The gradient of a curve is constantly changing, and so finding the gradient is not as simple as reading the x-coefficient of the equation. Instead, we can draw tangents to the curve to calculate the gradient at the point where the tangent touches the curve.
The gradient of a curves at a given point is given as the gradient of the tangent to the curve at that point
Finding the Derivative
However, drawing tangents is not very accurate when done by eye. Instead, we can use algebra to find the gradient of a curve at a given point.
This works by drawing a cord connecting two points on the curve, y = f(x). The gradient of this cord gives an estimate for the average gradient of the curve between the two points. As you can see, the closer the two points are together, the more accurate the gradient of of the cord is as an estimation. This is because the cord gets closer and closer to being parallel to the tangent.
This can be noted as a cord between points A and B, where the horizontal distance between the points (difference in x-values) is h. Therefore, the vertical distance (difference in y-values) is f(x₀+h) - f(x₀):
Since gradient is defined as change in y-value over change in x-value (rise over run), the gradient of the cord is given as:
As the value for h gets smaller, points A and B get closer together, and the gradient of the cord becomes a better estimation for the gradient of the curve at A.
As h → 0, the gradient of the cord is identical to the gradient of the curve at the point.
This is known as differentiation from first principles:
The derivative of a function is the differentiated form of the function
The derivative of f(x) can be noted as f'(x)
The derivative of y = f(x) can be noted as dy/dx
h → 0 means "as h tends to zero" - its value becomes negligible
You do not have to differentiate from first principles every time - it is only used as a proof. Instead, we use general rules to make differentiation quicker. To differentiate xⁿ:
multiply by the power, n
reduce the power by one, n becomes n-1
If f(x) = xⁿ, then f'(x) = nxⁿ‾¹
If f(x) = axⁿ, then f'(x) = anxⁿ‾¹
This applies when n is real, and a is a constant
For functions with more than one term, you differentiate one term at a time, using the rules above. A quadratic is a function where the highest power of x is x², and can be differentiated as such:
If f(x) is ax² + bx + c, then f'(x) = 2ax + b
This applies when a, b and c are constants and x is the only variable
Note that the derivative is now linear, because all x-terms were reduces by one order. c has disappeared all together, because it can be seen as cx⁰, so when c is multiplied by the power of x, 0, it becomes 0.
The exact same rules apply for all functions: differentiate each term individually.
If f(x) = ax³ + bx² + cx + d, then f'(x) = 3ax² + 2bx + c
This applies when a, b, c and d are constants and x is the only variable
Note that the cubic becomes a quadratic when differentiated.
Second Order Derivatives
The first order derivative is the rate of change of the function, or the gradient. The second order derivative is the rate of change of the gradient itself, or the rate of change of the rate of change of the function.
It is noted as f''(x) or d²y/dx²
This sounds complicated, but all it means is you differentiate a function twice:
If f(x) = ax³ + bx² + cx + d:
f'(x) = 3ax² + 2bx + c
f''(x) = 6ax + 2b
As well as second order derivatives, you can have third, fourth fifth etc order derivatives. However, these are rarely encountered at this stage.
Gradients, Tangents & Normals
Often, differentiation is used to find the equation of a tangent or normal to a curve at a given point.