# LSE: Mathematics and Statistics Fundamentals Proctored Exam

Test your knowledge and ability to apply the concepts and methods from the four courses included in the LSE MicroBachelors program in Mathematics and Statistics Fundamentals and take your first step towards further study at undergraduate level or upskill in high-growth careers. 1 weeks
1–2 hours per week
Self-paced
Free

### There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Dec 1

This exam assesses all concepts, methods and techniques introduced across the four courses within the LSE MicroBachelors program in Mathematics and Statistics Fundamentals:

Mathematics 1: Differential calculus

Mathematics 1: Integral calculus, algebra, and applications

Statistics 1: Introductory statistics, probability and estimation ****

Statistics 1: Statistical methods ****

It is two hours in duration and must be sat under online proctored conditions.

It is the final step towards completing the LSE MicroBachelors program in Statistics Fundamentals and you must pass with a mark of 60% or higher to gain your certificate.

### At a glance

• Institution: LSE
• Subject: Math
• Level: Intermediate
• Prerequisites:

This is the final assessment for the LSE MicroBachelors program in Mathematics and Statistics Fundamentals. Before you take this assessment, you should ensure you have taken and passed each of the four respective courses listed above, have engaged with all the learning materials and fully understood the concepts, methods and techniques introduced.

• Language: English
• Video Transcript: English
• Associated programs:
• Associated skills: Probability, Integral Calculus, Algebra, Statistics, Basic Math

# What you'll learn

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N/A

# Syllabus

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The following topics are assessed within this exam:

• Functions and graphs

• The derivative

• Curve sketching and optimisation

• Functions of two variables and partial derivatives

• Critical points of two-variable functions

• Integration

• Profit maximisation

• Constrained optimisation

• Matrices, vectors, and linear equations

• Sequences, series, and financial modelling

● Point and interval estimation

● Hypothesis testing I

● Hypothesis testing II

● Contingency tables and the chi-squared test

● Sampling design and some ideas underlying causation

● Correlation and linear regression

● Mathematical revision and the nature of statistics

● Data visualisation and descriptive statistics

● Probability theory

● The normal distribution and ideas of sampling