For the graph , state the -intercept and the horizontal asymptote.
Exponentials and logarithms
Notes and three levels of exam-style practice for each registered specification leaf in this section.
Open the printable packKnow and use the function aˣ and its graph, where a is positive; know and use the function eˣ and its graph.
- For with , the exponential function has domain , range , horizontal asymptote and intercept .
- If the graph is increasing, while if it is decreasing; gives the constant function .
- The natural exponential follows the same graph facts with base , and transformations such as scale, reflect and translate the basic graph.
- An exponential graph never reaches its horizontal asymptote; treating as instead of is a common error.
Tier 1 · Easy
Tier 2 · Standard
For , state the domain, range, horizontal asymptote and -intercept.
Tier 3 · Hard
The curve has a minimum point. Prove that its minimum value is and find the corresponding value of .
Know that the gradient of e^(kx) is equal to k·e^(kx) and hence understand why the exponential model is suitable in many applications.
- Differentiating gives , so the gradient of an exponential is a constant multiple of the function itself.
- For , the rate satisfies : positive models growth and negative models decay.
- A tangent at uses the point and gradient in .
- Exponential modelling assumes a rate proportional to the current amount and a constant proportionality factor; changing conditions can make that assumption unsuitable.
Tier 1 · Easy
Differentiate with respect to .
Tier 2 · Standard
Find the equation of the tangent to at .
Tier 3 · Hard
A culture is modelled by , where is in hours. Find its instantaneous growth rate when , and explain the feature of the model that makes this calculation direct.
Know and use the definition of logₐx as the inverse of aˣ, where a is positive and x ≥ 0; know and use the function ln x and its graph; know and use ln x as the inverse function of eˣ.
- For with , means exactly that ; a real logarithm requires .
- The graph of is the reflection of in , so its domain is , range is and vertical asymptote is .
- Natural logarithms use base , giving the inverse relations for every real and for .
- The specification's inverse relationship does not make defined: approaches zero but never equals it, so logarithm arguments must be strictly positive.
Tier 1 · Easy
Solve exactly.
Tier 2 · Standard
The function . Find and state the domain of the inverse.
Tier 3 · Hard
For , determine the domain and range, and identify the maximum point.
Understand and use the laws of logarithms: logₐx + logₐy = logₐ(xy); logₐx − logₐy = logₐ(x/y); k logₐx = logₐxᵏ (including, for example, k = −1 and k = −½).
- For positive arguments, , and .
- To expand a logarithm, turn products into sums, quotients into differences and powers into coefficients; to condense, apply those steps in reverse.
- A negative coefficient represents a reciprocal power, for example .
- The laws combine logarithms, not their arguments: cannot be split into , and every original logarithm argument must remain positive.
Tier 1 · Easy
Write as a single logarithm.
Tier 2 · Standard
Expand , where , and are positive.
Tier 3 · Hard
Solve , checking the domain of the original equation.
Solve equations of the form aˣ = b.
- For , and , taking logarithms gives .
- If the exponent is linear, isolate the exponential or take logarithms first, then solve the resulting linear equation without rounding intermediate values.
- When both and occur, substitute , so , and solve the resulting algebraic equation before converting back.
- A positive-base exponential cannot equal zero or a negative number; after a substitution, reject non-positive values because is always positive.
Tier 1 · Easy
Solve , giving to decimal places.
Tier 2 · Standard
Find the solution of , giving to decimal places.
Tier 3 · Hard
Determine all real solutions of .
Use logarithmic graphs to estimate parameters in relationships of the form y = axⁿ and y = kbˣ, given data for x and y.
- For , taking logarithms gives , so a plot of against has gradient and intercept .
- For , , so a plot of against has gradient and intercept .
- Read two well-separated points from the fitted line, calculate its gradient and intercept, then undo the chosen logarithm base to recover , or .
- The transformed coordinates and the logarithm base must be identified: confusing a log-log graph with a semi-log graph, or forgetting to exponentiate the intercept, gives incorrect parameters.
Tier 1 · Easy
A straight-line fit on base- logarithmic axes has equation . State the corresponding model , giving to significant figures.
Tier 2 · Standard
For a model , a fitted graph of against passes through and . Estimate and , then predict when .
Tier 3 · Hard
An exponential relationship is analysed by plotting against . The fitted line goes through and . Find and to significant figures, and estimate at .
Understand and use exponential growth and decay; use in modelling (e.g. compound interest, radioactive decay, drug concentration decay, population growth); consideration of limitations and refinements of exponential models.
- Repeated percentage change over whole periods uses , while continuous change at a rate proportional to the amount uses .
- For decay ; the half-life in is , independent of the starting amount.
- Use two observations to determine an unknown multiplier or exponent, preserve full precision, and interpret a calculated time or amount in the units and practical setting of the model.
- Unlimited exponential growth and a constant decay parameter may fail when resources, competition, dosage cycles or environmental conditions change; a carrying-capacity or piecewise model can be a refinement.
Tier 1 · Easy
A savings balance of earns compound interest each year. Find the balance after years, to the nearest penny.
Tier 2 · Standard
A medicine concentration is modelled by , where is in and is in hours. Calculate the half-life and the concentration after hours, each to significant figures.
Tier 3 · Hard
A colony is modelled by . Its measured population at days is . Find , predict when the model first reaches , and state one limitation with a suitable refinement.