x+4y=0\Pi: 3x + 4y = 0 is at least 39. Find the number of ways to choose these 7 points.

22

Let AA be a 4×44 \times 4 matrix defined as A=(x1x1x1x1x1x2x2x2x1x2x3x3x1x2x3x4)A = \begin{pmatrix} x_1 & x_1 & x_1 & x_1 \\ x_1 & x_2 & x_2 & x_2 \\ x_1 & x_2 & x_3 & x_3 \\ x_1 & x_2 & x_3 & x_4 \end{pmatrix} where x1,x2,x3,x4∈Zx_1, x_2, x_3, x_4 \in \mathbb{Z}. Find the number of distinct quadruplets (x1,x2,x3,x4)(x_1, x_2, x_3, x_4) such that ∣det⁡(A)∣=293|\det(A)| = 293.

23

Let f(x)={x3−3x+k−2for −2≤x<1ax2+bx+cfor 1≤x≤3f(x) = \begin{cases} x^3 - 3x + k - 2 & \text{for } -2 \le x < 1 \\ ax^2 + bx + c & \text{for } 1 \le x \le 3 \end{cases} be a function continuous and differentiable on (−2,3)(-2, 3), where a,b,ca, b, c are real constants and f(3)=kf(3) = k. Let SS be the set of all integers kk such that the equation f(x)=0f(x) = 0 has exactly 3 distinct real roots. For each k∈Sk \in S, a bag BkB_k is prepared containing NkN_k red balls and 15−Nk15 - N_k black balls, where NkN_k is the number of non-negative integer solutions to the equation x+y+z=kx + y + z = k. A bag is selected at random (all bags in SS are equally likely to be chosen), and two balls are drawn from it without replacement. If both balls are red, let PP be the probability that they were drawn from bag B3B_3. Find the value of PP.

24

Let S={z∈C:∣z∣≤5 and Re(z),Im(z)∈Z}S = \{z \in \mathbb{C} : |z| \le \sqrt{5} \text{ and } \text{Re}(z), \text{Im}(z) \in \mathbb{Z}\}. Let V={z∈S:Re(z)⋅Im(z)=0}V = \{z \in S : \text{Re}(z) \cdot \text{Im}(z) = 0\} and C=S∖VC = S \setminus V. If all elements of SS are arranged randomly in a row to form a sequence, what is the probability that at least two elements of VV are adjacent?

25

Let A,B,A, B, and CC be three independent events. A matrix MM is defined as: M=[1P(A)P(B)P(C)11P(B)P(C)1P(A)1P(C)1P(A)P(B)1]M = \begin{bmatrix} 1 & P(A) & P(B) & P(C) \\ 1 & 1 & P(B) & P(C) \\ 1 & P(A) & 1 & P(C) \\ 1 & P(A) & P(B) & 1 \end{bmatrix} Let NN be the matrix obtained by swapping the 2nd and 3rd columns of MM. If 3det⁡(N)+5P(A′∩B′∩C′)=1293\det(N) + 5P(A' \cap B' \cap C') = \frac{1}{29}, find the probability that at least one of the events A,B,A, B, or CC occurs.

26

Let Ω\Omega be the region in the first octant bounded by the coordinate planes and the tangent plane to the surface xyz=8xyz = 8 at the point on the surface closest to the origin. A point (x,y,z)(x,y,z) is chosen uniformly at random from Ω\Omega. Let f(t)=∣t−x∣+∣t−y∣+∣t−z∣f(t) = |t-x| + |t-y| + |t-z|. If EE is the event that the absolute minimum value of f(t)f(t) is at most 44, find the probability of EE.

27

Let NN be the exponent of the highest power of 33 dividing 100!100!, and kk be the exponent of the highest power of 55 dividing 100!100!. A subset AA is chosen at random from the power set of S={1,2,…,N}S = \{1, 2, \dots, N\}. The probability that the number of elements in AA is strictly greater than kk is pp. Then pp is equal to:

28

Let y=f(x)y = f(x) be the continuous solution to the differential equation dydx+∣x−2∣y=∣x−2∣\frac{dy}{dx} + |x-2| y = |x-2| satisfying f(2)=2f(2) = 2. Let kk be the sum of the xx-coordinates of all points of inflection of the curve y=f(x)y = f(x). Three fair six-sided dice are rolled, and their outcomes are used as the coordinates of a point P(a,b,c)P(a,b,c) in 3D space. Let Π\Pi be the plane 2x+2y+z=3k2x + 2y + z = 3k. Find the probability that the perpendicular distance from PP to the plane Π\Pi is strictly greater than 1.

29

Let S={1,2,3,…,20}S = \{1, 2, 3, \dots, 20\}. A number nn is chosen at random from SS. Three bags B1,B2,B_1, B_2, and B3B_3 are available. Bag B1B_1 is selected if ∣n−8∣+∣n−14∣=6|n - 8| + |n - 14| = 6. Bag B2B_2 is selected if ∣n−8∣+∣n−14∣>6|n - 8| + |n - 14| > 6 and ∣n−11∣≤5|n - 11| \le 5. Bag B3B_3 is selected if ∣n−11∣>5|n - 11| > 5. The contents of the bags are as follows: B1B_1 contains 2 Red and 3 Black balls, B2B_2 contains 3 Red and 2 Black balls, and B3B_3 contains 4 Red and 1 Black ball. A ball is drawn at random from the selected bag and is found to be Red. What is the probability that the chosen number nn is a prime number?

30

Six distinct balls are distributed randomly into four distinct boxes. Let EE be the event that no box is empty, and FF be the event that Box 1 and Box 2 contain an equal number of balls. The conditional probability P(F∣E)P(F|E) is:

31

A system has two states, S1S_1 and S2S_2. It can undergo two types of transition protocols, XX and YY. Under protocol XX, a system in S1S_1 transitions to S2S_2 with probability 12\frac{1}{2} and remains in S1S_1 with probability 12\frac{1}{2}. A system in S2S_2 remains in S2S_2 with probability 11. Under protocol YY, a system in S1S_1 remains in S1S_1 with probability 11. A system in S2S_2 transitions to S1S_1 with probability 12\frac{1}{2} and remains in S2S_2 with probability 12\frac{1}{2}. A fair coin is tossed. If it shows Heads, the system undergoes the sequence of protocols X,Y,X,YX, Y, X, Y in that order. If it shows Tails, the system undergoes the sequence X,X,Y,YX, X, Y, Y in that order. Given that the system initially starts in state S1S_1 and is found to be in state S1S_1 after the four protocols are completed, what is the probability that the coin showed Heads?

32

A unit vector u^\hat{u} is chosen uniformly at random in R3\mathbb{R}^3. Let π\pi be a plane passing through the origin with a fixed normal vector n^\hat{n}. Let v⃗\vec{v} be the orthogonal projection of u^\hat{u} onto the plane π\pi. Consider the triangle TT whose vertices are the origin OO, and the points UU and VV such that OU⃗=u^\vec{OU} = \hat{u} and OV⃗=v⃗\vec{OV} = \vec{v}. Let E1E_1 be the event that the area of triangle TT is at most 38\frac{\sqrt{3}}{8}, and E2E_2 be the event that ∣v⃗∣≤12|\vec{v}| \le \frac{1}{2}. Find the conditional probability P(E2∣E1)P(E_2 \mid E_1).

33

A real number xx is chosen uniformly at random from the interval [−4,4][-4, 4]. Let AA be the event that xx satisfies the inequality x3−2x2−4x+8x+1≥0\frac{x^3 - 2x^2 - 4x + 8}{x+1} \ge 0. Let BB be the event that lim⁡n→∞(n2+3xn+1n2+xn+1)n≥e2\lim_{n \to \infty} \left( \frac{n^2 + 3xn + 1}{n^2 + xn + 1} \right)^n \ge e^2. Find the conditional probability P(B∣A)P(B \mid A).

34

A system undergoes three sequential state transitions from state 0 to state 3. For a parameter t∈(0,1)t \in (0, 1), the probability of transitioning from state 0 to state 1 is tt, from state 1 to state 2 is t\sqrt{t}, and from state 2 to state 3 is t3/2t^{3/2}. Let P(t)P(t) be the probability that the system successfully reaches state 3 from state 0.

Three points in 3D space have position vectors: a⃗=(cos⁡−1t)i^+tj^\vec{a} = (\cos^{-1}t) \hat{i} + t \hat{j} b⃗=(cos⁡−1t)j^+k^\vec{b} = (\cos^{-1}t) \hat{j} + \hat{k} c⃗=i^+tk^\vec{c} = \hat{i} + t \hat{k} Let V(t)V(t) be the volume of the tetrahedron formed by the origin and the points with position vectors a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c}.

Find the value of lim⁡t→1−dVdP\lim_{t \to 1^-} \frac{dV}{dP}.

35

Let SS be the set of all 3×33 \times 3 matrices of the form B=(ab0cd0001)B = \begin{pmatrix} a & b & 0 \\ c & d & 0 \\ 0 & 0 & 1 \end{pmatrix}, where a,b,c,d∈{1,2,3,4,5,6}a, b, c, d \in \{1, 2, 3, 4, 5, 6\}. A matrix BB is chosen at random from SS. Let PP be the probability that BB is non-singular.

Consider the matrix A(x)=(0x2−xsin⁡(πx)x−x20ex−1−1−sin⁡(πx)1−ex−10)A(x) = \begin{pmatrix} 0 & x^2 - x & \sin(\pi x) \\ x - x^2 & 0 & e^{x-1} - 1 \\ -\sin(\pi x) & 1 - e^{x-1} & 0 \end{pmatrix} and let f(x)=det⁡(BA(x)BT)f(x) = \det(B A(x) B^T).

Find the value of 648P+f′(2024)648 P + f'(2024).

36

Let XX and YY be independent continuous random variables, each having the probability density function f(x)f(x) defined on the interval [0,2][0, 2] by: f(x)=d2dx2(∫0x(x−t)22∣t−1∣dt)f(x) = \frac{d^2}{dx^2} \left( \int_{0}^{x} \frac{(x - t)^2}{2} |t - 1| dt \right) Let E1E_1 be the event that the quadratic equation z2−2Xz+X=0z^2 - 2Xz + X = 0 has real roots, and E2E_2 be the event that the quadratic equation z2−2Yz+Y=0z^2 - 2Yz + Y = 0 has real roots. Find the probability of the event E1∪E2E_1 \cup E_2.

37

Let LL be the horizontal asymptote of the curve y=3000t3+2t2−7t3−t+4y = \frac{3000t^3 + 2t^2 - 7}{t^3 - t + 4} as t→∞t \to \infty. Two boxes contain mathematical expressions. Box I contains the expression (x2+1x)15\left(x^2 + \frac{1}{x}\right)^{15} and Box II contains the expression (x3−2x2)10\left(x^3 - \frac{2}{x^2}\right)^{10}. A fair coin is tossed. If it shows heads, Box I is selected; if tails, Box II is selected. Then, an integer rr is chosen at random from the set S={0,1,2,…,30}S = \{0, 1, 2, \dots, 30\}. What is the probability that the coefficient of xrx^r in the expression of the selected box is strictly greater than LL?

38

Let A,B,A, B, and CC be three events in a sample space such that P(A)=12P(A) = \frac{1}{2}, P(B)=13P(B) = \frac{1}{3}, P(C)=14P(C) = \frac{1}{4}, P(A∩B)=14P(A \cap B) = \frac{1}{4}, P(B∩C)=16P(B \cap C) = \frac{1}{6}, P(C∩A)=18P(C \cap A) = \frac{1}{8}, and P(A∩B∩C)=112P(A \cap B \cap C) = \frac{1}{12}. Let MM be a 3×33 \times 3 matrix such that its determinant is given by ∣M∣=P(A‾∩B‾∩C‾)|M| = P(\overline{A} \cap \overline{B} \cap \overline{C}). If N=adj(2M)N = \text{adj}(2M), find the value of 584∣N∣584 \sqrt{|N|}.

39

A real number xx is chosen uniformly at random from the interval [−1,5][-1, 5]. Let f(x)=∣x2−4x+3∣f(x) = |x^2 - 4x + 3|. Let E1E_1 be the event that f(x)≥3f(x) \ge 3 and E2E_2 be the event that f(x)≤2425f(x) \le \frac{24}{25}. Find the probability of the event E1′∩E2′E_1' \cap E_2'.

40

Let SS be a set of 10 points in 3D space consisting of the 4 vertices and the 6 edge midpoints of a regular tetrahedron. Four points are chosen at random from SS. Let AA be the event that the 4 chosen points are coplanar, and BB be the event that no three of the chosen points are collinear. Find the probability of the event Ac∪BcA^c \cup B^c.

41

Let XX be a continuous random variable with the probability density function: f(x)={12(1+sin⁡(πx2)),x∈[−1,1]0,otherwisef(x) = \begin{cases} \frac{1}{2} \left(1 + \sin\left(\frac{\pi x}{2}\right)\right), & x \in [-1, 1] \\ 0, & \text{otherwise} \end{cases} Let AA and BB be events defined by A={X>0}A = \{X > 0\} and B={X<−1/3}B = \{X < -1/3\}. Calculate the probability P(Ac∩Bc)P(A^c \cap B^c).

42

Let XX be a continuous random variable with probability density function f(x)={ax2+bx+cfor x∈[−1,1]0otherwisef(x) = \begin{cases} a x^2 + b x + c & \text{for } x \in [-1, 1] \\ 0 & \text{otherwise} \end{cases}, where a,b,ca, b, c are real constants. What is the maximum possible value of the expected value E[X]E[X]?

43

Let Ω={1,2,3,4,5,6,7}\Omega = \{1, 2, 3, 4, 5, 6, 7\} be a sample space equipped with a valid probability measure PP. Consider the events E1={1,2,3,4}E_1 = \{1, 2, 3, 4\}, E2={3,4,5,6}E_2 = \{3, 4, 5, 6\}, and E3={1,2,5,6}E_3 = \{1, 2, 5, 6\}. It is given that E1,E2E_1, E_2, and E3E_3 are pairwise independent events and P(E1)=P(E2)=P(E3)=p>0P(E_1) = P(E_2) = P(E_3) = p > 0. What is the value of P({7})P(\{7\})?

44

Let P=[p1p2p3]P = \begin{bmatrix} p_1 \\ p_2 \\ p_3 \end{bmatrix} be a probability vector such that pi≥0p_i \ge 0 for i=1,2,3i=1,2,3 and ∑i=13pi=1\sum_{i=1}^3 p_i = 1. Consider the skew-symmetric matrix A=[02−3−2043−40]A = \begin{bmatrix} 0 & 2 & -3 \\ -2 & 0 & 4 \\ 3 & -4 & 0 \end{bmatrix} and the column vector B=[−12−1]B = \begin{bmatrix} -1 \\ 2 \\ -1 \end{bmatrix}. If PP satisfies the system of linear equations AP=BAP = B, then the value of 81(p12+p22+p32)81(p_1^2 + p_2^2 + p_3^2) is:

45

Three independent trials are performed. In each trial, a fair die is rolled. If the die shows a multiple of 33, a success is recorded. Let x,y,z∈{1,−1}x, y, z \in \{1, -1\} such that the variable takes the value 11 if the corresponding trial (1st, 2nd, 3rd) is a success, and −1-1 if it is a failure. Consider the vectors u⃗=i^+j^\vec{u} = \hat{i} + \hat{j}, v⃗=xi^+yj^+zk^\vec{v} = x\hat{i} + y\hat{j} + z\hat{k}, and w⃗=i^+k^\vec{w} = \hat{i} + \hat{k}. Let AA be the event that the volume of the parallelepiped formed by u⃗,v⃗,w⃗\vec{u}, \vec{v}, \vec{w} is exactly 11. Given that event AA has occurred, what is the probability that the acute angle between the planes containing the pairs of vectors (u⃗,v⃗)(\vec{u}, \vec{v}) and (v⃗,w⃗)(\vec{v}, \vec{w}) is π6\frac{\pi}{6}?

46

Four married couples (8 distinct people) are to be seated around a circular table. Let XX be the number of arrangements in 8 distinct, numbered chairs such that no couple sits diametrically opposite to each other. Let YY be the number of arrangements in 8 identical, unnumbered chairs (where arrangements are identical if they differ only by rotation) such that exactly one couple sits diametrically opposite to each other. Find the value of XY\frac{X}{Y}.

47

An experiment has a countably infinite sample space S={E1,E2,E3,… }S = \{E_1, E_2, E_3, \dots\}. The probability of each elementary event EnE_n is given by P(En)=α2n+β⋅3n(3n−1)(3n+1−1)P(E_n) = \frac{\alpha}{2^n} + \frac{\beta \cdot 3^n}{(3^n - 1)(3^{n+1} - 1)} where α\alpha and β\beta are positive constants. If lim⁡n→∞2nP(En)=14\lim_{n \to \infty} 2^n P(E_n) = \frac{1}{4}, then the probability of the event E2E_2 is:

48

A real number xx is chosen uniformly at random from the interval [0,3][0, 3]. Let E1E_1 be the event that [x2]−3[x]+2≤0[x^2] - 3[x] + 2 \le 0, and E2E_2 be the event that x2−3x+2≤0x^2 - 3x + 2 \le 0, where [⋅][\cdot] denotes the greatest integer function. The probability of the event E1∪E2E_1 \cup E_2 is:

49

Let SS be the sample space of all positive integer solutions (x,y,z)(x, y, z) to the equation x+y+z=50x + y + z = 50. A solution (x,y,z)(x, y, z) is chosen at random from SS. Let E1E_1 be the event that x,y,zx, y, z can form the side lengths of a triangle (including degenerate triangles where the vertices are collinear). Let E2E_2 be the event that x,y,zx, y, z can form the side lengths of a non-degenerate triangle (with positive area). Find the conditional probability P(E2∣E1)P(E_2 \mid E_1).

50

Let XX be a random variable following the binomial distribution B(100,12)B\left(100, \frac{1}{2}\right). Evaluate the sum: S=∑k=51100(k2−100k+2499)2(100k)(12)100S = \sum_{k=51}^{100} (k^2 - 100k + 2499)^2 \binom{100}{k} \left(\frac{1}{2}\right)^{100}

Related Chapters

Explore other chapters from the same academic theme.

Ready to Master This Chapter?

Practice 50 carefully selected questions designed to strengthen your concepts, avoid common traps, and build confidence for competitive examinations.

Start Free Practice