1. Mathematical Methods 2013
(Teacher: Julie Sampson)
Week
beginning
Monday
Content Activities Resources Assessment
1
First
lessons
Thurs.
2.1 Algebraic
Generation of Linear
Models
Linear modelling,
correlation
Intro lesson Haese 3A - C
2 Least squares regression,
interpolation &
extrapolation. Residual
plots
Use IWB to access CD Rom Haese 3D - F Formative – quiz on
principles so far
3 2.2 Algebraic
Generation of
Exponential Models
Power & Exponential
models. Functions from
their graphs.
Review bookwork – are
students completing HW
exercises?
Haese 4A - B
4 Exponential e and natural
logs.
2.3 Algebraic
Generation of Power
ModelsModelling from
data – exponential
models. Residual plots
Reminder to prepare info
sheet for first test
Haese 4C
Haese 4D - E
5 Modelling from data –
power models. Residual
plots.
Haese 4F
Open Access Booklet
Summative Test 1
(Algebraic
modelling)
6 3.1 Rate of change
Concepts of average and
instantaneous rate of
change, the algebraic
method to find a slope
Haese 5A - B
7 3.2 Derivatives Haese 5C
8 3.3 Differentiation
Sums, compositions &
products
Worksheets on rules
Haese 5D1 - 4
9 Exponential and
logarithmic derivatives
10
Differentiation contd
3.4 Properties of
Regression
Modelsincreasing/decrea
sing functions, turning
points and inflections,
concave & convex
Haese 6A, C
Summative Test 2
(Rates &
Derivatives)
11 Modelling esp logistic,
surge & terminal
velocity. Using graphics
calculators and calculus.
Haese 6B, D, E Special Timetable
Day – DI Activity
HOLIDAYS
Skills review assignment on
Term 1 work
1 1.1 Normal
Distributions and
the standard normal
distribution
1.2 Finding k values,
unknown mean or
standard deviation.
Revision from yr 11
IWB demo with emulator
Haese 1F1 - 6 Summative Test 3
(Modelling with
Calculus)

2. 2 1.2 Central Limit
TheoremSampling
distributions
Haese 2A1 - 3
3 Using the central limit
theorem
Numb3rs video & activity on
clustered events
4 1.3 Confidence Intervals
for a Population
Meanand determining
how large a sample
should be
Haese 2B1 - 2
Summative Test
4(Statistics)
5 1.4 Continuous &
Discrete Interval and
Categorical Data
Haese 1D1
6 1.5 Binomial
Distributions
mean & st. dev. of a d.r.v,
binomial probability,
mean & st. dev. of a
binomial r.v.
Using the student census data
– need computer room access
Haese 1D2, 3,
1E1, 2 Directed
Investigation 2 -
stats
7 Normal approximation to
the Binomial
Haese 1F7
8 1.6 Confidence Intervals
for a Population
Proportion
How large a sample
should be.
Haese 2B3, 4 Summative Test
5(Statistics)
9 Revision
10 Mid-yr EXAMS Full 3-hour practice exam
HOLIDAYS
1 4.2 Matrices Intro – Networks – DVD 6
QANTAS
Haese 8A - D
2 What are matrices, their
uses
3 Matrices in action-
inventory, dominance,
transition,
Rock, paper, scissors Haese 8E - J
4 Matrices in action –
connectivity, Leslie,
codes
Internet – Simon Singh’s
Codes
5 Investigation –
matrix codes
6 4.1 Linear
programmingconstructin
g constraints, feasible
region
Internet resource - Diet
problem
Haese 7A - H
(Special TT Week)
7 Optimal solutions
8 Rock Concert
Project
9 Revision Summative Test 6
(Matrices & Linear
Programming)
10
Summit
to School
Exam Preparation MASA Rev Guide & Quizes
Holidays Studying hard for the exam
1 Exam Preparation
2 Swat Vac
3 First Major Exams