Netherlands VWO 5 Mathematics A

Chapters: 3

1. Relationships (part 2)

Functions, graphs, equations and inequalities

Transformations of Functions – A transformation changes the graph of a parent function y = f(x) without changing its basic shape. Adding outside the function moves it vertically: f(x) + k moves up k. Changes inside the brackets act horizontally and the opposite way: f(x − h) moves right h. Multiplying outside, a·f(x), stretches vertically by a (and flips in the x-axis if a < 0). Multiplying inside, f(bx), squeezes horizontally by factor 1/b (and flips in the y-axis if b < 0). All together: y = a·f(b(x − h)) + k, and the point (0, 0) of the parent moves to (h, k).

2. Change (part 1)

Sequences

Sequences and Series: AP, GP, AM and GM – A sequence is a list of numbers in a definite order: a₁, a₂, a₃, … A series is what we get when we add the terms. In an arithmetic progression (AP) we add the same number d each time; aₙ = a + (n − 1)d and Sₙ = n/2[2a + (n − 1)d]. The arithmetic mean (AM) of a and b is (a + b)/2; n AMs between a and b use d = (b − a)/(n + 1). In a geometric progression (GP) we multiply by the same number r each time; aₙ = arⁿ⁻¹ and Sₙ = a(rⁿ − 1)/(r − 1) for r ≠ 1. If |r| < 1 the terms shrink and the infinite sum is S∞ = a/(1 − r). The geometric mean (GM) of two positive numbers a and b is √(ab), and n GMs between them use r = (b/a)^(1/(n+1)). For positive a and b, AM ≥ GM, with equality only when a = b.

3. Statistics and probability (part 2)

Probability · Probability distributions · Inferential statistics

Probability: Chances of a Single Event – Probability tells how likely something is, as a number from 0 to 1. When all outcomes are equally likely, P(event) = number of favourable outcomes ÷ total number of outcomes. An impossible event has probability 0 and a sure event has probability 1.
Probability Distributions of Discrete Random Variables – A random variable X turns each outcome of an experiment into a number. Its probability distribution lists every value x with its probability P(X = x); each P is between 0 and 1 and they add to 1. The mean E(X) = Σx·P(x) is the long-run average (balance point). The variance Var(X) = Σ(x − μ)²P(x) = E(X²) − μ² measures spread; σ = √Var. Special models: uniform, binomial B(n, p) with mean np and variance np(1 − p), and Poisson with mean = variance = λ.
Statistical Inference – Statistical inference means using a sample to say something about a whole population. A number that describes the population (like the true proportion p or the mean μ) is a parameter. A number worked out from a sample (like p̂ or x̄) is a statistic, and we use it as a point estimate. Different random samples give different answers: this is sampling variability. If we took many samples, their statistics would form the sampling distribution, centred on the true value, with spread called the standard error: SE = √(p(1−p)/n) for a proportion and σ/√n for a mean. Bigger samples give smaller spread. A 95% confidence interval is estimate ± 1.96 × SE; about 95 of every 100 such intervals catch the true value. Simulation helps us check whether a claimed model fits the data. Good inference needs random sampling, and an association in data does not prove cause.