Exploring the IB Mathematics Analysis & Approaches syllabus provides a clear view of the skills and knowledge you'll acquire throughout the course. Join us as we unpack the key concepts of the syllabus, giving you a better understanding of what to expect on your learning journey.
Topic 4: Statistics and probability
In this topic, students explore the following concepts:
- 4.1
- Concepts of population, sample, random sample, discrete and continuous data
- Reliability of data sources and bias in sampling
- Interpretation of outliers
- Sampling techniques and their effectiveness
- 4.2
- Presentation of data (discrete and continuous): frequency distributions (tables)
- Histograms
- Cumulative frequency; cumulative frequency graphs; use to find median, quartiles, percentiles, range and interquartile range (IQR)
- Production and understanding of box and whisker diagrams
- 4.3
- Measures of central tendency (mean, median and mode)
- Estimation of mean from grouped data
- Modal class
- Measures of dispersion (interquartile range, standard deviation and variance)
- Effect of constant changes on the original data
- Quartiles of discrete data
- 4.4
- Linear correlation of bivariate data
- Pearson’s product-moment correlation coefficient, r
- Scatter diagrams; lines of best fit, by eye, passing through the mean point
- Equation of the regression line of y on x
- Use of the equation of the regression line for prediction purposes
- Interpret the meaning of the parameters, a and b, in a linear regression y = ax + b
- 4.5
- Concepts of trial, outcome, equally likely outcomes, relative frequency, sample space (U) and event
- The probability of an event
- The complementary events A and A′ (not A)
- Expected number of occurrences
- 4.6
- Use of Venn diagrams, tree diagrams, sample space diagrams and tables of outcomes to calculate probabilities
- Combined events
- Mutually exclusive events
- Conditional probability
- Independent events
- 4.7
- Concept of discrete random variables and their probability distributions
- Expected value (mean), for discrete data
- Applications of above concepts
- 4.8
- Binomial distribution
- Mean and variance of the binomial distribution
- 4.9
- The normal distribution and curve
- Properties of the normal distribution
- Diagrammatic representation
- Normal probability calculations
- Inverse normal calculations
- 4.10
- Equation of the regression line of x on y
- Use of the equation for prediction purposes
- 4.11
- Conditional probabilities and independent events
- 4.12
- Standardization of normal variables (z- values)
- Inverse normal calculations where mean and standard deviation are unknown
- 4.13 (HL only)
- Use of Bayes’ theorem for a maximum of three events
- 4.14 (HL only)
- Variance of a discrete random variable
- Continuous random variables and their probability density functions
- Mode and median of continuous random variables
- Mean, variance and standard deviation of both discrete and continuous random variables
- The effect of linear transformations of X
Topic 5: Calculus
In this topic, students explore the following concepts:
- 5.1
- Introduction to the concept of a limit
- Derivative interpreted as gradient function and as rate of change
- 5.2
- Increasing and decreasing functions
- Graphical interpretation of f′(x) > 0, f′(x) = 0, f′(x) < 0
- 5.3
- Derivative of f(x) = axn is f ′(x) = anxn−1, n ∈ Z
- The derivative of functions of the form f(x) = axn + bxn−1... where all exponents are integers
- 5.4
- Tangents and normals at a given point, and their equations
- 5.5
- Introduction to integration as anti-differentiation of functions of the form f(x) = axn + bxn-1 + ...., where n∈Z, n≠ −1
- Anti-differentiation with a boundary condition to determine the constant term
- Definite integrals using technology
- Area of a region enclosed by a curve y = f(x) and the x -axis, where f (x) > 0
- 5.6
- Derivative of xn (n ∈ Q), sinx, cosx, ex and lnx.
- Differentiation of a sum and a multiple of these functions
- The chain rule for composite functions
- The product and quotient rules
- 5.7
- The second derivative
- Graphical behaviour of functions, including the relationship between the graphs of f , f ′ and f'′
- 5.8
- Local maximum and minimum points
- Testing for maximum and minimum
- Optimization
- Points of inflexion with zero and non-zero gradients
- 5.9
- Kinematic problems involving displacement s, velocity v, acceleration a and total distance travelled
- 5.10
- Indefinite integrals
- Composites
- Reverse chain rule
- 5.11
- Definite integrals, including analytical approach
- Areas of a region enclosed by a curve y = f (x) and the x-axis, where f(x) can be positive or negative, without the use of technology
- Areas between curves
- 5.12 (HL only)
- Informal understanding of continuity and differentiability of a function at a point
- Understanding of limits (convergence and divergence)
- Higher derivatives
- 5.13 (HL only)
- Evaluation of limits
- Repeated use of l’Hôpital’s rule
- 5.14 (HL only)
- Implicit differentiation
- Related rates of change
- Optimisation problems
- 5.15 (HL only)
- Derivatives of tanx, secx, cosecx,cotx, ax, logax, arcsinx, arccosx, arcta
- Indefinite integrals of the derivatives of any of the above functions
- The composites of any of the above with a linear function
- Use of partial fractions to rearrange the integrand
- 5.16 (HL only)
- Integration by substitution
- Integration by parts
- Repeated integration by parts
- 5.17 (HL only)
- Area of the region enclosed by a curve and the y-axis in a given interval
- Volumes of revolution about the x-axis or y-axis
- 5.18 (HL only)
- First order differential equations
- Numerical solution of dy/dx = f(x,y) using Euler's rule
- Variables seperable
- Homogeneous differential equation dy/dx = f(y/x) using the substitution y = vx
- Solution of y′ + P(x)y = Q(x), using the integrating factor
- 5.19 (HL only)
- Maclaurin series to obtain expansions for ex, sinx, cosx, ln(1 + x), (1 + x)p, p ∈ Q
- Use of simple substitution, products, integration, differentiatio to obtain other series
- Maclaurin series developed from differential equation
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