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Specification
The last January exams for AS and A2 were in January 2013.
The exams are now only in June due to Changes to Alevels.
 Specification for exams from 2014 (3.1 MB)
16 Further Pure 1
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Core 1 and Core 2.
Candidates will also be expected to know for section 16.6 that the roots of an equation can be located by considering changes of sign of in an interval of in which is continuous.
Candidates may use relevant formulae included in the formulae booklet without proof.
16.1 Algebra and Graphs
Graphs of rational functions
of the form.
or 
Sketching the graphs. Finding the equations of the asymptotes which will always be parallel to the coordinate axes. Finding points of intersection with the coordinate axes or other straight lines. Solving associated inequalities. Using quadratic theory (not calculus) to find the possible values of the function and the coordinates of the maximum or minimum points on the graph. Eg for which has real roots if , ie if ; stationary points are and 
Graphs of parabolas, ellipses
and hyperbolas with
equations
and 
Sketching the graphs. Finding points of intersection with the coordinate axes or other straight lines. Candidates will be expected to interpret the geometrical implication of equal roots, distinct real roots or no real roots. Knowledge of the effects on these equations of single transformations of these graphs involving translations, stretches parallel to the axis or axis, and reflections in the line . Including the use of the equations of the asymptotes of the hyperbolas given in the formulae booklet. 
16.2 Complex Numbers
Nonreal roots of quadratic equations.  Complex conjugates – awareness that nonreal roots of quadratic equations with real coefficients occur in conjugate pairs. 
Sum, difference and product of complex numbers in the form  
Comparing real and imaginary parts. 
Including solving equations eg where
is the conjugate of . 
16.3 Roots and coefficients of a quadratic equation
Manipulating expressions involving and . 
Eg 
Forming an equation with roots or etc. 
16.4 Series
Use of formulae for the sum of the squares and the sum of the cubes of the natural numbers.  Eg to find a polynomial expression for
or 
16.5 Calculus
Finding the gradient of the tangent to a curve at a point, by taking the limit as tends to zero of the gradient of a chord joining two points whose coordinates differ by .  The equation will be given as , where is a simple polynomial such as . 
Evaluation of simple improper integrals.  E.g. 
16.6 Numerical Methods
Finding roots of equations by interval bisection, linear interpolation and the NewtonRaphson method.  Graphical illustration of these methods. 
Solving differential equations of the form  Using a stepbystep method based on the linear approximations with given values for and . 
Reducing a relation to a linear law.  E.g. 
Use of logarithms to base 10 where appropriate.  
Given numerical values of , drawing a linear graph and using it to estimate the values of the unknown constants. 
16.7 Trigonometry
General solutions of trigonometric equations including use of exact values for the sine, cosine and tangent of  Eg , 
16.8 Matrices and Transformations
and matrices;
addition and subtraction,
multiplication by a scalar.
Multiplying a matrix by
a matrix or by a
matrix.
The identity matrix for a matrix. 

Transformations of points in the plane represented by matrices.  Transformations will be restricted to rotations about the origin,
reflections in a line through the origin, stretches parallel to the axis
and axis, and enlargements with centre the origin.
Use of the standard transformation matrices given in the formulae booklet. Combinations of these transformations e.g. 
17 Further Pure 2
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Core 1, Core 2, Core 3, Core 4 and Further Pure 1.
Candidates may use relevant formulae included in the formulae booklet without proof except where proof is required in this module and requested in a question.
17.1 Roots of Polynomials
The relations between the roots and the coefficients of a polynomial equation; the occurrence of the nonreal roots in conjugate pairs when the coefficients of the polynomial are real. 
17.2 Complex Numbers
The Cartesian and polar coordinate forms of a complex number, its modulus, argument and conjugate.  and . 
The sum, difference, product and quotient of two complex numbers.  The parts of this topic also included in module Further Pure 1 will be examined only in the context of the content of this module. 
The representation of a complex number by a point on an Argand diagram; geometrical illustrations.  
Simple loci in the complex plane. 
For example, Maximum level of difficulty where and are complex numbers. 
17.3 De Moivre's Theorem
De Moivre's theorem for integral . 
Use of and , leading to,
for example, expressing in terms of multiple angles and in term of powers of .
Applications in evaluating integrals, for example,. 
De Moivre's theorem; the roots of unity, the exponential form of a complex number.  The use, without justification, of the identity 
Solutions of equations of the form  To include geometric interpretation and use, for example, in expressing in surd form. 
17.4 Proof by Induction
Applications to sequences and series, and other problems.  Eg proving that is divisible by 6, or where n is a positive integer. 
17.5 Finite Series
Summation of a finite series by any method such as induction, partial fractions or differencing.  Eg 
17.6 The calculus of inverse trigonometrical functions
Use of the derivatives of
as given in the
formulae booklet.
To include the use of the standard integrals given in the formulae booklet. 
17.7 Hyperbolic Functions
Hyperbolic and inverse hyperbolic functions and their derivatives; applications to integration. 
The proofs mentioned below require expressing hyperbolic functions in terms of exponential functions.
To include solution of equations of the form . Use of basic definitions in proving simple identities.Maximum level of difficulty: . The logarithmic forms of the inverse functions, given in the formulae booklet, may be required. Proofs of these results may also be required.Proofs of the results of differentiation of the hyperbolic functions, given in the formula booklet, are included. Knowledge, proof and use of:
Familiarity with the graphs of . 
17.8 Arc length and Area of surface of revolution about the xaxis
Calculation of the arc length of a curve and the area of a surface of revolution using Cartesian or parametric coordinates. 
Use of the following formulae will be expected:

18 Further Pure 3
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Core 1, Core 2, Core 3, Core 4 and Further Pure 1.
Candidates may use relevant formulae included in the formulae booklet without proof.
18.1 Series and Limits
Maclaurin series  
Expansions of , and , and for rational values of 
Use of the range of values of for which these expansions are valid, as given in the formulae booklet, is expected to determine the range of values for which expansions of related functions are valid;
eg. . 
Knowledge and use, for , of as tends to infinity and as tends to zero.  
Improper integrals. 
E.g.. Candidates will be expected to show the limiting processes used. 
Use of series expansion to find limits.  E.g. 
18.2 Polar Coordinates
Relationship between polar and Cartesian coordinates.  The convention wil be used. The sketching of curves given by equations of the form may be required. Knowledge of the formula is not required. 
Use of the formula . 
18.3 Differential Equations
The concept of a differential equation and its order.  The relationship of order to the number of arbitrary constants in the general solution will be expected. 
Boundary values and initial conditions, general solutions and particular solutions. 
18.4 Differential Equations  First Order
Analytical solution of first order linear differential equations of the form where and are functions of . 
To include use of an integrating factor and solution by complementary function and particular integral. 
Numerical methods for the solution of differential eqations of the form . 

Euler's formula and extensions to second order methods for this first order differential equation.  Formulae to be used will be stated explicitly in questions, but candidates should be familiar with standard notation such as used in
Euler's formula the formula , and the formula where and . 
18.5 Differential Equations  Second Order
Solution of differential equations of the form
, where , and are integers, by using an auxiliary equation whose roots may be real or complex. 
Including repeated roots. 
Solution of equations of the form

Finding particular integrals will be restricted to cases where is of the form or a polynomial of degree at most 4, or a linear combination of any of the above. 
Solutions of differential equations of the form:
where and are functions of . A substitution will always be given which reduces the differential equation to a form which can be directly solved using the other analytical methods in 18.4 and 18.5 of this specification or by separating variables. 
Level or difficulty as indicated by:
(a) Given use the substitution
to show that .
(b) use the subsitution to show that and hence that , where is an arbitrary constant. Hence find in terms of . 
19 Further Pure 4
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Core 1, Core 2, Core 3, Core 4 and Further Pure 1.
Candidates may use relevant formulae included in the formulae booklet without proof.
19.1 Vectors and ThreeDimensional Coordinate Geometry
Definition and properties of the vector product. Calculation of vector products. 
Including the use of vector products in the calculation of the area of a triangle or parallelogram. 
Calculation of scalar triple products.  Including the use of the scalar triple product in the calculation of the volume of a parallelepiped and in identifying coplanar vectors. Proof of the distributive law and knowledge of particular formulae is not required. 
Applications of vectors to two and threedimensional geometry, involving points, lines and planes.  Including the equation of a line in the form .
Vector equation of a plane in the form or . Intersection of a line and a plane. Angle between a line and a plane and between two planes. 
Cartesian coordinate geometry of lines and planes. Direction ratios and direction cosines.  To include finding the equation of the line of intersection of two nonparallel planes.
Including the use of where are the direction cosines. Knowledge of formulae other than those in the formulae booklet will not be expected. 
19.2 Matrix Algebra
Matrix algebra of up to 3 x 3 matrices, including the inverse of a 2 x 2 or 3 x 3 matrix. 
Including nonsquare matrices and use of the results and Singular and nonsingular matrices. 
The identity matrix for 2 x 2 and 3 x 3 matrices. 

Matrix transformations in two dimensions: shears. 
Candidates will be expected to recognise the matrix for a shear parallel to the or axis. Where the line of invariant points is not the or axis candidates will be informed that the matrix represents a shear. The combination of a shear with a matrix transformation from MFP1 is included. 
Rotations, reflections and enlargements in three dimensions, and combinations of these. 
Rotations about the coordinate axes only.

Invariant points and invariant lines. 

Eigenvalues and eigenvectors of 2 x 2 and 3 x 3 matrices.

Characteristic equations. Real eigenvalues only. Repeated eigenvalues may be included. 
Diagonalisation of 2 x 2 and 3 x 3 matrices. 
where is diagonal matrix featuring the eigenvalues and is a matrix whose columns are the eigenvectors.

19.3 Solution of Linear Equations
Consideration of up to three linear equations in up to three unknowns.

Any method of solution is acceptable. 
19.4 Determinants
Second order and third order determinants, and their manipulation.  Including the use of the result , but a general treatment of products is not required. 
Factorisation of determinants.  Using row and/or column operations or other suitable methods. 
Calculation of area and volume scale factors for transformation representing enlargements in two and three dimensions. 
19.5 Linear Independence
Linear independence and dependence vectors. 
20 Statistics 1
Introduction
Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formula, which is not included in the formulae booklet, but which may be required to answer questions.
20.1 Numerical Measures
Standard deviation and variance calculated on ungrouped and grouped data.  Where raw data are given, candidates will be expected to be able to obtain standard deviation and mean values directly from calculators. Where summarised data are given, candidates may be required to use the formula from the booklet provided for the examination. It is advisable for candidates to know whether to divide by when calculating the variance; either divisor will be accepted unless a question specifically requests an unbiased estimate of a population variance. 
Linear scaling.  Artificial questions requiring linear scaling will not be set, but candidates should be aware of the effect of linear scaling on numerical measures. 
Choice of numerical measures.  Candidates will be expected to be able to choose numerical measures, including mean, median, mode, range and interquartile range, appropriate to given contexts. Linear interpolation will not be required. 
20.2 Probability
Elementary probability; the concept of a random event and its probability.  Assigning probabilities to events using relative frequencies or equally likely outcomes. Candidates will be expected to understand set notation but its use will not be essential. 
Addition law of probability. Mutually exclusive events.  ; two events only.
; two or more events. . 
Multiplication law of probability and conditional probability. Independent events.  ; two or more events.
; two or more events. 
Application of probability laws.  Only simple problems will be set that can be solved by direct application of the probability laws, by counting equally likely outcomes and/or the construction and the use of frequency tables or relative frequency (probability) tables. Questions requiring the use of tree diagrams or Venn diagrams will not be set, but their use will be permitted. 
20.3 Binomial Distribution
Discrete random variables.  Only an understanding of the concepts; not examined beyond binomial distributions. 
Conditions for application of a binomial distribution.  
Calculation of probabilities using formula.  Use of notation. 
Use of tables.  
Mean, variance and standard deviation of a binomial distribution.  Knowledge, but not derivations, will be required. 
20.4 Normal Distribution
Continuous random variables.  Only an understanding of the concepts; not examined beyond normal distributions. 
Properties of normal distributions.  Shape, symmetry and area properties. Knowledge that approximately of observations lie within and equivalent results. 
Calculation of probabilities.  Transformation to the standardised normal distribution and use of the supplied tables. Interpolation will not be essential; rounding − values to two decimal places will be accepted. 
Mean, variance and standard deviation of a normal distribution.  To include finding unknown mean and/or standard deviation by making use of the table of percentage points. (Candidates may be required to solve two simultaneous equations.) 
20.5 Estimation
Population and sample.  To include the terms ‘parameter’ and ‘statistic’. Candidates will be expected to understand the concept of a simple random sample. Methods for obtaining simple random samples will not be tested directly in the written examination. 
Unbiased estimators of a population mean and variance.  respectively. 
The sampling distribution of the mean of a random sample from a normal distribution.  To include the standard error of the sample mean, , and its estimator, . 
A normal distribution as an approximation to the sampling distribution of the mean of a large sample from any distribution.  Knowledge and application of the Central Limit Theorem. 
Confidence intervals for the mean of a normal distribution with known variance.  Only confidence intervals symmetrical about the mean will be required. 
Confidence intervals for the mean of a distribution using a normal approximation.  Large samples only. Known and unknown variance. 
Inferences from confidence intervals.  Based on whether a calculated confidence interval includes or does not include a ’hypothesised’ mean value. 
20.6 Correlation and Regression
Calculation and interpretation of the product moment correlation coefficient.  Where raw data are given, candidates should be encouraged to obtain correlation coefficient values directly from calculators. Where summarised data are given, candidates may be required to use a formula from the booklet provided for the examination. Calculations from grouped data are excluded. Importance of checking for approximate linear relationship but no hypothesis tests. Understanding that association does not necessarily imply cause and effect.  
Identification of response (dependent) and explanatory (independent) variables in regression.  
Calculation of least squares regression lines with one explanatory variable. Scatter diagrams and drawing a regression line theorem.  Where raw data are given, candidates should be encouraged to obtain gradient and intercept values directly from calculators. Where summarised data are given, candidates may be required to use formulae from the booklet provided for the examination. Practical interpretation of values for the gradient and intercept. Use of line for prediction within range of observed values of explanatory variable. Appreciation of the dangers of extrapolation.  
Calculation of residuals.  Use of . Examination of residuals to check plausibility of model and to identify outliers. Appreciation of the possible large influence of outliers on the fitted line.  
Linear scaling.  Artificial questions requiring linear scaling will not be set, but candidates should be aware of the effect of linear scaling in correlation and regression. 
21 Statistics 2
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the module Statistics 1 and Core 1.
Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
and
and
and
Yates’ correction (for table) is
21.1 Discrete Random Variables
Discrete random variables and their associated probability distributions.  The number of possible outcomes will be finite. Distributions will be given or easily determined in the form of a table or simple function. 
Mean, variance and standard deviation.  Knowledge of the formulae
and will be expected. 
Mean, variance and standard deviation of a simple function of a discrete random variable. 
Eg Eg . 
21.2 Poisson Distribution
Conditions for application of a Poisson distribution.  
Calculation of probabilities using formula.  To include calculation of values of from a calculator. 
Use of Tables.  
Mean, variance and standard deviation of a Poisson distribution.  Knowledge, but not derivations, will be required. 
Distribution of sum of independent Poisson distributions.  Result, not proof. 
21.3 Continuous Random Variables
Differences from discrete random variables.  
Probability density functions, cumulative distribution functions and their relationship. 
and . Polynomial integration only. 
The probability of an observation lying in a specified interval.  and . 
Median, quartiles and percentiles.  
Mean, variance and standard deviation. 
Knowledge of the formulae
and
will be expected. 
Mean, variance and standard deviation of a simple function of a continuous random variable. 
Eg . Eg . 
Rectangular distribution.  Calculation of probabilities, proofs of mean, variance and standard deviation. 
21.4 Estimation
Confidence intervals for the mean of a normal distribution with unknown variance. 
Using a distribution. Only confidence intervals symmetrical about the mean will be required. Questions may involve a knowledge of confidence intervals from the module Statistics 1.

21.5 Hypothesis Testing
Null and alternative hypotheses.  The null hypothesis to be of the form that a parameter takes a specified value. 
One tailed and two tailed tests, significance level, critical value, critical region, acceptance region, test statistic, and errors.  The concepts of and should be understood but questions which require the calculation of the risk of a will not be set. The significance level to be used in a hypothesis test will usually be given. 
Tests for the mean of a normal distribution with known variance.  Using a statistic. 
Tests for the mean of a normal distribution with unknown variance.  Using a statistic. 
Tests for the mean of a distribution using a normal approximation.  Large samples only. Known and unknown variance. 
21.6 ChiSquared Contingency Table Tests
Introduction to distribution.  To include use of the supplied tables. 
Use of as an approximate statistic.  
Conditions for approximation to be valid.  The convention that all should be greater than 5 will be expected. 
Test for independence in contingency tables.  Use of Yates' correction for table will be required. 
22 Statistics 3
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Statistics 1 and 2 and Core 1 and 2.
Candidate may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
For independently distributed , then
is distributed
Power = 1 – P(Type error)
22.1 Further Probability
Bayes’ Theorem.  Knowledge and application to at most three events.
The construction and use of tree diagrams 
22.2 Linear Combinations of Random Variables
Mean, variance and standard deviation of a linear combination of two (discrete or continuous) random variables.  To include covariance and correlation. Implications of independence. Applications, rather than proofs, will be required. 
Mean, variance and standard deviation of a linear combination of independent (discrete or continuous) random variables.  Use of these, rather than proofs, will be required. 
Linear combinations of independent normal random variables.  Use of these only. 
22.3 Distributional Approximations
Mean, variance and standard deviation of binomial and Poisson distributions.  Proofs using and together with . 
A Poisson distribution as an approximation to a binomial distribution.  Conditions for use. 
A normal distribution as an approximation to a binomial distribution.  Conditions for use. Knowledge and use of continuity corrections. 
A normal distribution as an approximation to a Poisson distribution.  Conditions for use. Knowledge and use of continuity corrections. 
22.4 Estimation
Estimation of sample sizes necessary to achieve confidence intervals of a required width with a given level of confidence.  Questions may be set based on a knowledge of confidence intervals from the module Statistics 1. 
Confidence intervals for the difference between the means of two independent normal distributions with known variances.  Symmetric intervals only. Using a normal distribution. 
Confidence intervals for the difference between the means of two independent distributions using normal approximations.  Large samples only. Known and unknown variances. 
The mean, variance and standard deviation of a sample proportion.  
Unbiased estimator of a population proportion.  
A normal distribution as an approximation to the sampling distribution of a sample proportion based on a large sample.  
Approximate confidence intervals for a population proportion and for the mean of a Poisson distribution.  Using normal approximations. The use of a continuity correction will not be required in these cases. 
Approximate confidence intervals for the difference between two population proportions and for the difference between the means of two Poisson distributions.  Using normal approximations. The use of continuity corrections will not be required in these cases. 
22.5 Hypothesis Testing
The notion of the power of a test.  Candidates may be asked to calculate the probability of a Type error or the power for a simple alternative hypothesis of a specific test, but they will not be asked to derive a power function. Questions may be set which require the calculation of a statistic using knowledge from the module Statistics 1. The significance level to be used in a hypothesis test will usually be given. 
Tests for the difference between the means of two independent normal distributions with known variances.  Using a statistic. 
Tests for the difference between the means of two independent distributions using normal approximations.  Large samples only. Known and unknown variances. 
Tests for a population proportion and for the mean of a Poisson distribution.  Using exact probabilities or, when appropriate, normal approximations where a continuity correction will not be required. 
Tests for the difference between two population proportions and for the difference between the means of two Poisson distributions.  Using normal approximations where continuity corrections will not be required. In cases where the null hypothesis is testing an equality, a pooling of variances will be expected. 
Use of the supplied tables to test for a bivariate normal population.  Where denotes the population product moment correlation coefficient. 
23 Statistics 4
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Statistics 1 and Statistics 2 and Core 1, Core 2 and Core 3.
Those candidates who have not studied the module Statistics 3 will also require knowledge of the mean, variance and standard deviation of a difference between two independent normal random variables.
Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
For an exponential distribution,
Efficiency of relative to
23.1 Geometric and Exponential Distributions
Conditions for application of a geometric distribution.  
Calculation of probabilities for a geometric distribution using formula.  
Mean, variance and standard deviation of a geometric distribution.  Knowledge and derivations will be expected. 
Conditions for application of an exponential distribution.  Knowledge that lengths of intervals between Poisson events have an exponential distribution. 
Calculation of probabilities for an exponential distribution.  Using cumulative distribution function or integration of probability density function. 
Mean, variance and standard deviation of an exponential distribution.  Knowledge and derivations will be expected. 
23.2 Estimators
Review of the concepts of a sample statistic and its sampling distribution, and of a population parameter.  
Estimators and estimates.  
Properties of estimators.  Unbiasedness, consistency, relative efficiency.
Mean and variance of pooled estimators of means and proportions. Proof that . 
23.3 Estimation
Confidence intervals for the difference between the means of two normal distributions with unknown variances. 
Independent and paired samples. For independent samples, only when the population variances may be assumed equal so that a pooled estimate of variance may be calculated. Small samples only. Using a distribution. 
Confidence intervals for a normal population variance (or standard deviation) based on a random sample.  Using a distribution. 
Confidence intervals for the ratio of two normal population variances (or standard deviations) based on independent random samples. 
Introduction to distribution. To include use of the supplied tables. Using an distribution. 
23.4 Hypothesis Testing
The significance level to be used in a hypothesis test will usually be given.  
Tests for the difference between the means of two normal distributions with unknown variances.  Independent and paired samples. For independent samples, only when the population variances may be assumed equal so that a pooled estimate of variance may be calculated. Small samples only. Using a statistic. 
Tests for a normal population variance (or standard deviation) based on a random sample.  Using a statistic. 
Tests for the ratio of two normal population variances (or standard deviations) based on independent random samples.  Using a statistic. 
23.5 ChiSquared Goodness of Fit Tests
Use of as an
approximate statistic. 

Conditions for approximation to be valid.  The convention that all should be greater than 5 will be expected. 
Goodness of fit tests.  Discrete probabilities based on either a discrete or a continuous distribution. Questions may be set based on a knowledge of discrete or continuous random variables from the module Statistics 2. Integration may be required for continuous random variables. 
24 Mechanics 1
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the modules Core 1 and Core 2. Candidates may use relevant formulae included in the formulae booklet without proof. Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions. 

Constant Acceleration Formulae 
 
Weight  
Momentum  
Newton's Second Law  or Force = rate of change of momentum  
Friction, dynamic  
Friction, static 
24.1 Mathematical Modelling
Use of assumptions in simplifying reality.  Candidates are expected to use mathematical models to solve problems. 
Mathematical analysis of models.  Modelling will include the appreciation that:
it is appropriate at times to treat relatively large moving bodies as point masses; the friction law is experimental. 
Interpretation and validity of models.  Candidates should be able to comment on the modelling assumptions made when using terms such as particle, light, inextensible string, smooth surface and motion under gravity. 
Refinement and extension of models. 
24.2 Kinematics in One and Two Dimensions
Displacement, speed, velocity, acceleration.  Understanding the difference between displacement and distance.  
Sketching and interpreting kinematics graphs.  Use of gradients and area under graphs to solve problems.  
Use of constant acceleration equations 
 
Vertical motion under gravity.  
Average speed and average velocity.  
Application of vectors in two dimensions to represent position, velocity or acceleration.  Resolving quantities into two perpendicular components.  
Use of unit vectors and .  Candidates may work with column vectors.  
Magnitude and direction of quantities represented by a vector.  
Finding position, velocity, speed and acceleration of a particle moving in two dimensions with constant acceleration.  The solution of problems such as when a particle is at a specified position or velocity, or finding position, velocity or acceleration at a specified time.
Use of constant acceleration equations in vector form, for example,
. 

Problems involving resultant velocities.  To include solutions using either vectors or vector triangles. 
24.3 Statics and Forces
Drawing force diagrams, identifying forces present and clearly labelling diagrams.  When drawing diagrams, candidates should distinguish clearly between forces and other quantities such as velocity. 
Force of gravity (Newton's Universal Law not required).  The acceleration due to gravity,, will be taken as . 
Friction, limiting friction, coefficient of friction and the relationship of .  Candidates should be able to derive and work with inequalities from the relationship . 
Normal reaction forces.  
Tensions in strings and rods, thrusts in rods.  
Modelling forces as vectors.  Candidates will be required to resolve forces only in two dimensions. 
Finding the resultant of a number of forces acting at a point  Candidates will be expected to express the resultant using components of a vector and to find the magnitude and direction of the resultant. 
Finding the resultant force acting on a particle.  
Knowledge that the resultant force is zero if a body is in equilibrium  Find unknown forces on bodies that are at rest. 
24.4 Momentum
Concept of momentum  Momentum as a vector in one or two dimensions. (Resolving velocities is not required.) Momentum 
The principle of conservation of momentum applied to two particles.  Knowledge of Newton’s law of restitution is not required. 
24.5 Newton's Laws of Motion.
Newton’s three laws of motion.  Problems may be set in one or two dimensions 
Simple applications of the above to the linear motion of a particle of constant mass.  Including a particle moving up or down an inclined plane. 
Use of as a model for dynamic friction. 
24.6 Connected Particles
Connected particle problems.  To include the motion of two particles connected by a light inextensible string passing over a smooth fixed peg or a smooth light pulley, when the forces on each particle are constant. Also includes other connected particle problems, such as a car and trailer. 
24.7 Projectiles
Motion of a particle under gravity in two dimensions.  Candidates will be expected to state and use equations of the form and . Candidates should be aware of any assumptions they are making. 
Calculate range, time of flight and maximum height.  Formulae for the range, time of flight and maximum height should not be quoted in examinations. Inclined plane and problems involving resistance will not be set. The use of the identity will not be required.
Candidates may be expected to find initial speeds or angles of projection. 
Modification of equations to take account of the height of release. 
25 Mechanics 2
Introduction
A knowledge of the topics and associated formulae from Modules Core 1 – Core 4, and Mechanics 1 is required. Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
Centres of Mass  and 
Circular Motion  and 
Work and Energy  Work done, constant force: Work done, variable force in direction of motion in a straight line: Gravitational Potential Energy Kinetic Energy Elastic potential energy 
Hooke’s Law 
25.1 Mathematical Modelling
The application of mathematical modelling to situations that relate to the topics covered in this module. 
25.2 Moments and Centres of Mass
Finding the moment of a force about a given point.  Knowledge that when a rigid body is in equilibrium, the resultant force and the resultant moment are both zero. 
Determining the forces acting on a rigid body when in equilibrium.  This will include situations where all the forces are parallel, as on a horizontal beam or where the forces act in two dimensions, as on a ladder leaning against a wall. 
Centres of Mass.  Integration methods are not required. 
Finding centres of mass by symmetry (eg for circle, rectangle).  
Finding the centre of mass of a system of particles.  Centre of mass of a system of particles is given by where and 
Finding the centre of mass of a composite body.  
Finding the position of a body when suspended from a given point and in equilibrium. 
25.3 Kinematics
Relationship between position, velocity and acceleration in one, two or three dimensions, involving variable acceleration.  Application of calculus techniques will be required to solve problems. 
Finding position, velocity and acceleration vectors, by the differentiation or integration of , with respect to . 
If
then then Vectors may be expressed in the form as column 
25.4 Newton's Laws of Motion
Application of Newton’s laws to situations, with variable acceleration.  Problems will be posed in one, two or three dimensions and may require the use of integration or differentiation. 
25.5 Application of differential equations
Onedimensional problems where simple differential equations are formed as a result of the application of Newton’s second law. 
Use of for acceleration, to form simple differential equations, for example, . Use of . The use of is not required. Problems will require the use of the method of separation of variables. 
25.6 Uniform Circular Motion
Motion of a particle in a circle with constant speed.  Problems will involve either horizontal circles or situations, such as a satellite describing a circular orbit, where the gravitational force is towards the centre of the circle. 
Knowledge and use of the relationships and . 

Angular speed in radians
converted from other units such as revolutions per minute or time for one revolution. 
Use of the term angular speed. 
Position, velocity and acceleration vectors in relation to circular motion in terms of and .  Candidates may be required to show that motion is circular by showing that the body is at a constant distance from a given point. 
Conical pendulum. 
25.7 Work and Energy
Work done by a constant force.  Forces may or may not act in the direction of motion. Work done 
Gravitational potential energy.  Universal law of gravitation will not be required. Gravitational Potential Energy 
Kinetic energy.  Kinetic Energy 
The workenergy principle.  Use of Work Done = Change in Kinetic Energy. 
Conservation of mechanical energy.  Solution of problems using conservation of energy. Onedimensional problems only for variable forces. 
Work done by a variable force.  Use of will only be used for elastic strings and springs. 
Hooke’s law.  . 
Elastic potential energy for strings and springs.  Candidates will be expected to quote the formula for elastic potential energy unless explicitly asked to derive it. 
Power, as the rate at which a force does work, and the relationship . 
25.8 Vertical Circular Motion
Circular motion in a vertical plane.  Includes conditions to complete vertical circles. 
26 Mechanics 3
Introduction
A knowledge of the topics and associated formulae from Modules Mechanics 1, Core 1 and Core 2 is required. A knowledge of the trigonometric identity is also required. Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
Momentum and Collision26.1 Relative Motion
Relative velocity. Use of relative velocity and initial conditions to find relative displacement. 
Velocities may be expressed in the form or as column vectors. 
Interception and closest approach. 
Use of calculus or completing the square. Geometric approaches may be required. 
26.2 Dimensional Analysis
Finding dimensions of quantities. Prediction of formulae. Checks on working, using dimensional consistency. 
Finding the dimensions of quantities in terms of M, L and T. Using this method to predict the indices in proposed formulae, for example, for the period of a simple pendulum. Use dimensional analysis to find units, and as a check on working. 
26.3 Collisions in one dimension
Momentum. Impulse as change of momentum. 
Knowledge and use of the equation . 
Impulse as Force Time. Impulse as  Applied to explosions as well as collisions. 
Conservation of momentum. Newton’s Experimental Law. Coefficient of restitution. 
26.4 Collisions in two dimensions
Momentum as a vector.  
Impulse as a vector.  and will be required. 
Conservation of momentum in two dimensions.  
Coefficient of restitution and Newton’s experimental law.  
Impacts with a fixed surface.  The impact may be at any angle to the surface. Candidates may be asked to find the impulse on the body. Questions that require the use of trigonometric identities will not be set. 
Oblique Collisions  Collisions between two smooth spheres. Candidates will be expected to consider components of velocities parallel and perpendicular to the line of centres. 
26.5 Further Projectiles
Elimination of time from equations to derive the equation of the trajectory of a projectile  Candidates will not be required to know the formula , but should be able to derive it when needed. The identity will be required. 
26.6 Projectiles on Inclined Planes
Projectiles launched onto inclined planes. 
Problems will be set on projectiles that are launched and land on an inclined plane. Candidates may approach these problems by resolving the acceleration parallel and perpendicular to the plane. Questions may be set which require the use of trigonometric identities, but any identities which are needed, apart from and those in Core 2, will be given in the examination paper. Candidates will be expected to find the maximum range for a given slope and speed of projection. Candidates may be expected to determine whether a projectile lands at a higher or lower point on the plane after a bounce. 
27 Mechanics 4
Introduction
A knowledge of the topics and associated formulae from Modules Core 1 – Core 4, Mechanics 1 and Mechanics 2 is required. Candidates may use relevant formulae included in the formulae booklet without proof.
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
Rotations  Moment of a Force Moment of Inertia Rational Kinetic Energy Resultant Moment 
Centre of Mass  For a Uniform Lamina
For a Solid of Revolution (about the axis) 
27.1 Moments
Couples.  Understanding of the concept of a couple. 
Reduction of systems of coplanar forces.  Reduction to a single force, a single couple or to a couple and a force acting at a point. The line of action of a resultant force may be required. 
Conditions for sliding and toppling.  Determining how equilibrium will be broken in situations, such as a force applied to a solid on a horizontal surface or on an inclined plane with an increasing slope. Derivation of inequalities that must be satisfied for equilibrium. 
27.2 Frameworks
Finding unknown forces acting on a framework. Finding the forces in the members of a light, smoothly jointed framework. Determining whether rods are in tension or compression.  Awareness of assumptions made when solving framework problems. 
27.3 Vector Product and Moments
The vector product

Candidates may use determinants to find vector products. 
The result  
The moment of a force as .  
Vector methods for resultant force and moment. Application to simple problems. 
Finding condition for equilibrium, unknown forces or points of application. 
27.4 Centres of Mass by Integration for Uniform Bodies
Centre of mass of a uniform lamina by integration. Centre of mass of a uniform solid formed by rotating a region about the axis. 
Finding and coordinates of the centre of mass. 
27.5 Moments of Inertia
Moments of inertia for a system of particles.  About any axis 
Moments of inertia for uniform bodies by integration.  Candidates should be able to derive standard results, ie rod, rectangular lamina, hollow or solid sphere and cylinder. 
Moments of inertia of composite bodies.  Bodies formed from simple shapes. 
Parallel and perpendicular axis theorems.  Application to finding moments of inertia about different axes. 
27.6 Motion of a rigid body about a smooth fixed axis.
Angular velocity and acceleration of a rigid body.  To exclude small oscillations of a compound pendulum. 
Motion of a rigid body about a fixed horizontal or vertical axis.  Resultant Moment Including motion under the action of a couple. 
Rotational kinetic energy and the principle of conservation of energy.  Rotational Kinetic Energy and To include problems such as the motion of a mass falling under gravity while fixed to the end of a light inextensible string wound round a pulley of given moment of inertia. 
Moment of momentum (angular momentum).  
The principle of conservation of angular momentum.  To include simple collision problems, eg a particle colliding with a rod rotating about a fixed axis. 
Forces acting on the axis of rotation 
28 Mechanics 5
Introduction
Candidates should learn the following formulae, which are not included in the formulae booklet, but which may be required to answer questions.
Energy Formulae  Gravitational Potential Energy
or Elastic Potential Energy 
Simple Harmonic Motion 

28.1 Simple Harmonic Motion
Knowledge of the definition of simple harmonic motion.  
Finding frequency, period and amplitude  
Knowledge and use of the formula .  
Formation of simple second order differential equations to show that simple harmonic motion takes place.  Problems will be set involving elastic strings and springs. Candidates will be required to be familiar with both modulus of elasticity and stiffness. They should be aware of and understand the relationship . 
Solution of second order differential equations of the form  State solutions in the form or and use these in problems. 
Simple Pendulum.  Formation and solution of the differential equation, including the use of a small angle approximation. Finding the period. 
28.2 Forced and Damped Harmonic Motion
Understanding the terms forcing and damping and solution of problems involving them. Candidates should be able to set up and solve differential equations in situations involving damping and forcing. 
Damping will be proportional only to velocity.
Forcing forces will be simple polynomials or of the form
or . 
Light, critical and heavy damping.  Candidates should be able to determine which of these will take place. 
Resonance.  Solutions may be required for the case where the forcing frequency is equal to the natural frequency. 
Application to spring/mass systems. 
28.3 Stability
Finding and determining whether positions of equilibrium are stable or unstable.  Use of potential energy methods. Problems will involve gravitational and elastic potential energy. 
28.4 Variable Mass Problems
Equation of motion for variable mass.  Derive equations of motion for variable mass problems, for example, a rocket burning fuel, or a falling raindrop. Rocket problems will be set in zero or constant gravitational fields. 
28.5 Motion in a Plane using Polar Coordinates
Polar coordinates  
Transverse and radial components of velocity in polar form.  These results may be stated. No proof will be required. 
Transverse and radial components of acceleration in polar form.  
Application of polar form of velocity and acceleration.  
Application to simple central forces.  No specific knowledge of planetary motion will be required. 
29 Decision 1
29.1 Simple Ideas of Algorithms
Correctness, finiteness and generality. Stopping conditions.  Candidates should appreciate that for a given input an algorithm produces a unique output. Candidates will not be required to write algorithms in examinations, but may be required to trace, correct, complete or amend a given algorithm, and compare or comment on the number of iterations required. The algorithm may be presented as a flow diagram. 
Bubble, shuttle, shell, quicksort algorithms.  Candidates should appreciate the relative advantages of the different algorithms in terms of the number of iterations required. When using the quicksort algorithm, the first number in each list will be taken as the pivot. 
29.2 Graphs and Networks
Vertices, edges, edge weights, paths, cycles, simple graphs.  
Adjacency/distance matrices.  For storage of graphs. 
Connectedness.  
Directed and undirected graphs.  
Degree of a vertex, odd and even vertices, Eulerian trails and Hamiltonian cycles.  
Trees.  
Bipartite and complete graph.  Use of the notations and 
29.3 Spanning Tree Problems
Prim's and Kruskal's algorithms to find minimum spanning trees. Relative advantage of the two algorithms.  Minimum length spanning trees are also called minimum connectors. Candidates will be expected to apply these algorithms in graphical, and for Prim's algorithm also in tabular, form. Candidates may be required to comment on the appropriateness of their solution in its context. 
Greediness. 
29.4 Matchings
Use of bipartite graphs.  
Improvement of matching using an algorithm.  Use of an alternating path. 
29.5 Shortest Paths in Networks
Dijkstra's algorithm.  Problems involving shortest and quickest routes and paths of minimum cost. Including a labelling technique to identify the shortest path. Candidates may be required to comment on the appropriateness of their solution in its context. 
29.6 Route Inspection Problem
Chinese Postman problem. 
Candidates should appreciate the significance of the odd vertices. Although problems with more than four odd vertices will not be set, candidates must be able to calculate the number of possible pairings for odd vertices. Candidates may be required to comment on the appropriateness of their solution in its context. 
29.7 Travelling Salesperson Problem
Conversion of a practical problem into the classical problem of finding a Hamiltonian cycle.  
Determination of upper bounds by nearest neighbour algorithm.  
Determination of lower bounds on route lengths using minimum spanning trees. 
By deleting a node, then adding the two shortest distances to the node and the length of the minimum spanning tree for the remaining graph.

29.8 Linear Programming
Candidates will be expected to formulate a variety of problems as linear programmes. They may be required to use up to a maximum of 3 variables, which may reduce to two variable requiring a graphical solution.  
Graphical solution of two variable problems. 
In the case of two decision variables candidates may be expected to plot a feasible region and objective line.

29.9 Mathematical modelling
The application of mathematical modelling to situations that relate to the topics covered in this module.  Including the interpretation of results in context. 
30 Decision 2
Introduction
Candidates will be expected to be familiar with the knowledge, skills and understanding implicit in the linear programming section of Decision 1.
30.1 Critical Path Analysis
Representation of compound projects by activity networks, algorithm to find the critical path(s); cascade (or Gantt) diagrams; resource histograms and resource levelling.  Activityonnode representation will be used for project networks. Heuristic procedures only are required for resource levelling. Candidates may be required to comment on the appropriateness of their solution in its context 
30.2 Allocation
The Hungarian algorithm. 
Including the use of a dummy row or column for unbalanced problems.

30.3 Dynamic Programming
The ability to cope with negative edge lengths.  A stage and state approach may be required in dynamic programming problems. 
Application to production planning.  
Finding minimum or maximum path through a network.  
Solving maximin and minimax problems. 
30.4 Network Flows
Maximum flow/minimum cut theorem.  Problems may require supersources and sinks, may have upper and lower capacities and may have vertex restrictions. 
Labelling procedure.  For flow augmentation. 
30.5 Linear Programming
The Simplex method and the Simplex tableau.  Candidates will be expected to introduce slack variables, iterate using a tableau and interpret the outcome at each stage. 
30.6 Game Theory for Zero Sum Games
Payoff matrix, playsafe strategies and saddle points. Optimal mixed strategies for the graphical method. 
Reduction of payoff matrix using dominance arguments.
Candidates may be required to comment on the appropriateness of their solution in its context. 
30.7 Mathematical modelling
The application of mathematical modelling to situations that relate to the topics covered in this module.  Including the interpretation of results in context. 