Historical Context & Motivation
Humans have been thinking about straight-line relationships for thousands of years. Ancient surveyors in Egypt noticed that the Nile's flood levels rose at roughly the same rate each day during flood season, making it possible to predict water heights and plan crop planting. This core idea — that a quantity changes at a constant rate — lies at the heart of what we now call a linear function. Over the centuries, mathematicians refined this idea into one of the most powerful and widely used tools in all of mathematics.
The question that drives this topic is deceptively simple: If something changes at a steady pace, how can we describe it, graph it, and use it to make predictions? Answering that question with precision is exactly what SL 2.2 is about.
Core Principles & Definitions
A linear function connects two variables with a relationship that produces a perfectly straight line when graphed on the Cartesian plane. Before we dive into calculations, let's lock down the four foundational ideas you'll use throughout the course.
Linear Function
Gradient (Slope)
y-Intercept
Rate of Change
Visualising Linear Functions
The graph below shows the line y = 2x + 3 plotted on a coordinate plane. Pay close attention to how the gradient triangle (rise over run) and the y-intercept are marked — these two features define every linear function you'll ever meet.
Every linear function graph shares these features. The line extends infinitely in both directions, the gradient triangle can be drawn between any two points and will always yield the same ratio, and the y-intercept sits exactly where x = 0. When you see a graph in an exam, these are the first two features to identify.
Mathematical Framework
The IB formula booklet provides several forms for linear functions. Understanding each form — what it tells you and when to use it — is essential for the exam and for real-world modelling.
Understanding Different Gradients
The sign and size of the gradient dramatically change what the linear model means in context. A positive gradient signals growth or increase, a negative gradient signals decline, and a zero gradient means no change at all. The diagram below compares four key cases side by side.
| Gradient (m) | Direction of Line | Real-World Example |
|---|---|---|
| m > 0 (positive) | Rises from left to right | Earning $15/hour: total pay rises with hours worked |
| m < 0 (negative) | Falls from left to right | A phone battery losing 10 % per hour |
| m = 0 | Horizontal line | A flat monthly subscription fee regardless of usage |
| Large |m| | Steep line | Rapid temperature drop during a cold front |
| Small |m| | Shallow line | Slow, steady erosion of a hillside over years |
Worked Example — Water Tank Model
A water tank is being filled at a constant rate. At time t = 0 minutes the tank already holds 20 litres of water. After 5 minutes, it holds 45 litres. Find the linear model that describes the volume V (in litres) as a function of time t (in minutes), and predict the volume after 12 minutes.
Strengths & Limitations of Linear Models
Linear models are beautifully simple, but simplicity is a double-edged sword. It's important to recognise when a straight line is a good fit for the data and when a more complex model (quadratic, exponential, etc.) would be more appropriate.
| Strengths | Limitations |
|---|---|
| Easy to create — only two data points are needed to define the line | Assumes a constant rate of change, which rarely holds over large ranges |
| Simple to interpret — the gradient and intercept have clear, intuitive meanings | Cannot model curves, acceleration, or saturation effects |
| Great for interpolation (predicting within the data range) | Extrapolation can be unreliable if the trend changes outside the observed range |
| Foundational — many advanced models (regression, calculus) build on linearity | Real-world constraints (e.g., capacity limits) may make the model unrealistic |
Connection to Advanced Models
Linear functions are the first step on a ladder of increasingly powerful models you'll encounter in IB Maths. Understanding how linear relates to what comes next helps you see the bigger picture and prepares you for SL 2.5 (modelling) and later HL topics.
| Feature | Linear Model (SL 2.2) | Beyond Linear (SL 2.5+) |
|---|---|---|
| Equation form | y = mx + c | y = ax² + bx + c (quadratic), y = abˣ (exponential), etc. |
| Rate of change | Constant — same everywhere | Variable — changes depending on x |
| Graph shape | Straight line | Parabola, curve, exponential growth/decay |
| Typical use | Constant-speed motion, fixed-rate billing | Projectile motion, population growth, radioactive decay |
| Number of parameters | 2 (m and c) | 3 or more, allowing more flexible curve shapes |
In calculus, you'll learn that the gradient of any smooth curve at a single point is found by zooming in until the curve looks like a straight line — a tangent line. So even when the world isn't linear, linear thinking is still the tool you use to analyse it locally. Mastering SL 2.2 now gives you a head start on those more advanced ideas.
Practice Problems
Lesson Summary
A linear function has the form y = mx + c, where m (the gradient) represents the constant rate of change and c (the y-intercept) is the output value when x = 0. The gradient is calculated using m = (y₂ − y₁) / (x₂ − x₁) and can be positive (line rises), negative (line falls), or zero (horizontal line).
Linear models are powerful for situations with constant rates — such as fixed-speed travel, hourly wages, or steady filling of a tank — but have limitations when the rate of change is not truly constant. Always interpret m and c in context for full IB marks, and question whether extrapolation beyond the given data is reasonable. Mastering these ideas prepares you for quadratic, exponential, and other non-linear models later in the course.