I’m looking at an exercise. What kind of problem is this?

Every exercise in Chapters 2–3 was read and filed by question type, not by section. Each type teaches two independent recognition paths — the phrases printed on the page, and what the situation structurally demands — plus the traps where those two disagree.

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C017 q · ●●○○○Sample-Space Enumeration & Event ListingAsks you to list outcomes, never to compute a number.§2.1Sample SpaceEvent

C023 q · ●●●○○Event Algebra & Set IdentitiesTranslate English into ∪ / ∩ / ′, or verify an identity with a Venn diagram.§2.1Set Operations on EventsVenn Diagrams

C035 q · ●●○○○Two-Event Addition RuleTwo events, three of {P(A), P(B), P(A∩B), P(A∪B)} given, solve for the fourth.§2.2Addition Rule (Two Events)Complement Rule

C046 q · ●●●●○Three-Event Inclusion–ExclusionThree indexed events plus a pile of unions and/or intersections.§2.2§2.4§2.5Inclusion–Exclusion (Three Events)Venn Diagrams

C056 q · ●●○○○Complement Rule & 'At Least One'The event you want is ugly; its complement is a single easy product or a single draw.§2.2§2.5Complement RuleThe 'At Least One' Rule

C066 q · ●●●○○Joint / Partition Tables (Marginals & Conditionals)A rectangular table of proportions, or a list of categories summing to 100%.§2.2§2.4Joint Probability TableMarginal Probability

C074 q · ●●○○○Equally Likely Outcomes (Classical Probability)A symmetry statement licenses P = (favourable)/(total); the work is counting.§2.2Equally Likely OutcomesCombinations

C085 q · ●●●○○Conditional Probability from Given Event Probabilities"Given that ..." with the numerator and denominator both built from Venn regions.§2.4Conditional ProbabilityInclusion–Exclusion (Three Events)

C098 q · ●●●●○Multi-Stage Trees & the Chain RulePercentages conditional on an earlier stage, often on an intersection of earlier stages.§2.4§2.5Tree DiagramMultiplication Rule (Chain Rule)

C106 q · ●●●○○Law of Total Probability & Bayes' TheoremPriors for a partition, a success rate per branch, then a question that runs backwards.§2.4Law of Total ProbabilityBayes' Theorem

C114 q · ●●●○○Verifying or Refuting IndependenceYou have P(A), P(B) and P(A∩B) and are asked whether the events are independent.§2.5IndependenceDetecting Dependence

C123 q · ●●●●○System Reliability (Series & Parallel)A wiring diagram plus 'components work independently'; compute P(system works).§2.5Series & Parallel SystemsIndependence

C134 q · ●●●○○Computations with Mutually Independent EventsThe problem states independence; every intersection becomes a product.§2.5IndependenceMutual Independence

C1410 q · ●●●○○Proofs & Derivations of RulesThe verb is "show", "prove", "verify", or "derive"; there are no numbers.§2.2§2.4§2.5§3.2§3.4§3.6Axioms of ProbabilityMonotonicity of Probability

C154 q · ●●○○○Defining a Random Variable on a Sample Space"List each outcome ... along with the associated value of X."§3.1Random VariableSample Space

C166 q · ●●○○○Possible Values & Discrete vs Continuous"What are the possible values ...?" / "state whether the variable is discrete."§3.1Random VariableDiscrete Random Variable

C175 q · ●●○○○Working with a Given pmfA pmf table (or formula) is handed to you; sum it over an event, or find its constant.§3.2Probability Mass Function (pmf)Normalization Condition

C183 q · ●●●○○cdf ⇄ pmf ConversionA step function is given (or requested); jumps are probabilities.§3.2Cumulative Distribution Function (cdf)Probability Mass Function (pmf)

C194 q · ●●●○○Deriving a pmf by EnumerationNo probabilities given; build 𝒮, evaluate X, tally by value.§3.2Probability Mass Function (pmf)Equally Likely Outcomes

C205 q · ●●●●○Deriving a pmf from Independent TrialsRepeated independent trials with a stated per-trial probability; build p(x) yourself.§3.2Probability Mass Function (pmf)Independence

C213 q · ●●●○○Mean, Variance and SD from a pmf"Compute E(X)" and "V(X)" with a pmf table (or a closed-form pmf) in hand.§3.3Expected ValueVariance

C224 q · ●●●○○Expected Value of a Function h(X)A nonlinear function of X appears; E[h(X)] = Σ h(x)p(x), never h(E(X)).§3.3Expected Value of a FunctionLaw of the Unconscious Statistician

C235 q · ●●○○○Linear Rescaling: E(aX + b) and V(aX + b)The quantity of interest is a linear function of X — a price, a remainder, a reflection.§3.3Linear Transformation RulesExpected Value

C243 q · ●●●○○Expected Value as a Decision CriterionTwo or more courses of action; compute E[payoff] for each and pick the best.§3.3Expected Value of a FunctionExpected Value

C253 q · ●●●○○Chebyshev's Inequality & Moment Bounds"k standard deviations from the mean" with a bound, or an inequality on E(X).§3.3§3.4Chebyshev's InequalityExpected Value

C267 q · ●●○○○Binomial Computation & Momentsn and p are handed to you; compute pmf/cdf values, μ, σ, or a linear function of X.§3.4Binomial DistributionBinomial pmf

C275 q · ●●●●○Identifying the Success ProbabilityNo stated percentage is p; you must build or flip it before any binomial work.§3.4Binomial DistributionDefining 'Success' Correctly

C284 q · ●●●●○Binomial with a Capacity ConstraintLimited stock or seats on both sides; 'everyone is satisfied' becomes a two-sided window.§3.4Binomial DistributionCapacity Constraints

C293 q · ●●●●○Binomial as a Function of p (OC Curves & Error Probabilities)p is not a number but an axis; evaluate the same binomial probability at several p.§3.4Binomial cdf and Table A.1Binomial Distribution

C305 q · ●●●○○Hypergeometric DistributionA fully specified finite population, a dichotomy inside it, sampling without replacement.§3.5Hypergeometric DistributionSampling Without Replacement

C313 q · ●●●●○Large-Population Approximation (Hypergeometric → Binomial)Sampling without replacement, but n/N is tiny — so pretend it is with replacement.§2.5§3.5Binomial Approximation to the HypergeometricHypergeometric Distribution

C324 q · ●●●●○Negative Binomial & Geometric DistributionsTrials continue until a fixed number of successes is reached.§3.5Negative Binomial DistributionGeometric Distribution

C334 q · ●●○○○Poisson Computationμ is stated outright; compute p(x; μ), F(x; μ), or use σ = √μ.§3.6Poisson DistributionPoisson pmf

C343 q · ●●●○○Poisson Approximation to the Binomialn is large, p is tiny, and the question says 'approximate'.§3.6Poisson Approximation to the BinomialPoisson cdf and Table A.2

C356 q · ●●●●○Poisson Process (Rate × Interval)"according to a Poisson process with rate α" — form μ = α·(size of region).§3.6Poisson ProcessPoisson Distribution