IB Math AI SL Syllabus
Complete breakdown of topics, assessment structure, and learning resources for IB Mathematics Applications & Interpretations Standard Level. Master practical mathematical applications with our expert video tutorials and comprehensive study materials.
Topic 1: Number & Algebra
1.1 Operations with Numbers
- Numbers in the form a × 10k
- Where 1 ≤ a < 10 and k is an integer
1.2 Arithmetic Sequences and Series
- Formulae for nth term: un = u1 + (n − 1)d
- Sum of first n terms: Sn = n/2 (2u1 + (n − 1)d)
- Use of sigma (Σ) notation, e.g. Σk=1n (3k + 2)
- Applications: analysis, interpretation, prediction when models are not perfectly arithmetic
1.3 Geometric Sequences and Series
- Formulae for nth term: un = u1 rn−1
- Sum of first n terms: Sn = u1(rn − 1) / (r − 1)
- Sigma notation for sums of geometric sequences
- Identify first term and ratio; applications
1.4 Financial Applications
- Compound interest: FV = PV × (1 + r / (100k))nk
- Annual depreciation
1.5 Laws of Exponents and Logarithms
- Laws of exponents with integer exponents
- Introduction to logarithms: log10 x and ln x
- Numerical evaluation using technology
1.6 Approximation
- Decimal places and significant figures
- Upper and lower bounds of rounded numbers
- Percentage errors: ε = | (vA − vE) / vE | × 100%, estimation
1.7 Amortization and Annuities
- Use of technology to calculate payments and balances
1.8 Solving Equations with Technology
- Systems of linear equations (up to 3 variables)
- Polynomial equations
Topic 2: Functions
2.1 Equations of a Straight Line
- Different forms: gradient, intercepts
- Parallel lines: m1 = m2
- Perpendicular lines: m1 × m2 = −1
2.2 Concept of a Function
- Domain, range, graph, notation (f(x), v(t), C(n))
- Inverse function as reflection in y = x, notation f−1(x)
2.3 Graphing Functions
- Sketch from context or data; transfer from screen to paper
- Graph sums and differences using technology
2.4 Key Features of Graphs
- Determine points of intersection of curves/lines using technology
2.5 Modelling with Functions
- Linear: f(x) = mx + c
- Quadratic: f(x) = ax2 + bx + c, a ≠ 0; axis, vertex, zeros, intercepts
- Exponential: f(x) = k · ax + c, f(x) = k · erx + c; horizontal asymptote
- Direct/inverse variation: f(x) = a · xn, n ∈ ℤ
- Cubic: f(x) = ax3 + bx2 + cx + d
- Sinusoidal: f(x) = a sin(bx) + d, f(x) = a cos(bx) + d
2.6 Modelling Skills
- Develop, fit, test, reflect, and use models
- Select a reasonable domain
- Justify the choice of a model based on data, curve shape, and context
Topic 3: Geometry & Trigonometry
3.1 Distance, Midpoint, and Solids
- Distance between two points: d = √((x2−x1)2 + (y2−y1)2 + (z2−z1)2)
- Midpoint: M = ((x1+x2)/2, (y1+y2)/2, (z1+z2)/2)
- Volume and surface area: right pyramid, right cone, sphere, hemisphere, and combinations
- Angle between two intersecting lines or between a line and a plane
3.2 Trigonometry in Triangles
- Right-angled triangles (sine, cosine, tangent):
- sin θ = opp / hyp
- cos θ = adj / hyp
- tan θ = opp / adj
- Sine rule: a / sin A = b / sin B = c / sin C
- Cosine rule: c2 = a2 + b2 − 2ab cos C; cos C = (a2 + b2 − c2) / (2ab)
- Area of triangle: Area = 1/2 · a · b · sin C
3.3 Applications of Trigonometry
- Applications of right and non-right triangle including Pythagoras’ theorem: a2 + b2 = c2
- Angles of elevation and depression
- Constructing labelled diagrams from statements
3.4 The Circle
- Radian measure of angles
- Length of an arc: L = r · θ
- Area of a sector: A = 1/2 · r2 · θ
3.5 Equations of Perpendicular Bisectors
- Find perpendicular bisector of a line segment
- Given two points or the equation of a line segment, calculate midpoint
- Determine slope of perpendicular bisector
- Equation using point-slope form: y − y0 = m(x − x0)
3.6 Voronoi Diagrams
- Sites, vertices, edges, cells
- Adding a site to an existing diagram
- Nearest neighbour interpolation
- Applications such as the “toxic waste dump” problem
Topic 4: Statistics & Probability
4.1 Population and Sampling
- 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
- Frequency distributions (tables) for discrete and continuous data
- Histograms
- Cumulative frequency and cumulative frequency graphs; find median, quartiles, percentiles, range, interquartile range (IQR)
- Box-and-whisker diagrams
4.3 Measures of Central Tendency
- Mean, median, mode
- Estimation of mean from grouped data
- Modal class
- Interquartile range (IQR), standard deviation, variance
- Effect of constant changes on data
4.4 Linear Correlation
- Scatter diagrams; lines of best fit (by eye through mean point)
- Pearson’s correlation coefficient r
- Regression line of y on x: y = ax + b; interpret parameters a and b
- Use of regression for prediction
4.5 Probability – Basic Concepts
- Trial, outcome, equally likely outcomes, relative frequency, sample space U, event
- Probability: P(A) = n(A) / n(U)
- Complementary events: P(A′) = 1 − P(A)
- Expected number of occurrences
4.6 Probability – Diagrams and Rules
- Venn diagrams, tree diagrams, sample space diagrams, tables of outcomes
- Combined events: P(A ∪ B) = P(A) + P(B) − P(A ∩ B)
- Mutually exclusive: P(A ∩ B) = 0
- Conditional probability: P(A|B) = P(A ∩ B) / P(B)
- Independent events: P(A ∩ B) = P(A) · P(B)
4.7 Discrete Random Variables
- Probability distributions
- Expected value (mean) E(X)
- Applications
4.8 Binomial Distribution
- General notation: X ~ B(n, p)
- Mean formula: μ = np
- Variance formula: σ2 = np(1 − p)
4.9 Normal Distribution
- General notation: X ~ N(μ, σ2)
- Properties of normal curve and diagrammatic representation
- Normal probability calculations using technology
- Inverse normal calculations
4.10 Spearman’s Rank Correlation
- Spearman’s rank correlation coefficient rs
- Awareness of Pearson vs Spearman correlation
- Effect of outliers
4.11 Hypothesis Testing
- Null and alternative hypotheses: H0 and H1
- Significance levels, p-values
- Chi-square test (χ2): independence, goodness of fit
- t-test: comparing two population means
- One-tailed and two-tailed tests
Topic 5: Calculus
5.1 Introduction to Limits and Derivatives
- Concept of a limit
- Derivative interpreted as gradient function and rate of change
5.2 Increasing and Decreasing Functions
- Graphical interpretation: f′(x) > 0, f′(x) = 0, f′(x) < 0
5.3 Derivatives of Polynomial Functions
- f(x) = axn ⇒ f′(x) = anxn−1, n ∈ ℤ
- Derivative of f(x) = axn + bxm + …, all integer exponents
5.4 Tangents and Normals
- Tangent and normal lines at a given point
- Equations of tangents and normals
5.5 Introduction to Integration
- Anti-differentiation of f(x) = axn + bxm + …, n ∈ ℤ, n ≠ −1
- Notation: ∫ f(x) dx
- Anti-differentiation with boundary condition
- Definite integrals using technology
- Area of a region enclosed by y = f(x) and the x-axis where f(x) > 0
5.6 Stationary Points
- Values of x where gradient is zero: f′(x) = 0
- Local maximum and minimum points
5.7 Optimisation Problems
- Solving context-based optimisation problems
5.8 Approximating Areas
- Trapezoidal rule for approximating areas under curves
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