TCX1004 | Finals Cheatsheet (Units 6-9)

Open-book cheatsheet for NUS TCX1004 Finals (Apr 30, 2026) — Combinatorics, Graph Theory, Probability, Distributions

for the general cheatsheet (Units 1-5: proof rules, sets, induction), see the TCX1004 cheatsheet. full unit-by-unit notes in the TCX1004 notebook.

Exam: Apr 30, 2026 · 17:00 · LT7A Seat 64 · Open Book · No calculators · 1.5h Scope: Unit 6+ (Combinatorics, Graph Theory, Probability, Distributions/Expectation/Variance) Note: leave answers unsimplified.


Unit 6: Combinatorics

Placeholder — to be translated from Unit Notes.


Unit 7: Graph Theory

Placeholder — to be translated from Unit Notes.


Unit 8: Probability

Placeholder — to be translated from Unit Notes.


Unit 9: Distributions, Expectation, Variance

Step 1 — identify the distribution (3 questions)

  1. Single trial, two outcomes? → Bernoulli$(p)$
  2. Fixed $n$ trials, count successes? → Binomial$(n, p)$ — verify BINS (Binary / Independent / Number fixed / Same $p$)
  3. Count trials until first success? → Geometric$(p)$

If none match: likely Uniform (all outcomes equally likely), or distribution is unknown → use bounds (Markov / Chebyshev).

Step 2 — distribution formulas

Distribution$E[X]$$\text{Var}[X]$Trigger words
Bernoulli$(p)$$p$$p(1-p)$one trial, two outcomes
Binomial$(n, p)$$np$$np(1-p)$$n$ fixed trials, count successes (BINS)
Geometric$(p)$$1/p$$(1-p)/p^2$until first success
Uniform (discrete)$(a+b)/2$depends on rangeequally likely outcomes

Bounds (distribution unknown)

BoundFormulaWhen to use
Markov$P[X \geq a] \leq E[X]/a$only $E[X]$ known, one-tail, $X \geq 0$
Chebyshev$P[\lvert X - \mu \rvert \geq a] \leq \text{Var}[X]/a^2$$E[X]$ and $\text{Var}[X]$ known, two-tail

Linearity of Expectation

$$E[X + Y] = E[X] + E[Y] \quad \text{(always, even if dependent)}$$

$$\text{Var}[X + Y] = \text{Var}[X] + \text{Var}[Y] \quad \text{(only if independent)}$$

Variable legend (don’t confuse under pressure)

SymbolSourceRole
$\mu$$E[X]$ from questioncenter
$\text{Var}[X]$from questionspread$^2$
$\sigma$$= \sqrt{\text{Var}[X]}$ (derived)std dev — appears in $1/k^2$ form only
$a$threshold inside $P[\lvert X - \mu \rvert \geq a]$denominator (squared)
$k$$= a/\sigma$$\sigma$-multiples in alternate form

$a$ and $\sigma$ are different variables even when they share a numeric value.


Pre-submit sanity checks

  • probability $\in [0, 1]$ — never negative
  • $E[X] \in [\min X, \max X]$ — NOT bounded by $[0, 1]$
  • re-add arithmetic on 1-mark questions
  • plugged $a$ (threshold) or $\sigma$ (std dev)? they are different
  • Chebyshev monotonicity: larger $a$ → smaller bound. if bound grew, you plugged wrong.
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