Search

ACMSM065

prove results for sums, such as \(1+4+9\dots+n^2=\frac{n(n+1)(2n+1)}6\) for any positive integer n

ACMEM102

recognise relations between volume and capacity, recognising that \(1\mathrm c\mathrm m^3=1\mathrm m\mathrm L\) and \(1\mathrm m^3=1\mathrm k\mathrm L\)

ACMMM116

establish and use the formula \(\int x^ndx=\frac1{n+1}x^{n+1}+c\) for \(n\neq-1\)

ACMSM050

model periodic motion using sine and cosine functions and understand the relevance of the period and amplitude of these functions in the model.

ACMMM099

recognise that \(e\) is the unique number \(a\) for which the above limit is 1

ACMSM067

define the imaginary number i as a root of the equation \(x^2=-1\)

ACMEM174

use technology and a recurrence relation to model a reducing balance loan

ACMGM057

model a linear relationship by fitting a least-squares line to the data

ACMGM058

use a residual plot to assess the appropriateness of fitting a linear model to the data

ACMMM143

use a Bernoulli random variable as a model for two-outcome situations

A wave model explains a wide range of light-related phenomena including reflection, refraction, total internal reflection, dispersion, diffraction and interference; a transverse wave model is required to explain polarisation

ACMMM068

recognise and use the recursive definition of an arithmetic sequence: \(t_{n+1}=t_n+d\)

ACMMM072

recognise and use the recursive definition of a geometric sequence:\(t_{n+1}=rt_n\)

ACMSM101

review the concepts of vectors from Unit 1 and extend to three dimensions including introducing the unit vectors i, j and k.

ACSCH028

Nanomaterials are substances that contain particles in the size range 1–100 nm and have specific properties relating to the size of these particles

ACMGM042

interpret, in context, the slope and intercept of a straight-line graph used to model and analyse a practical situation

ACMGM092

fit a least-squares line to model long-term trends in time series data.

ACMMM138

recognise uniform discrete random variables and use them to model random phenomena with equally likely outcomes

ACMMM146

use Bernoulli random variables and associated probabilities to model data and solve practical problems.

ACSPH017

The kinetic particle model describes matter as consisting of particles in constant motion, except at absolute zero

Curriculum Types

Curriculum Elements Types

General Capabilities