Statistics 1 (S1) – AS/A-Level Mathematics Module

Statistics 1 (S1) – AS/A-Level Mathematics Module

The Statistics 1 (S1) module provides students with a solid foundation in statistical techniques and their applications in real-world contexts. This course is an essential component of the AS/A-Level Mathematics curriculum (Edexcel/Cambridge) and is ideal for learners pursuing careers or studies in science, engineering, economics, business, and social sciences.

Students will learn how to analyze, interpret, and represent data using mathematical models and probability theory, helping them make informed decisions based on statistical reasoning.

📘 Course Content Overview

Chapter 1: Mathematical Modelling

• Introduction to statistics through real-world problem contexts

• Understanding the process of creating mathematical models

Chapter 2: Measures of Location & Spread

• Mean, median, mode

• Range, interquartile range, variance, standard deviation

Chapter 3: Representation of Data

• Charts and graphs (histograms, box plots, cumulative frequency)

• Interpreting and comparing different data sets

Chapter 4: Probability

• Probability rules and laws

• Venn diagrams and tree diagrams

Chapter 5: Correlation & Regression

• Scatter diagrams

• Pearson’s correlation coefficient

• Regression lines and line of best fit

Chapter 6: Discrete Random Variable

• Probability distributions

• Expected value and variance of discrete variables

Chapter 7: The Normal Distribution

• Characteristics and applications of the normal distribution

• Standardization and use of z-scores

🎯 What Students Will Gain

• Deep understanding of statistical methods and data interpretation

• Strong skills in probability theory and its real-life applications

• Confidence in working with distributions, modeling, and graphical representation

• Preparation for advanced studies in quantitative disciplines and standardized assessments

• Data collection and analysis
• Graphical data representation
• Statistical calculations
• Probability problem-solving
• Correlation and regression modeling
• Working with discrete and normal distributions
• Practice with exam-style questions
• Statistical interpretation of real-life case studies