QCAA Specialist Mathematics Units 3 and 4 Concepts

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Comprehensive vocabulary flashcards covering the core concepts, theorems, algebraic and vector properties, and statistical definitions in the QCAA Specialist Mathematics Units 3 and 4 syllabus.

28 Terms

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Modulus and Argument Identities

Identities for complex numbers stating that the modulus of a product equals the product of the moduli (∣z1z2∣=∣z1∣∣z2∣|z_1 z_2| = |z_1||z_2|), the argument of a product equals the sum of the arguments (arg⁡(z1z2)=arg⁡(z1)+arg⁡(z2)\arg(z_1 z_2) = \arg(z_1) + \arg(z_2)), and the product of a complex number and its conjugate equals the square of its modulus (zzˉ=∣z∣2z \bar{z} = |z|^2).

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De Moivre’s Theorem

A theorem stating that for any complex number in polar form and any integer power nn, [cis⁡(θ)]n=cis⁡(nθ)[\operatorname{cis}(\theta)]^n = \operatorname{cis}(n\theta).

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nthn^{\text{th}} Roots of Unity

The solutions to the polynomial equation zn=1z^n = 1, which are represented geometrically as nn equally spaced points located on the unit circle in the complex plane.

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Factor Theorem

A algebraic theorem stating that a linear expression x−cx - c is a factor of a polynomial P(x)P(x) if and only if P(c)=0P(c) = 0.

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Remainder Theorem

A theorem stating that when a polynomial P(x)P(x) is divided by a linear divisor x−cx - c, the resulting remainder is equal to P(c)P(c).

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Complex Conjugate Root Theorem

A theorem establishing that if a polynomial with real coefficients has a complex root of the form a+bia + bi, then its complex conjugate a−bia - bi must also be a root of that polynomial.

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Inductive Proof

A formal method of mathematical proof consisting of an initial statement (base step), an assumption statement (inductive hypothesis), an inductive step establishing truth for n+1n + 1, and a concluding statement.

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Sigma Notation (Σ\Sigma)

A compact mathematical notation used to represent the summation of a sequence of terms over a defined set of index limits.

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Unit Vector (a^\hat{\mathbf{a}}) in Three Dimensions

A vector that has a magnitude of 11, calculated by dividing a non-zero vector by its magnitude: a^=a∣a∣\hat{\mathbf{a}} = \frac{\mathbf{a}}{|\mathbf{a}|}.

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Altitude Angle (φ\varphi)

The vertical angle between a three-dimensional vector and the horizontal xyxy-plane.

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Scalar (Dot) Product

An algebraic operation between two vectors yielding a scalar quantity, defined geometrically as a⋅b=∣a∣∣b∣cos⁡(θ)\mathbf{a} \cdot \mathbf{b} = |\mathbf{a}||\mathbf{b}|\cos(\theta), which equals 00 if and only if non-zero vectors are perpendicular.

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Scalar Projection

The signed scalar length of the orthogonal projection of a vector a\mathbf{a} onto a vector b\mathbf{b}, given by a⋅b∣b∣\frac{\mathbf{a} \cdot \mathbf{b}}{|\mathbf{b}|}.

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Vector Projection

The vector component of a vector a\mathbf{a} in the direction of a vector b\mathbf{b}, given by (a⋅b∣b∣2)b\left(\frac{\mathbf{a} \cdot \mathbf{b}}{|\mathbf{b}|^2}\right)\mathbf{b}.

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Vector (Cross) Product

A vector operation a×b\mathbf{a} \times \mathbf{b} that produces a vector perpendicular (normal) to both vectors a\mathbf{a} and b\mathbf{b}, commonly used to find the normal vector to a plane.

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Singular Matrix

A square matrix whose determinant is equal to zero (det⁡(A)=0\det(A) = 0), which means it does not possess a multiplicative inverse.

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Non-singular Matrix

A square matrix whose determinant is non-zero (det⁡(A)≠0\det(A) \neq 0), guaranteeing the existence of a unique multiplicative inverse A−1A^{-1}.

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Gaussian Elimination

A systematic method of solving a system of linear equations by performing elementary row operations on an augmented matrix to reduce it to row echelon form.

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Leslie Matrix

A square population-projection matrix used in mathematical modeling to describe the growth, fecundity, and survival transitions across distinct age or life-stage groups over discrete time steps.

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Integration by Parts

An integration method derived from the product rule of differentiation, defined by the formula ∫u(dvdx)dx=uv−∫v(dudx)dx\int u\left(\frac{dv}{dx}\right)dx = uv - \int v\left(\frac{du}{dx}\right)dx.

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Simpson’s Rule

A numerical technique for approximating the value of a definite integral and area under a curve by fitting parabolic arcs over an even number of subintervals.

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Exponential Random Variable

A continuous random variable modeling waiting times between independent events, characterized by parameter λ>0\lambda > 0 and probability density function f(t)=λe−λtf(t) = \lambda e^{-\lambda t} for t≥0t \ge 0.

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Implicit Differentiation

A calculus technique using the chain rule to differentiate an equation relating xx and yy where the dependent variable yy is not explicitly expressed as a function of xx.

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Slope Field (Direction Field)

A geometric visualization of a first-order differential equation dydx=f(x,y)\frac{dy}{dx} = f(x, y) consisting of short tangent segments plotted at a grid of points to show the family of solution curves.

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Representations of Rectilinear Acceleration

Alternative mathematical formulations of the acceleration of an object moving in a straight line, given by d2xdt2\frac{d^2x}{dt^2}, v(dvdx)v\left(\frac{dv}{dx}\right), or ddx(12v2)\frac{d}{dx}\left(\frac{1}{2}v^2\right).

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Sample Mean (Xˉ\bar{X})

A random variable whose value varies from sample to sample, calculated from random samples of size nn taken from a population with mean μ\mu and standard deviation σ\sigma.

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Sampling Distribution of the Sample Mean

The distribution of the sample mean Xˉ\bar{X} across all possible samples of size nn, having mean μ\mu, standard deviation σn\frac{\sigma}{\sqrt{n}}, and approximately normal form for large sample sizes: Xˉ∼N(μ,σ2n)\bar{X} \sim N\left(\mu, \frac{\sigma^2}{n}\right).

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Confidence Interval for μ\mu

An approximate interval estimate for the population mean μ\mu constructed from a sample as (xˉ−zsn,xˉ+zsn)\left(\bar{x} - z\frac{s}{\sqrt{n}}, \bar{x} + z\frac{s}{\sqrt{n}}\right), where zz is the standard normal quantile corresponding to the specified confidence level.

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Margin of Error

The maximum expected difference between the sample estimate and the true population parameter for a specified level of confidence, calculated as zsnz\frac{s}{\sqrt{n}}.