Mathematical Methods

Mathematical Methods

Thank you for your interest in Year 12 Mathematical Methods. To revise as much as possible, it is recommended to attend all the seminars listed below. However, this is not compulsory and you may choose any combination of sessions.

Our Term 3 Revision Seminars have an exclusive exam focus integrated into each session. We cover exam revision and how to answer exam questions according to the marker’s requirements. 

Can’t make it to our in-person seminars? We also offer this subject as part of our Virtual Seminars.

 

Teacher in charge

Sessions

Session A

Introductory differential calculus

Available

Course details

  • Derivatives from first principles
  • Rules of differentiation including chain rule, product rule and quotient rule
  • Deriving local maxima and minima
  • Increasing and decreasing functions
  • Graphing of functions  y = xn

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$80.00p/session
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Session B

Derivatives of exponential and logarithmic functions

Available

Course details

  • Slopes of tangents of the exponential and logarithmic functions
  • Local maxima and minima
  • Application of exponential and logarithmic functions including growth and decay, surge and logistic functions
  • Graphing of y = ln x and y = ex

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$80.00p/session
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Session C

Derivatives of trigonometric functions

Available

Course details

  • Derivatives of trigonometric functions
  • Kinematics: displacement, velocity, acceleration
  • Application of trigonometric functions, including tidal heights and temperature changes
  • Graphing of and sin x and cos x.

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$80.00p/session
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Session D

The second derivative

Available

Course details

  • Relationship between the 1st and 2nd derivatives
  • Optimisation
  • Points of inflection.

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Session E

Integration and statistics

Available

Course details

Integration (integral calculus):

  • Area under a curve
  • Problem-solving by integration

Statistics:

  • Measuring centre and spread of data
  • Discrete random variables – the Bernoulli and Binomial distribution
  • Continuous random variables – normal distribution

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