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Laboratory Work 7 — Combinatorics and Descriptive Statistics

Seventh laboratory work of Computer Discrete Mathematics. You implement the counting formulas of combinatorics — permutations, arrangements, and combinations — compute the basic descriptive statistics of a dataset (mean, variance, standard deviation), and generate combinatorial objects explicitly.

At a glance

Topic Combinatorics (permutations, arrangements, combinations) and descriptive statistics (mean, variance, standard deviation)
Prerequisite lectures L13 — Probabilities
Deliverable Source code + report (see 5report.md)
Grading Banded: Easy 60–74 / Medium 75–89 / Hard 90–100 (see 4task.md)

Contents

# Part File
1 Objective 1purpose.md
2 Methodical guidelines (theory & algorithms) 2method.md
4 Task and order of execution 4task.md
5 Report contents 5report.md
6 Control questions 6questions.md

Conventions

  • Language — source code and the report are in English.
  • Implement it yourself — no built-in shortcuts. Do not call a library or built-in that performs the core task for you (e.g. no HashSet/set()/Set for sets, no library graph/relation type, no math.gcd, no eval). Build the mechanism from basic primitives — arrays/lists, loops, arithmetic, strings.
  • Deliverable — submit the source code and a report structured as in 5report.md; the code must reproduce the example outputs in 4task.md.
  • Self-contained theory — everything needed is in 2method.md; no external material is required.

Summary

Combinatorics counts the ways to arrange and select objects: the number of orderings of a set (permutations), of ordered selections of kk from nn (arrangements), and of unordered selections (combinations). These counts underpin probability, which is why the work pairs them with the basic descriptive statistics of a dataset — its mean (mathematical expectation), variance, and standard deviation. A final task moves from counting the objects to generating them. The tasks are grouped into three difficulty bands.

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