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Mathematics • Class 12

Probability - Class 12

Master Probability — Advanced 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 covers advanced topics in probability, including conditional probability, Bayes' theorem, random variables, probability distributions, and binomial distributions. It explores the practical applications of probability in various real-life scenarios.

Why This Chapter Matters

  • Probability is a crucial mathematical tool used in science, engineering, economics, and everyday life. Understanding probability helps make informed decisions, analyze risks, and predict outcomes in uncertain situations.

Real-Life Applications

  • Analyzing risks and uncertainties in insurance and financial markets.
  • Solving problems related to genetics and population studies in biology.
  • Predicting outcomes in sports, weather forecasts, and artificial intelligence models.

Skills You Will Learn

  • Understanding conditional probability and Bayes' theorem.
  • Working with random variables and probability distributions.
  • Applying probability concepts to solve real-world problems.

Core Concepts to Master

Sample Space Partitioning

Total probability theorem requires mutually exclusive and exhaustive events; ignoring the 'exhaustive' condition leads to invalid results.

Conditional Probability Constraints

P(A|B) is only defined when P(B) > 0; students often fail to verify this constraint in complex problems.

Independent vs Disjoint Events

Confusion between P(A∩B)=P(A)P(B) and P(A∩B)=0; they are mathematically distinct and never imply each other.

Bayes' Theorem Reversal

Identifying the 'cause' versus 'effect' in inverse probability; mapping the correct prior and posterior distributions.

Bernoulli Trials Logic

Fixed probability of success and independence are mandatory; applying binomial distribution to dependent events is a fatal error.

Binomial Distribution Tail-Ends

Calculating 'at least one' via 1 - P(none) is efficient; manual summation of multiple terms invites calculation traps.

Random Variable Expectation

E(X) is the probability-weighted average; confusion arises when X is not a simple numerical count.

Sampling With/Without Replacement

Replacement implies independence (Binomial); non-replacement implies dependence (Hypergeometric/Combinatorics) which changes probability per trial.

Probability Mass Function (PMF)

Sum of probabilities must equal one; check if the defined range of X covers all possible sample space outcomes.

Complementary Counting

Using 'at most' or 'at least' constraints; failing to identify the full complement set is a common logical oversight.

Combinatorial Probability

Using nCr/nPr correctly; students often mistake 'ordered' versus 'unordered' selection in marbles/cards problems.

Equiprobable Outcomes

Classical definition requires equally likely events; applying it to biased dice or weighted sets results in total failure.

De Morgan's Laws in Probability

Transforming P(A' ∩ B') into 1 - P(A ∪ B) is crucial for simplifying complex intersection-based Venn diagrams.

Variance of Discrete Variable

Calculated as E(X²) - [E(X)]²; students frequently forget to square the expectation or miscalculate the variance.

Multiplication Principle Scope

Distinguishing between independent sequential events and conditional dependence; the logic pivot is whether the first action alters the second.

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 Partitioning
Bernoulli Trials Logic
Multinomial Theorem
Sample Space Partitioning
Condition for Common Roots
Quadratic Equations with Complex Coefficients
Sample Space Partitioning
Global Extrema on Intervals
Non-Differentiable Extrema
Sample Space Partitioning
Increasing/Decreasing Strictness
Concavity Analysis
Sample Space Partitioning
Line-Plane intersection logic
Parametric form pitfalls
Sample Space Partitioning
Nilpotent Matrix Logic
Row/Column Exchange Parity
Sample Space Partitioning
Rectangular Hyperbola
Locus of a Moving Point
Sample Space Partitioning
Sigma Notation Properties
Compound Function Boundaries
Sample Space Partitioning
System of Linear Equations (Cramer/Inverse)
Singular vs. Non-Singular Threshold
Conditional Probability Constraints
Adjoint-Determinant Relationship
Modulus Sign Dependency
Conditional Probability Constraints
Angle between line and plane
Line-Plane intersection logic
Conditional Probability Constraints
Bernoulli Trials Logic
Variance Invariance under Translation
Squeeze Theorem (Sandwich Theorem)
Conditional Probability Constraints
Binomial Theorem for Negative/Fractional Indices
Composition Inverse Identity Traps
Conditional Probability Constraints
Definite Integral Properties (King's Property)
Bounded vs Unbounded Regions
Wallis' Formula
Conditional Probability Constraints
Determinant as Geometric Area/Volume
Position Vector Origin Dependence
Unit Vector Normalization
Conditional Probability Constraints
Distributing Identical Items (Stars and Bars)
Greatest Integer Function Traps
Closed vs Open Intervals
Conditional Probability Constraints
Equivalence Class Partitioning
The Empty Set Trap
Conditional Probability Constraints
Leibniz Rule for Differentiation under Integral
Even and Odd Function Symmetry
Conditional Probability Constraints
Sigma Notation Properties
Conjugate Root Theorem
Domain Constraint Intersections
Independent vs Disjoint Events
Area of Triangle via Determinants
Shifting Origin
Independent vs Disjoint Events
Complementary Counting
Equiprobability Fallacy
Independent vs Disjoint Events
Exact Differential Recognition
Implicit Differentiation Rate
Independent vs Disjoint Events
Leibniz Rule for Differentiation under Integral
Squeeze Theorem
Independent vs Disjoint Events
Linear Independence vs Dependence
Even Multiplicity Roots
Independent vs Disjoint Events
Orthogonal Matrix Conditions
Modulus Function Breakdown
Independent vs Disjoint Events
Properties of Binomial Coefficients
Greatest Integer Function (GIF)
Bayes' Theorem Reversal
Composite Injectivity/Surjectivity
Composition Inverse Identity Traps
Bayes' Theorem Reversal
Conditional Probability Constraints
Locus of a Moving Point
Bayes' Theorem Reversal
General Term Formulation
Polynomial Function Parity
Bayes' Theorem Reversal
Integral as the Limit of a Sum
Square Root and Modulus Logic
Bayes' Theorem Reversal
Line-Plane intersection logic
Distance from point to plane
Bayes' Theorem Reversal
Sample Space Partitioning
Greatest Integer Function Traps
Bayes' Theorem Reversal
Sigma Notation Properties
Greatest Binomial Coefficient
Bernoulli Trials Logic
Adjoint-Determinant Relationship
Inversion and Determinant Product
Bernoulli Trials Logic
Arithmetic Mean Inequality
System of Inequalities
Bernoulli Trials Logic
Determinant as Geometric Area/Volume
Locus Problems via Transformation
Bernoulli Trials Logic
First Derivative Test Trap
Extraneous Solution Rejection
Bernoulli Trials Logic
Non-Differentiable Extrema
Modulus Sign Dependency
Bernoulli Trials Logic
Scalar Triple Product Volume Logic
Linear Independence vs Dependence
Bernoulli Trials Logic
Standard Deviation and Coefficient of Variation
Maximum/Minimum of Quadratic Expressions
Binomial Distribution Tail-Ends
Characteristic Equation
Idempotent and Involutory Properties
Binomial Distribution Tail-Ends
Complementary Counting
Venn Diagram Overlap Logic
Binomial Distribution Tail-Ends
Geometry Constraint Modeling
Critical Point vs Stationary Point
L'Hopital's Pitfall
Binomial Distribution Tail-Ends
Multiple Optimal Solutions
Sign Flipping Rule
Redundant Constraints
Binomial Distribution Tail-Ends
Nilpotent Matrix Logic
Matrix Equality
Implicit to Explicit Conversion
Random Variable Expectation
Bernoulli Trials Logic
Definite Integral Properties (King's Property)
Random Variable Expectation
Division into Groups
Signum Function Discontinuity
Random Variable Expectation
Quadratic Roots in Sequences
Modulus Inequality Logic
Random Variable Expectation
System of Linear Equations (Cramer/Inverse)
Skew-Symmetric Determinant
Sampling With/Without Replacement
Determinant as Geometric Volume
Collinearity vs Coplanarity
Condition of Collinearity
Sampling With/Without Replacement
Locus Problems via Transformation
Euler's Form and Rotations
The Quadrant Adjustment Logic
Sampling With/Without Replacement
Random Variable Expectation
Critical Point vs Stationary Point
Non-Differentiable Extrema
Sampling With/Without Replacement
Selection vs Arrangement
Greatest Binomial Coefficient
Complementary Counting
Probability Mass Function (PMF)
Conditional Probability Constraints
Equivalence Class Partitioning
Probability Mass Function (PMF)
Newton-Leibniz Formula with variable limits
Step Function Discontinuities
Probability Mass Function (PMF)
Variance Invariance under Translation
Concavity Analysis
Complementary Counting
Linear Dependency of Rows
Row/Column Exchange Parity
Complementary Counting
Properties of Binomial Coefficients
Modulus Inequality Logic
Complementary Counting
Sampling With/Without Replacement
Correction of Incorrect Mean/Variance
Complementary Counting
Scalar Triple Product Volume Logic
Dot Product Projection Sign
Combinatorial Probability
Exponent of Prime in n!
Even and Odd Function Symmetry
Combinatorial Probability
Injective Mapping (One-One)
Domain Restrictions
Combinatorial Probability
Linear Dependency of Rows
Matrix Trace Properties
Combinatorial Probability
Quadratic Roots in Sequences
Extraneous Solution Rejection
Combinatorial Probability
Scalar Triple Product Volume Logic
Distance from point to plane
Equiprobable Outcomes
Infinite GP Convergence
Cross Multiplication Trap
Equiprobable Outcomes
Leibniz Rule for Differentiation under Integral
Modulus Function Breakdown
Equiprobable Outcomes
Properties of Binomial Coefficients
Denominator Restriction
Equiprobable Outcomes
Scalar Triple Product Volume Logic
Skew-Symmetric Determinant
De Morgan's Laws in Probability
Independent vs Disjoint Events
Mutually Exclusive vs Independent
De Morgan's Laws in Probability
Linear Dependency of Rows
Characteristic Equation
De Morgan's Laws in Probability
Sampling With/Without Replacement
Exponent of Prime in n!
Variance of Discrete Variable
Coefficient of x^r in Expansion
Squeeze Theorem (Sandwich Theorem)
Variance of Discrete Variable
Indicator Function Logic
De Morgan's Laws in Probability
Variance of Discrete Variable
Matrix Trace Properties
Triangular/Diagonal Matrix Shortcut
Variance of Discrete Variable
Method of Differences
Integral as the Limit of a Sum
Variance of Discrete Variable
Multinomial Theorem
Step Function Discontinuities
Multiplication Principle Scope
Combinatorial Counting (P&C Integration)
Coefficient of x^r in Expansion
Minors and Cofactors Sign Convention
Multiplication Principle Scope
Combinatorial Probability
Signum Function Discontinuity
Unbounded Region Trap
Multiplication Principle Scope
Conditional Probability Constraints
Sample Space Partitioning
Modulus Sign Dependency
Multiplication Principle Scope
Homogeneous Function Homogeneity
Arbitrary Constants vs Parameters
Empty and Universal Relation
Multiplication Principle Scope
Substitution (Change of Variable)
Second Derivative Test Limitations
Point of Inflection Conditions

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

Two fair dice are rolled to obtain two numbers aa and bb respectively, where a,b∈{1,2,3,4,5,6}a, b \in \{1, 2, 3, 4, 5, 6\}. Consider the system of linear equations: x+2y+z=1x + 2y + z = 1 2x+ay+3z=b2x + ay + 3z = b 3x+7y+4z=43x + 7y + 4z = 4 Three urns U1,U2,U_1, U_2, and U3U_3 contain balls as follows: U1U_1 has 3 Red and 2 Black balls, U2U_2 has 2 Red and 3 Black balls, and U3U_3 has 4 Red and 1 Black balls. A ball is drawn at random from U1U_1 if the system has a unique solution, from U2U_2 if the system has infinitely many solutions, and from U3U_3 if the system has no solution. If the drawn ball is Red, what is the probability that the system of equations had no solution?

2

A biased die has its faces painted with three colors: Red, Blue, and Green. The probability of rolling a Red face is 12\frac{1}{2}, a Blue face is 13\frac{1}{3}, and a Green face is 16\frac{1}{6}. In a game, a player rolls this die 66 times independently. For each Red face rolled, the player scores 11 point; for each Blue face, 22 points; and for each Green face, 00 points. What is the probability that the player scores exactly 88 points in the 66 rolls?

3

Let G(x)G(x) be a generating function whose first derivative is given by G′(x)=x4−12x3+46x2−60xG'(x) = x^4 - 12x^3 + 46x^2 - 60x. A random variable XX follows a binomial distribution B(n,p)B(n, p) with n=9n=9 and p=12p=\frac{1}{2}. Let AA be the event that G(x)G(x) is strictly decreasing at x=Xx = X, and let BB be the event that G(x)G(x) is strictly concave at x=Xx = X. Find the conditional probability P(B∣A)P(B \mid A).

4

Let S={1,ω,ω2}S = \{1, \omega, \omega^2\}, where ω=ei2π/3\omega = e^{i2\pi/3}. Let F\mathcal{F} be the set of all quadratic equations of the form z2+az+b=0z^2 + az + b = 0, where a,b∈Sa, b \in S. If two distinct equations are chosen at random from F\mathcal{F}, the probability that they have at least one common root is pq\frac{p}{q}, where pp and qq are coprime positive integers. The value of p+qp+q is:

5

Let S={−2,−1,0,1,2}S = \{-2, -1, 0, 1, 2\}. A point Q(a,b,c)Q(a, b, c) is chosen uniformly at random such that a,b,c∈Sa, b, c \in S. Let PP be the point (1,1,1)(1, 1, 1) and Π\Pi be the plane x+y+z=0x + y + z = 0. What is the probability that the line segment PQPQ intersects the plane Π\Pi?

6

15 identical coins are distributed into 3 distinct boxes. Let xix_i be the number of coins in the ii-th box (i=1,2,3i=1,2,3). Let EE be the event that ∑i=13[xi4]=2\sum_{i=1}^3 \left[ \frac{x_i}{4} \right] = 2, where [⋅][ \cdot ] denotes the greatest integer function. Let FF be the event that xi∈[0,7)x_i \in [0, 7) for all i=1,2,3i=1,2,3. The conditional probability P(F∣E)P(F|E) is

7

Let I=∫0πxsin⁡6xcos⁡4x dxI = \int_0^{\pi} x \sin^6 x \cos^4 x \, dx. A continuous random variable XX has the probability density function f(x)={5123π2ln⁡3⋅Ix2+2xfor x≥λ0otherwisef(x) = \begin{cases} \frac{512}{3\pi^2 \ln 3} \cdot \frac{I}{x^2+2x} & \text{for } x \ge \lambda \\ 0 & \text{otherwise} \end{cases} where λ>0\lambda > 0 is a constant such that f(x)f(x) is a valid probability density function. Find the conditional probability P(X>2∣X>12)P\left(X > 2 \mid X > \frac{1}{2}\right).

8

Let SS be the set of distinct complex roots of the polynomial P(z)=z6−z5+z4−z3+z2−z+1P(z) = z^6 - z^5 + z^4 - z^3 + z^2 - z + 1. For any z∈Sz \in S, let arg⁡z∈(−π,π]\arg z \in (-\pi, \pi] denote its principal argument. Define the set T⊂ST \subset S as: T={z∈S ∣ ∑n=1∞(arcsin⁡(sin⁡(arg⁡z))arg⁡z)n diverges}T = \left\{ z \in S \ \Big| \ \sum_{n=1}^{\infty} \left( \frac{\arcsin(\sin(\arg z))}{\arg z} \right)^n \text{ diverges} \right\} A pair of distinct complex numbers {z,w}\{z, w\} is chosen uniformly at random from TT. Let pp be the probability that Im(z+w)>0\text{Im}(z + w) > 0. Find the value of: p∑z∈S∣z2−z+1∣2p \sum_{z \in S} |z^2 - z + 1|^2

9

Let a^,b^,c^\hat{a}, \hat{b}, \hat{c} be three mutually perpendicular unit vectors in R3\mathbb{R}^3 forming a right-handed coordinate system. Let x^\hat{x} be a random unit vector chosen uniformly from the surface of the unit sphere centered at the origin. Let VV be the volume of the parallelepiped whose coterminous edges are the vectors a^−x^,b^−x^\hat{a}-\hat{x}, \hat{b}-\hat{x}, and c^−x^\hat{c}-\hat{x}.

Define two events based on this volume: E1E_1: V≤2V \le 2 E2E_2: V≥1V \ge 1

Find the conditional probability P(E2∣E1)P(E_2 \mid E_1).

10

Let p=lim⁡n→∞∑k=1nk2n3+k2sin⁡(k)p = \lim_{n \to \infty} \sum_{k=1}^n \frac{k^2}{n^3 + k^2 \sin(k)}. A biased coin with probability of heads P(H)=14P(H) = \frac{1}{4} is tossed repeatedly. Let NN be the random variable representing the number of tails obtained before exactly 3 heads appear. Let XX be the number of successes in NN independent Bernoulli trials, where the probability of success in each trial is pp. Find the variance of the random variable Z=X+N3Z = X + \frac{N}{3}.

11

Let SS be the sample space of all 2×22 \times 2 orthogonal matrices with real entries. A matrix A=(a11a12a21a22)A = \begin{pmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{pmatrix} is chosen uniformly at random from SS. (The uniform distribution is defined by choosing the parameter θ∈[0,2π)\theta \in [0, 2\pi) uniformly, and the determinant det⁡(A)∈{−1,1}\det(A) \in \{-1, 1\} with equal probability 12\frac{1}{2}). Let E1E_1 be the event that ∣a11+a22∣>2|a_{11} + a_{22}| > \sqrt{2}, E2E_2 be the event that ∣a12−a21∣>2|a_{12} - a_{21}| > \sqrt{2}, E3E_3 be the event that ∣a11∣≥12|a_{11}| \ge \frac{1}{2}, and E4E_4 be the event that det⁡(A)=1\det(A) = 1. Which of the following statements is correct?

12

Let AA and BB be two independent events with probabilities P(A)=12P(A) = \frac{1}{2} and P(B)=qP(B) = q, where q∈(0,1)q \in (0, 1). Consider the matrix M=(P(A∪B)P(Ac∩Bc)−12P(A∩B)P(Ac∪Bc)−12P(A)P(Ac)−12)M = \begin{pmatrix} P(A \cup B) & P(A^c \cap B^c) & -\frac{1}{2} \\ P(A \cap B) & P(A^c \cup B^c) & -\frac{1}{2} \\ P(A) & P(A^c) & -\frac{1}{2} \end{pmatrix} Let SS be the set of all values of qq for which the eigenvectors of MM span R3\mathbb{R}^3. Then the sum of all elements in SS is:

13

Let AA and BB be two disjoint events in a sample space such that P(A)=3P(B)P(A) = 3P(B). Consider three points in the Cartesian plane: V1=(P(A),P(B))V_1 = (P(A), P(B)), V2=(P(A∪B),P(A∩B))V_2 = (P(A \cup B), P(A \cap B)), and V3=(P(Ac),P(Bc))V_3 = (P(A^c), P(B^c)). Let MM be the 3×33 \times 3 matrix whose rows are (xi,yi,1)(x_i, y_i, 1) for i=1,2,3i=1,2,3, where (xi,yi)(x_i, y_i) are the coordinates of ViV_i in the standard (X,Y)(X, Y) coordinate system. Let D=det⁡(M)D = \det(M). A new coordinate system (X′,Y′)(X', Y') is defined by the transformation X′=X−P(A)X' = X - P(A) and Y′=P(B)−YY' = P(B) - Y. Let M′M' be the corresponding matrix using the coordinates of the same points in the (X′,Y′)(X', Y') system, and let D′=det⁡(M′)D' = \det(M'). If D−D′=14D - D' = \frac{1}{4}, what is the value of P(A∪B)P(A \cup B)?

14

For sufficiently small x>0x > 0, let AxA_x and BxB_x be two events such that P(Ax)=xP(A_x) = x and P(Bx)=x2P(B_x) = x^2. The probability of their union satisfies the integral equation: ∫0xtP(At∪Bt)dt=x33+x44−1535x5−∫0xx2−t22t2sin⁡(1t)dt\int_0^x t P(A_t \cup B_t) dt = \frac{x^3}{3} + \frac{x^4}{4} - \frac{153}{5} x^5 - \int_0^x \frac{x^2 - t^2}{2} t^2 \sin\left(\frac{1}{t}\right) dt Let L=lim⁡x→0+P(Ax∩Bx)P(Ax)P(Bx)L = \lim_{x \to 0^+} \frac{P(A_x \cap B_x)}{P(A_x) P(B_x)}. Find the value of LL.

15

Let A={1,2,3,4}A = \{1, 2, 3, 4\} and SS be the set of all functions from AA to AA. The set SS is partitioned into three subsets S1,S2,S3S_1, S_2, S_3 as follows:

S1S_1: The set of all bijective functions. S2S_2: The set of all functions h∈Sh \in S that can be expressed as h=f∘gh = f \circ g, where g:A→Bg: A \to B is surjective, f:B→Af: B \to A is injective, and B={1,2,3}B = \{1, 2, 3\}. S3S_3: The set of all remaining functions in SS.

A biased die is rolled to select one of the subsets, such that the probability of selecting SiS_i is proportional to ii. Then, a function hh is chosen uniformly at random from the selected subset. Given that the chosen function satisfies h(1)=h(2)h(1) = h(2), what is the probability that it was chosen from S2S_2?

16

Let InI_n be the set of all integers kk (0≤k≤n0 \le k \le n) for which the binomial coefficient (nk)\binom{n}{k} attains its maximum value. Define Sn=∑k∈InkS_n = \sum_{k \in I_n} k. Two bags B1B_1 and B2B_2 are given. Bag B1B_1 contains 2 Red and 3 Black balls, while Bag B2B_2 contains 3 Red and 4 Black balls. A bag is selected at random such that the probability of choosing B1B_1 is proportional to ∑m=17S2m\sum_{m=1}^{7} S_{2m} and the probability of choosing B2B_2 is proportional to ∑m=18S2m−1\sum_{m=1}^{8} S_{2m-1}. A ball is drawn at random from the selected bag and is found to be Red. The probability that it came from Bag B1B_1 is:

17

A point P(x,y,z)P(x,y,z) is chosen uniformly at random from the solid sphere x2+y2+z2≤9x^2 + y^2 + z^2 \le 9. Let DD be the perpendicular distance from PP to the plane Π:x+2y+2z=3\Pi: x + 2y + 2z = 3. Two boxes, B1B_1 and B2B_2, contain colored balls. Box B1B_1 contains 2 white and 3 black balls. Box B2B_2 contains 4 white and 1 black balls. If D<1D < 1, Box B1B_1 is selected. If D≥1D \ge 1, Box B2B_2 is selected. A ball is drawn at random from the selected box and is found to be white. The probability that Box B1B_1 was selected is:

18

A point zz is chosen uniformly at random from the region S={z∈C:4≤∣z∣≤5}S = \{z \in \mathbb{C} : 4 \le |z| \le 5\}. Let w=u+ivw = u + iv be a complex number defined by the transformation w=z+16zw = z + \frac{16}{z}. A Bernoulli trial is considered a success if ww satisfies both of the following conditions: (i) det⁡(uv1801−801)≥0\det \begin{pmatrix} u & v & 1 \\ 8 & 0 & 1 \\ -8 & 0 & 1 \end{pmatrix} \ge 0 (ii) ∣w−8∣+∣w+8∣≤12|w - 8| + |w + 8| \le 12 Let XX be the number of successes in n=2024n = 2024 independent trials. Find the value of E(X2+X+1)E(X^2 + X + 1), where E(⋅)E(\cdot) denotes the expected value.

19

Let pp be a valid probability of success for a Bernoulli trial such that the function f(x)=16x4−48x3+46x2−15xf(x) = 16x^4 - 48x^3 + 46x^2 - 15x attains a local maximum at x=px=p. An experiment consists of repeatedly performing independent Bernoulli trials with success probability pp until 2 consecutive successes occur. Let EE be the expected number of trials required. Find the value of 9E9E.

20

Let XX be a random variable representing the number of successes in 2525 independent Bernoulli trials, each with probability of success p∈(0,1)p \in (0, 1). If the coefficient of variation of XX is at most 210\frac{\sqrt{2}}{10}, then the maximum possible standard deviation of XX is:

21

Let V(x)V(x) be the volume of a right circular cone of height xx inscribed in a sphere of radius 33. Let x0x_0 be the height that maximizes this volume, and let λ=932πV(x0)\lambda = \frac{9}{32\pi} V(x_0). For each integer n≥3n \ge 3, let XnX_n be a random variable following the binomial distribution B(n,pn)B(n, p_n), where pn=λnp_n = \frac{\lambda}{n}. Evaluate the limit: lim⁡n→∞n(eλP(Xn≤1)−4)\lim_{n \to \infty} n \left( e^\lambda P(X_n \le 1) - 4 \right)

22

Let A=(3p−2tan⁡θ−cot⁡θq)A = \begin{pmatrix} 3p - 2 & \tan \theta \\ -\cot \theta & q \end{pmatrix}, where p∈(0,1)p \in (0, 1), q∈Rq \in \mathbb{R}, and θ∈(0,π2)\theta \in \left(0, \frac{\pi}{2}\right). It is given that AA is a nilpotent matrix of index 2. If pp represents the probability of success in a single trial of a binomial experiment, find the probability of at least 4 successes in 6 independent trials.

23

Let SS be the feasible region in the first quadrant (x≥0,y≥0x \ge 0, y \ge 0) satisfying the system of inequalities: −x−2y≥−6-x - 2y \ge -6 2x+y≤62x + y \le 6 3x+4y≤243x + 4y \le 24 Let MM be the maximum value of the objective function Z=x+2yZ = x + 2y over the region SS. A biased coin, whose probability of showing heads is p=M15p = \frac{M}{15}, is tossed 5 times. Let XX be the number of heads obtained. Find the probability that the coin shows heads at least 3 times.

24

Let XX be a random variable representing the number of successes in n=280n = 280 independent Bernoulli trials. The probability of success in a single trial is given by pp, where p=∫0π/22sin⁡280x+sin⁡(2x)2(sin⁡280x+cos⁡280x+sin⁡(2x)) dxp = \int_0^{\pi/2} \frac{2\sin^{280} x + \sin(2x)}{2(\sin^{280} x + \cos^{280} x + \sin(2x))} \, dx Find the value of 16π2E[X2−X]\frac{16}{\pi^2} E[X^2 - X].

25

Let V={u⃗,v⃗,w⃗}V = \{ \vec{u}, \vec{v}, \vec{w} \} be a set of three vectors where u⃗=[1,−2,1]\vec{u} = [1, -2, 1], v⃗=[−1,1,2]\vec{v} = [-1, 1, 2], and w⃗=[2,−1,−3]\vec{w} = [2, -1, -3]. Let SS be the set of all 3×33 \times 3 matrices whose rows are formed by choosing vectors from VV with replacement. A matrix AA is chosen at random from SS. Let E1E_1 be the event that the system of linear equations Ax⃗=0⃗A \vec{x} = \vec{0} has non-trivial solutions, and E2E_2 be the event that AA is a nilpotent matrix. The value of P(E2∣E1)P(E_2 \mid E_1) is:

26

Let S={1,2,3,…,1500}S = \{1, 2, 3, \dots, 1500\}. A number nn is chosen uniformly at random from SS. Let EE be the event that ∑k=1n(302−∣k−302∣)≥0\sum_{k=1}^{n} (302 - |k - 302|) \ge 0 Find the probability of the event EE.

27

Let S={1,2,3,…,15}S = \{1, 2, 3, \dots, 15\}. A number kk is chosen at random from SS. Let the function fk:[−3,3]→Rf_k : [-3, 3] \to \mathbb{R} be defined as fk(x)=min⁡(9−x2,2∣x∣+k)f_k(x) = \min(9-x^2, 2|x|+k) Let MkM_k be the global maximum value of fk(x)f_k(x) on the interval [−3,3][-3, 3]. Let EE be the event that MkM_k is an integer, and FF be the event that fk(x)f_k(x) is non-differentiable at the point(s) where it attains its global maximum. Find the conditional probability P(F∣E)P(F \mid E).

28

Let S={(h,k)∈Z×Z:h2+k2≤34}S = \{(h, k) \in \mathbb{Z} \times \mathbb{Z} : h^2 + k^2 \le 34\}. A point (h,k)(h, k) is chosen at random from SS. Given that the equation hx2−hy2+2kxy+2x−2y+1=0hx^2 - hy^2 + 2kxy + 2x - 2y + 1 = 0 represents a conic section, what is the probability that it represents a non-degenerate rectangular hyperbola?

29

Let S={−2,−1,0,1,2}S = \{-2, -1, 0, 1, 2\}. An ordered triplet (x,y,z)(x, y, z) of distinct numbers is chosen randomly from SS to form the matrix A=(x∣x∣1y∣y∣1z∣z∣1)A = \begin{pmatrix} x & |x| & 1 \\ y & |y| & 1 \\ z & |z| & 1 \end{pmatrix} Let EE be the event that det⁡(adjA)≥16\det(\text{adj} A) \ge 16 and FF be the event that the matrix AA is invertible. The value of the conditional probability P(E∣F)P(E \mid F) is

30

Let SS be the sample space of all functions f:A→Af: A \to A, where A={1,2,3,4}A = \{1, 2, 3, 4\}. A function is chosen at random from SS with uniform probability. Define an equivalence relation ∼\sim on SS by f∼g  ⟺  Range(f)=Range(g)f \sim g \iff \text{Range}(f) = \text{Range}(g). Let CfC_f denote the equivalence class containing ff. Let EE be the event that the chosen function satisfies f(f(x))=f(x)f(f(x)) = f(x) for all x∈Ax \in A. Let FF be the event that the chosen function belongs to an equivalence class of size exactly 1616. Find the conditional probability P(E∣F)P(E \mid F).

31

Let XX be a continuous random variable taking values in the interval [−1,1][-1, 1]. Its probability density function f(x)f(x) satisfies the integral equation: ∫0xf(t)1+t2 dt=14(x1+x2+ln⁡(x+1+x2))+16((1+x2)3/2−1)\int_{0}^{x} f(t) \sqrt{1+t^2} \, dt = \frac{1}{4} \left( x \sqrt{1+x^2} + \ln(x + \sqrt{1+x^2}) \right) + \frac{1}{6} \left( (1+x^2)^{3/2} - 1 \right) for all x∈[−1,1]x \in [-1, 1]. Let E1E_1 be the event that the roots of the quadratic equation y2+2Xy+X=0y^2 + 2Xy + X = 0 (in variable yy) are real. Let E2E_2 be the event that XX satisfies the inequality: ∫−XXt3cos⁡2t1+t2 dt+X2≤14\int_{-X}^{X} \frac{t^3 \cos^2 t}{1+t^2} \, dt + X^2 \le \frac{1}{4} Find the conditional probability P(E1∣E2)P(E_1 \mid E_2).

32

A line LL with direction vector d⃗\vec{d} is generated such that d⃗\vec{d} is a random vector uniformly distributed on the surface of the unit sphere in R3\mathbb{R}^3. Let PP be a fixed plane in R3\mathbb{R}^3. Let θ∈[0,π2]\theta \in \left[0, \frac{\pi}{2}\right] be the acute angle between the line LL and the plane PP. If E1E_1 is the event that θ≥π6\theta \ge \frac{\pi}{6} and E2E_2 is the event that θ≤π3\theta \le \frac{\pi}{3}, then the conditional probability P(E2∣E1)P(E_2 \mid E_1) is equal to

33

Let XX be a discrete random variable taking values in the set of non-negative integers W={0,1,2,… }\mathbb{W} = \{0, 1, 2, \dots\}. The probability mass function of XX is given by P(X=k)=C(k+22)tkP(X=k) = C \binom{k+2}{2} t^k where t=1πsin⁡−1(sin⁡8π9)t = \frac{1}{\pi} \sin^{-1}\left(\sin \frac{8\pi}{9}\right) and CC is a positive constant such that ∑k=0∞P(X=k)=1\sum_{k=0}^{\infty} P(X=k) = 1. Find the conditional probability P(X=1∣X≤2)P(X=1 \mid X \le 2).

34

Let AA and BB be two independent events associated with a random experiment. The probabilities of these events are given by: P(A)=1320∑r=010(202r)22rP(A) = \frac{1}{3^{20}} \sum_{r=0}^{10} \binom{20}{2r} 2^{2r} P(B)=1220∑r=020(20r)sin⁡2(rπ2)P(B) = \frac{1}{2^{20}} \sum_{r=0}^{20} \binom{20}{r} \sin^2\left(\frac{r\pi}{2}\right) Find the value of ⌊4⋅P(A∪B)⌋\lfloor 4 \cdot P(A \cup B) \rfloor, where ⌊x⌋\lfloor x \rfloor denotes the greatest integer less than or equal to xx.

35

The probability P(t)P(t) that a quantum system remains in its initial state at time t≥0t \ge 0 satisfies the differential equation dPdt=−P22tP+1\frac{dP}{dt} = -\frac{P^2}{2tP + 1} with the initial condition P(0)=1P(0) = 1. A researcher measures the state at t=2t=2 and correctly determines the probability P(2)P(2). To predict the probability at t=12t=12, the researcher incorrectly assumes the system's decay is memoryless, satisfying P(t1+t2)=P(t1)P(t2)P(t_1 + t_2) = P(t_1)P(t_2) for all t1,t2≥0t_1, t_2 \ge 0. Let PestP_{est} be the researcher's estimated probability at t=12t=12, and PtrueP_{true} be the actual probability at t=12t=12 governed by the differential equation. Find the value of PtruePest\frac{P_{true}}{P_{est}}.

36

A biased coin with probability of heads p∈(0,1)p \in (0, 1) is tossed repeatedly until a head appears. Let XX be the total number of tosses required. Let AA be the event that XX is a multiple of 3, and BB be the event that X≥4X \ge 4. If P(A∪B)=2536P(A \cup B) = \frac{25}{36}, find the probability that XX is an even number.

37

Let E1,E2,E_1, E_2, and E3E_3 be a partition of a sample space. The prior probabilities of these events are given by: P(E1)=[x]5,P(E2)=[x2]5,P(E3)=[x3]5P(E_1) = \frac{[x]}{5}, \quad P(E_2) = \frac{[x^2]}{5}, \quad P(E_3) = \frac{[x^3]}{5} where x>0x > 0 and [⋅][\cdot] denotes the greatest integer function. Let SS be the set of all possible values of xx for which this partition is valid. If S=[a,b)S = [a, b), an observable event AA occurs such that the conditional probabilities are P(A∣E1)=12P(A|E_1) = \frac{1}{2}, P(A∣E2)=34P(A|E_2) = \frac{3}{4}, and P(A∣E3)=14P(A|E_3) = \frac{1}{4}. Find the value of (a2+b3)P(E2∣A)(a^2 + b^3) P(E_2|A).

38

Let ara_r be the coefficient of xrx^r in the polynomial P(x)=12((1+x)13+(1−x)13)P(x) = \frac{1}{2} \left( (1+x)^{13} + (1-x)^{13} \right). A set of 14 bags, labeled B0,B1,…,B13B_0, B_1, \dots, B_{13}, is given. A bag is selected such that the probability of selecting bag BrB_r is proportional to ara_r. Each bag BrB_r contains rr red balls and 13−r13-r black balls. A bag is chosen at random according to the given probabilities, and two balls are drawn from it, one after another, with replacement. Given that both drawn balls are red, what is the probability that they were drawn from bag B6B_6?

39

Let E1E_1 and E2E_2 be two mutually exclusive and exhaustive events such that the prior probability P(E1)P(E_1) is given by P(E1)=613lim⁡n→∞1n2∑k=1n9k2−6kn+n2P(E_1) = \frac{6}{13} \lim_{n \to \infty} \frac{1}{n^2} \sum_{k=1}^n \sqrt{ 9k^2 - 6kn + n^2 } A box B1B_1 contains 4 red and 1 black balls, while box B2B_2 contains 3 red and 5 black balls. A box is selected such that the probability of selecting B1B_1 is P(E1)P(E_1) and the probability of selecting B2B_2 is P(E2)P(E_2). A ball is drawn at random from the selected box and is found to be red. The probability that it was drawn from box B2B_2 is:

40

A particle P(x,y)P(x,y) is placed in the Cartesian plane. A random process selects its domain of uniform distribution: with probability 25\frac{2}{5}, the particle is distributed uniformly over the region R1R_1 enclosed by the locus ∣x∣+∣y∣=2|x| + |y| = 2; with probability 35\frac{3}{5}, it is distributed uniformly over the region R2R_2 enclosed by the locus max⁡(∣x∣,∣y∣)=2\max(|x|, |y|) = 2. Given that the particle's position satisfies the condition x2+y2≤4x^2 + y^2 \le 4, what is the probability that its domain was R1R_1?

41

Let g(x)=∣∫−1x(2∣t∣−2) dt∣g(x) = \left| \int_{-1}^x (2|t| - 2) \, dt \right|. Let mm be the number of points in R\mathbb{R} where g(x)g(x) is non-differentiable. A biased coin with probability of heads p=m5p = \frac{m}{5} is tossed 75 times. If XX is the number of heads obtained, find the value of E(X)+Var(X)E(X) + \text{Var}(X).

42

Let u⃗=sec⁡2θi^+cot⁡2θj^+csc⁡2θk^\vec{u} = \sec^2 \theta \hat{i} + \cot^2 \theta \hat{j} + \csc^2 \theta \hat{k}, v⃗=tan⁡2θi^+csc⁡2θj^+sec⁡2θk^\vec{v} = \tan^2 \theta \hat{i} + \csc^2 \theta \hat{j} + \sec^2 \theta \hat{k}, and w⃗=i^−j^+(cot⁡2θ−tan⁡2θ)k^\vec{w} = \hat{i} - \hat{j} + (\cot^2 \theta - \tan^2 \theta)\hat{k}, where θ∈(0,π4)\theta \in \left(0, \frac{\pi}{4}\right). Let pp be the volume of the parallelepiped whose coterminous edges are u⃗,v⃗,w⃗\vec{u}, \vec{v}, \vec{w}. A biased coin, which shows heads with probability pp, is tossed 10 times. Let XX denote the number of heads obtained. Find the value of ∑k=010(k2−3k+2)P(X=k)+2024p\sum_{k=0}^{10} (k^2 - 3k + 2) P(X=k) + 2024^p

43

Let An=[aij]A_n = [a_{ij}] be an n×nn \times n matrix where aij=i+ja_{ij} = i + j for 1≤i,j≤n1 \le i, j \le n. Let p=∣adj(An)∣+1∣An∣n−1+2p = \frac{|adj(A_n)| + 1}{|A_n|^{n-1} + 2} be the probability of success in a single trial of a Bernoulli experiment. Let XX be the number of successes in nn independent trials of this experiment. If P(X=2)=P(X=3)>0P(X=2) = P(X=3) > 0, then the variance of XX is equal to

44

Let SS be the set of all positive integers nn for which there exists a positive integer mm satisfying the system of linear inequalities: 3n−2m≥143n - 2m \ge 14 n+m≤6n + m \le 6 A biased coin, whose probability of getting heads is p=max⁡x>0(x2x4+4)p = \max_{x > 0} \left( \frac{x^2}{x^4 + 4} \right), is tossed nn times for each n∈Sn \in S. Let PnP_n be the probability of getting exactly 2 heads in nn tosses. Find the value of 4096∑n∈SPn4096 \sum_{n \in S} P_n.

45

Let BB be a 3×33 \times 3 matrix such that B2=IB^2 = I, B≠IB \neq I, and B≠−IB \neq -I. Let A=12(I−B)A = \frac{1}{2}(I - B). The probability of success of an event is given by p=det⁡(A)p = \det(A). A trial is repeated 216216 times independently. Let XX be the number of successes, and let λ=P(X≥1)\lambda = P(X \ge 1). If P(x)=det⁡(A−xI)P(x) = \det(A - x I) and Q(x)=det⁡(B−xI)Q(x) = \det(B - x I), find the value of P(λ)+(Q(λ))2P(\lambda) + (Q(\lambda))^2.

46

A biased tetrahedral die has four faces coloured red, black, white, and white. The probability of rolling the red face is 14\frac{1}{4}, the black face is 14\frac{1}{4}, and a white face is 12\frac{1}{2}. If the die is rolled 55 times, let pp be the probability that either the red face appears at least twice, or the black face appears at least twice (or both). Find the value of pp.

47

Let YY be a random variable chosen uniformly from the set {−2,−1,0,1,2}\{-2, -1, 0, 1, 2\}. A set of 5 distinct objects is divided into kk non-empty subsets, where k=3+sgn(Y)k = 3 + \text{sgn}(Y). All valid partitions of the 5 objects into kk subsets are equally likely. Let XX be the random variable representing the number of objects in the subset containing a specific object O1O_1. Find the expected value E[X]E[X].

48

Let SS be the set of all integers nn satisfying the inequality ∣n2−6n+5∣≤5−n|n^2 - 6n + 5| \le 5 - n. A number kk is chosen uniformly at random from SS. Let the random variable XX be defined as the maximum real root of the quadratic equation x2−(2k+k2)x+k22k=0x^2 - (2^k + k^2)x + k^2 2^k = 0 Find the expected value E(X)E(X).

49

Let X,Y,X, Y, and ZZ be independent random variables, each chosen uniformly from the set {1,2,3,…,10}\{1, 2, 3, \dots, 10\}. Consider the system of linear equations Av=bA \mathbf{v} = \mathbf{b}, where A=(0XY−X0Z−Y−Z0)andb=(ZYX)A = \begin{pmatrix} 0 & X & Y \\ -X & 0 & Z \\ -Y & -Z & 0 \end{pmatrix} \quad \text{and} \quad \mathbf{b} = \begin{pmatrix} Z \\ Y \\ X \end{pmatrix} Let RR be the rank of the augmented matrix [A∣b][A | \mathbf{b}]. The expected value of RR, denoted by E[R]E[R], is:

50

Let NN be the greatest binomial coefficient in the expansion of (1+x)6(1+x)^6. A bag contains NN distinct balls numbered 11 to NN. Experiment A consists of drawing 3 balls from the bag with replacement. Let PAP_A be the probability that the sum of the numbers on the drawn balls is at most NN. Experiment B consists of drawing 3 balls from the bag without replacement. Let PBP_B be the probability that the sum of the numbers on the drawn balls is at most NN. Find the value of PAPB\frac{P_A}{P_B}.

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