UNIVERSITY OF BRISTOL 
MATH10013: Probability and Statistics 
Final Examination Guide and Notes 2025 
 
- Part 01: Revision Notes 
- Part 02: Exam Practice Questions and Answers 
- Part 03: Sample Final Examination Paper 
- Part 04: Suggested Solutions to Final Exam Paper 
 
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University of Bristol - Probability and Statistics (MATH10013)

£35.00

ACADEMIC YEAR 2025 - 2026

University	University of Bristol
Unit Name	Probability and Statistics
Unit Code	MATH10013
Credit points	20
Level of study	C/4
Notes Title	Final Examination Guide and Notes
Format	PDF
Page Length	61

A clear, easy-to-follow set of notes for MATH10013: Probability and Statistics at the University of Bristol. It covers all examinable content, explains the exam format, and highlights the topics to focus on. You will get exam-style papers with full solutions, step-by-step explanations, and practical hints and tips. This set of notes is organised in a logical order so you can revise efficiently and apply methods under time pressure.

Probability is an everyday concept that most people have only a vague, intuitive understanding of. It concerns the notion that in a random experiment there are many possible events that could occur and one should quantify how probable these events are. For example, one may wish to compute the probability that if two six-sided dice are thrown the sum of the resulting numbers is 8. Probability has key connections to various other branches of mathematics, such as Analysis, Combinatorics, Functional Analysis, Game Theory, Geometry, Mathematical Physics, and Statistics.

Statistics builds upon probabilistic modelling of phenomena, often using a suitable family of parameterized distributions. Observations are modelled as realizations of random variables, and probability theory can then be used to make suitable inferences about parameters. Statistical ideas are essential for reasoning about uncertainty in a variety of disciplines.

After completing this unit successfully, students should be able to:

Calculate probabilities, expectations and variances for random variables with a given probability distribution, including by using techniques such as moment generating functions and conditioning.
Construct a formal probability model from an informal description.
Estimate parameters of tractable, parametric statistical models.
Construct a confidence interval and calibrate a hypothesis test.

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University of Bristol - Probability and Statistics (MATH10013)

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