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10 Mathematics HL guide

Syllabus outline

Syllabus

Syllabus componentTeaching hours

HL

All topics are compulsory. Students must study all the sub-topics in each of the topics in the syllabus as listed in this guide. Students are also required to be familiar with the topics listed as prior learning.

Topic 1

Algebra

30

Topic 2

Functions and equations

22

Topic 3

Circular functions and trigonometry

22

Topic 4

Vectors

24

Topic 5

Statistics and probability

36

Topic 6

Calculus

48

Option syllabus content

Students must study all the sub-topics in one of the following options as listed in the syllabus details.

Topic 7


Topic 8

Sets, relations and groups

Topic 9

Calculus

Topic 10

Discrete mathematics

48

Mathematical exploration

Internal assessment in mathematics HL is an individual exploration. This is a piece of written work that involves investigating an area of mathematics.

10

Total teaching hours 240

Mathematics HL guide 11

Approaches to the teaching and learning of mathematics HL

Syllabus

Throughout the DP mathematics HL course, students should be encouraged to develop their understanding of the methodology and practice of the discipline of mathematics. The processes of mathematical inquiry, mathematical modelling and applications and the use of technology should be introduced appropriately. These processes should be used throughout the course, and not treated in isolation.

Mathematical inquiryThe IB learner profile encourages learning by experimentation, questioning and discovery. In the IB classroom, students should generally learn mathematics by being active participants in learning activities rather than recipients of instruction. Teachers should therefore provide students with opportunities to learn through mathematical inquiry. This approach is illustrated in figure 2.

Explore the context

Make a conjecture

Extend

Prove

Accept

RejectTest the conjecture

Figure 2

Mathematics HL guide12


Mathematical modelling and applicationsStudents should be able to use mathematics to solve problems in the real world. Engaging students in the mathematical modelling process provides such opportunities. Students should develop, apply and critically analyse models. This approach is illustrated in figure 3.

Pose a real-world problem

Develop a model

Extend

Reflect on and apply the model

Accept

RejectTest the model

Figure 3

TechnologyTechnology is a powerful tool in the teaching and learning of mathematics. Technology can be used to enhance visualization and support student understanding of mathematical concepts. It can assist in the collection, recording, organization and analysis of data. Technology can increase the scope of the problem situations that are accessible to students. The use of technology increases the feasibility of students working with interesting problem contexts where students reflect, reason, solve problems and make decisions.

As teachers tie together the unifying themes of mathematical inquiry, mathematical modelling and applications and the use of technology, they should begin by providing substantial guidance, and then gradually encourage students to become more independent as inquirers and thinkers. IB students should learn to become strong communicators through the language of mathematics. Teachers should create a safe learning environment in which students are comfortable as risk-takers.

Teachers are encouraged to relate the mathematics being studied to other subjects and to the real world, especially topics that have particular relevance or are of interest to their students. Everyday problems and questions should be drawn into the lessons to motivate students and keep the material relevant; suggestions are provided in the “Links” column of the syllabus. The mathematical exploration offers an opportunity to investigate the usefulness, relevance and occurrence of mathematics in the real world and will add an extra dimension to the course. The emphasis is on communication by means of mathematical forms (for



example, formulae, diagrams, graphs and so on) with accompanying commentary. Modelling, investigation, reflection, personal engagement and mathematical communication should therefore feature prominently in the DP mathematics classroom.

For further information on “Approaches to teaching a DP course”, please refer to the publication The Diploma Programme: From principles into practice (April 2009). To support teachers, a variety of resources can be found on the OCC and details of workshops for professional development are available on the public website.

Format of the syllabus• Content: this column lists, under each topic, the sub-topics to be covered.

• Further guidance: this column contains more detailed information on specific sub-topics listed in the content column. This clarifies the content for examinations.

• Links: this column provides useful links to the aims of the mathematics HL course, with suggestions for discussion, real-life examples and ideas for further investigation. These suggestions are only a guide for introducing and illustrating the sub-topic and are not exhaustive. Links are labelled as follows.

Appl real-life examples and links to other DP subjects

Aim 8 moral, social and ethical implications of the sub-topic

Int international-mindedness

TOK suggestions for discussion

Note that any syllabus references to other subject guides given in the “Links” column are correct for the current (2012) published versions of the guides.

Notes on the syllabus• Formulae are only included in this document where there may be some ambiguity. All formulae required

for the course are in the mathematics HL and further mathematics HL formula booklet.

• The term “technology” is used for any form of calculator or computer that may be available. However, there will be restrictions on which technology may be used in examinations, which will be noted in relevant documents.

• The terms “analysis” and “analytic approach” are generally used when referring to an approach that does not use technology.

Course of studyThe content of all six topics and one of the option topics in the syllabus must be taught, although not necessarily in the order in which they appear in this guide. Teachers are expected to construct a course of study that addresses the needs of their students and includes, where necessary, the topics noted in prior learning.



Integration of the mathematical explorationWork leading to the completion of the exploration should be integrated into the course of study. Details of how to do this are given in the section on internal assessment and in the teacher support material.

Time allocationThe recommended teaching time for higher level courses is 240 hours. For mathematics HL, it is expected that 10 hours will be spent on work for the exploration. The time allocations given in this guide are approximate, and are intended to suggest how the remaining 230 hours allowed for the teaching of the syllabus might be allocated. However, the exact time spent on each topic depends on a number of factors, including the background knowledge and level of preparedness of each student. Teachers should therefore adjust these timings to correspond to the needs of their students.

Use of calculatorsStudents are expected to have access to a graphic display calculator (GDC) at all times during the course. The minimum requirements are reviewed as technology advances, and updated information will be provided to schools. It is expected that teachers and schools monitor calculator use with reference to the calculator policy. Regulations covering the types of calculators allowed in examinations are provided in the Handbook of procedures for the Diploma Programme. Further information and advice is provided in the Mathematics HL/SL: Graphic display calculators teacher support material (May 2005) and on the OCC.

Mathematics HL and further mathematics HL formula bookletEach student is required to have access to a clean copy of this booklet during the examination. It is recommended that teachers ensure students are familiar with the contents of this document from the beginning of the course. It is the responsibility of the school to download a copy from IBIS or the OCC, check that there are no printing errors, and ensure that there are sufficient copies available for all students.

Teacher support materialsA variety of teacher support materials will accompany this guide. These materials will include guidance for teachers on the introduction, planning and marking of the exploration, and specimen examination papers and markschemes.

Command terms and notation listTeachers and students need to be familiar with the IB notation and the command terms, as these will be used without explanation in the examination papers. The “Glossary of command terms” and “Notation list” appear as appendices in this guide.


Syllabus

Prior learning topics

As noted in the previous section on prior learning, it is expected that all students have extensive previous mathematical experiences, but these will vary. It is expected that mathematics HL students will be familiar with the following topics before they take the examinations, because questions assume knowledge of them. Teachers must therefore ensure that any topics listed here that are unknown to their students at the start of the course are included at an early stage. They should also take into account the existing mathematical knowledge of their students to design an appropriate course of study for mathematics HL. This table lists the knowledge, together with the syllabus content, that is essential to successful completion of the mathematics HL course.

Students must be familiar with SI (Système International) units of length, mass and time, and their derived units.

Topic Content

Number Routine use of addition, subtraction, multiplication and division, using integers, decimals and fractions, including order of operations.

Rational exponents.

Simplification of expressions involving roots (surds or radicals), including rationalizing the denominator.

Prime numbers and factors (divisors), including greatest common divisors and least common multiples.

Simple applications of ratio, percentage and proportion, linked to similarity.

Definition and elementary treatment of absolute value (modulus), a .

Rounding, decimal approximations and significant figures, including appreciation of errors.

Expression of numbers in standard form (scientific notation), that is, 10ka× , 1 10a≤ < , k∈ .

Sets and numbers Concept and notation of sets, elements, universal (reference) set, empty (null) set, complement, subset, equality of sets, disjoint sets. Operations on sets: union and intersection. Commutative, associative and distributive properties. Venn diagrams.

Number systems: natural numbers; integers, ; rationals, , and irrationals; real numbers, .

Intervals on the real number line using set notation and using inequalities. Expressing the solution set of a linear inequality on the number line and in set notation.

Mappings of the elements of one set to another; sets of ordered pairs.


Prior learning topics

Topic Content

Algebra Manipulation of linear and quadratic expressions, including factorization, expansion, completing the square and use of the formula.

Rearrangement, evaluation and combination of simple formulae. Examples from other subject areas, particularly the sciences, should be included.

Linear functions, their graphs, gradients and y-intercepts.

Addition and subtraction of simple algebraic fractions.

The properties of order relations: <, ≤ , >, ≥ .

Solution of linear equations and inequalities in one variable, including cases with rational coefficients.

Solution of quadratic equations and inequalities, using factorization and completing the square.

Solution of simultaneous linear equations in two variables.

Trigonometry Angle measurement in degrees. Compass directions. Right-angle trigonometry. Simple applications for solving triangles.

Pythagoras’ theorem and its converse.

Geometry Simple geometric transformations: translation, reflection, rotation, enlargement. Congruence and similarity, including the concept of scale factor of an enlargement.

The circle, its centre and radius, area and circumference. The terms arc, sector, chord, tangent and segment.

Perimeter and area of plane figures. Properties of triangles and quadrilaterals, including parallelograms, rhombuses, rectangles, squares, kites and trapeziums (trapezoids); compound shapes. Volumes of cuboids, pyramids, spheres, cylinders and cones. Classification of prisms and pyramids, including tetrahedra.

Coordinate geometry

Elementary geometry of the plane, including the concepts of dimension for point, line, plane and space. The equation of a line in the form y mx c= + . Parallel and perpendicular lines, including 1 2m m= and 1 2 1m m = − .

The Cartesian plane: ordered pairs ( , )x y , origin, axes. Mid-point of a line segment and distance between two points in the Cartesian plane.


Descriptive statistics: collection of raw data, display of data in pictorial and diagrammatic forms, including frequency histograms, cumulative frequency graphs.

Obtaining simple statistics from discrete and continuous data, including mean, median, mode, quartiles, range, interquartile range and percentiles.

Calculating probabilities of simple events.


Sylla

bus

Sylla

bus

cont

ent

Top

ic 1

—C

ore:

Alg

ebra

30

hou

rs

The

aim

of t

his t

opic

is to

intro

duce

stud

ents

to so

me

basi

c al

gebr

aic

conc

epts

and

app

licat

ions

.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

1.1

Arit

hmet

ic se

quen

ces a

nd se

ries;

sum

of f

inite

ar

ithm

etic

serie

s; g

eom

etric

sequ

ence

s and

se

ries;

sum

of f

inite

and

infin

ite g

eom

etric

se

ries.

Sigm

a no

tatio

n.

Sequ

ence

s can

be

gene

rate

d an

d di

spla

yed

in

seve

ral w

ays,

incl

udin

g re

curs

ive

func

tions

.

Link

infin

ite g

eom

etric

serie

s with

lim

its o

f co

nver

genc

e in

6.1

.

Int:

The

che

ss le

gend

(Sis

sa ib

n D

ahir)

.

Int:

Ary

abha

tta is

som

etim

es c

onsi

dere

d th

e “f

athe

r of a

lgeb

ra”.

Com

pare

with

al

-Kha

war

izm

i.

Int:

The

use

of s

ever

al a

lpha

bets

in

mat

hem

atic

al n

otat

ion

(eg

first

term

and

co

mm

on d

iffer

ence

of a

n ar

ithm

etic

sequ

ence

).

TOK

: Mat

hem

atic

s and

the

know

er. T

o w

hat

exte

nt sh

ould

mat

hem

atic

al k

now

ledg

e be

co

nsis

tent

with

our

intu

ition

?

TOK

: Mat

hem

atic

s and

the

wor

ld. S

ome

mat

hem

atic

al c

onst

ants

(π

, e, φ

, Fib

onac

ci

num

bers

) app

ear c

onsi

sten

tly in

nat

ure.

Wha

t do

es th

is te

ll us

abo

ut m

athe

mat

ical

kn

owle

dge?

TOK

: Mat

hem

atic

s and

the

know

er. H

ow is

m

athe

mat

ical

intu

ition

use

d as

a b

asis

for

form

al p

roof

? (G

auss

’ met

hod

for a

ddin

g up

in

tege

rs fr

om 1

to 1

00.)

(con

tinue

d)

App

licat

ions

. Ex

ampl

es in

clud

e co

mpo

und

inte

rest

and

po

pula

tion

grow

th.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

(s

ee n

otes

abo

ve)

Aim

8: S

hort-

term

loan

s at h

igh

inte

rest

rate

s. H

ow c

an k

now

ledg

e of

mat

hem

atic

s res

ult i

n in

divi

dual

s bei

ng e

xplo

ited

or p

rote

cted

from

ex

torti

on?

App

l: Ph

ysic

s 7.2

, 13.

2 (r

adio

activ

e de

cay

and

nucl

ear p

hysi

cs).

1.2

Expo

nent

s and

loga

rithm

s.

Law

s of e

xpon

ents

; law

s of l

ogar

ithm

s.

Cha

nge

of b

ase.

Expo

nent

s and

loga

rithm

s are

furth

er

deve

lope

d in

2.4

. A

ppl:

Che

mis

try 1

8.1,

18.

2 (c

alcu

latio

n of

pH

an

d bu

ffer

solu

tions

).

TOK

: The

nat

ure o

f mat

hem

atic

s and

scie

nce.

Wer

e log

arith

ms a

n in

vent

ion

or d

iscov

ery?

(Thi

s to

pic i

s an

oppo

rtuni

ty fo

r tea

cher

s and

stud

ents

to

refle

ct o

n “t

he n

atur

e of m

athe

mat

ics”

.)

1.3

Cou

ntin

g pr

inci

ples

, inc

ludi

ng p

erm

utat

ions

an

d co

mbi

natio

ns.

The

abili

ty to

find

n r

an

d n

rP u

sing

bot

h th

e

form

ula

and

tech

nolo

gy is

exp

ecte

d. L

ink

to

5.4.

TOK

: The

nat

ure

of m

athe

mat

ics.

The

unfo

rese

en li

nks b

etw

een

Pasc

al’s

tria

ngle

, co

untin

g m

etho

ds a

nd th

e co

effic

ient

s of

poly

nom

ials

. Is t

here

an

unde

rlyin

g tru

th th

at

can

be fo

und

linki

ng th

ese?

Int:

The

pro

perti

es o

f Pas

cal’s

tria

ngle

wer

e kn

own

in a

num

ber o

f diff

eren

t cul

ture

s lon

g be

fore

Pas

cal (

eg th

e C

hine

se m

athe

mat

icia

n Y

ang

Hui

).

Aim

8: H

ow m

any

diff

eren

t tic

kets

are

po

ssib

le in

a lo

ttery

? W

hat d

oes t

his t

ell u

s ab

out t

he e

thic

s of s

ellin

g lo

ttery

tick

ets t

o th

ose

who

do

not u

nder

stan

d th

e im

plic

atio

ns

of th

ese

larg

e nu

mbe

rs?

The

bino

mia

l the

orem

:

expa

nsio

n of

()n

ab

+,

n∈

.

Not

req

uire

d:

Perm

utat

ions

whe

re so

me

obje

cts a

re id

entic

al.

Circ

ular

arr

ange

men

ts.

Proo

f of b

inom

ial t

heor

em.

Link

to 5

.6, b

inom

ial d

istri

butio

n.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

1.4

Proo

f by

mat

hem

atic

al in

duct

ion.

Li

nks t

o a

wid

e va

riety

of t

opic

s, fo

r exa

mpl

e,

com

plex

num

bers

, diff

eren

tiatio

n, su

ms o

f se

ries a

nd d

ivis

ibili

ty.

TOK

: Nat

ure

of m

athe

mat

ics a

nd sc

ienc

e.

Wha

t are

the

diff

eren

t mea

ning

s of i

nduc

tion

in

mat

hem

atic

s and

scie

nce?

TOK

: Kno

wle

dge

clai

ms i

n m

athe

mat

ics.

Do

proo

fs p

rovi

de u

s with

com

plet

ely

certa

in

know

ledg

e?

TOK

: Kno

wle

dge

com

mun

ities

. Who

judg

es

the

valid

ity o

f a p

roof

?

1.5

Com

plex

num

bers

: the

num

ber

i1

=−

; the

te

rms r

eal p

art,

imag

inar

y pa

rt, c

onju

gate

, m

odul

us a

nd a

rgum

ent.

Car

tesi

an fo

rm

iz

ab

=+

.

Sum

s, pr

o duc

ts a

nd q

uotie

nts o

f com

plex

nu

mbe

rs.

Whe

n so

lvin

g pr

oble

ms,

stud

ents

may

nee

d to

us

e te

chno

logy

. A

ppl:

Con

cept

s in

elec

trica

l eng

inee

ring.

Im

peda

nce

as a

com

bina

tion

of re

sist

ance

and

re

acta

nce;

als

o ap

pare

nt p

ower

as a

co

mbi

natio

n of

real

and

reac

tive

pow

ers.

Thes

e co

mbi

natio

ns ta

ke th

e fo

rm

iz

ab

=+

.

TOK

: Mat

hem

atic

s and

the

know

er. D

o th

e w

ords

imag

inar

y an

d co

mpl

ex m

ake

the

conc

epts

mor

e di

ffic

ult t

han

if th

ey h

ad

diff

eren

t nam

es?

TOK

: The

nat

ure

of m

athe

mat

ics.

Has

“i”

be

en in

vent

ed o

r was

it d

isco

vere

d?

TOK

: Mat

hem

atic

s and

the

wor

ld. W

hy d

oes

“i”

appe

ar in

so m

any

fund

amen

tal l

aws o

f ph

ysic

s?


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

1.6

Mod

ulus

–arg

umen

t (po

lar)

form

i(c

osis

in)

cis

ez

rr

rθ

θθ

θ=

+=

=.

i erθ

is a

lso

know

n as

Eul

er’s

form

.

The

abili

ty to

con

vert

betw

een

form

s is

expe

cted

.

App

l: C

once

pts i

n el

ectri

cal e

ngin

eerin

g.

Phas

e an

gle/

shift

, pow

er fa

ctor

and

app

aren

t po

wer

as a

com

plex

qua

ntity

in p

olar

form

.

TOK

: The

nat

ure

of m

athe

mat

ics.

Was

the

com

plex

pla

ne a

lread

y th

ere

befo

re it

was

use

d to

repr

esen

t com

plex

num

bers

geo

met

rical

ly?

TOK

: Mat

hem

atic

s and

the

know

er. W

hy

mig

ht it

be

said

that

i e

10

π+

= is

bea

utifu

l?

The

com

plex

pla

ne.

The

com

plex

pla

ne is

als

o kn

own

as th

e A

rgan

d di

agra

m.

1.7

Pow

ers o

f com

plex

num

bers

: de

Moi

vre’

s th

eore

m.

nth ro

ots o

f a c

ompl

ex n

umbe

r.

Proo

f by

mat

hem

atic

al in

duct

ion

for

n+

∈

. TO

K: R

easo

n an

d m

athe

mat

ics.

Wha

t is

mat

hem

atic

al re

ason

ing

and

wha

t rol

e do

es

proo

f pla

y in

this

form

of r

easo

ning

? A

re th

ere

exam

ples

of p

roof

that

are

not

mat

hem

atic

al?

1.8

Con

juga

te ro

ots o

f pol

ynom

ial e

quat

ions

with

re

al c

oeff

icie

nts.

Link

to 2

.5 a

nd 2

.7.

1.9

Solu

tions

of s

yste

ms o

f lin

ear e

quat

ions

(a

max

imum

of t

hree

equ

atio

ns in

thre

e un

know

ns),

incl

udin

g ca

ses w

here

ther

e is

a

uniq

ue so

lutio

n, a

n in

finity

of s

olut

ions

or n

o so

lutio

n.

Thes

e sy

stem

s sho

uld

be so

lved

usi

ng b

oth

alge

brai

c an

d te

chno

logi

cal m

etho

ds, e

g ro

w

redu

ctio

n.

Syst

ems t

hat h

ave

solu

tion(

s) m

ay b

e re

ferr

ed

to a

s con

sist

ent.

Whe

n a

syst

em h

as a

n in

finity

of s

olut

ions

, a

gene

ral s

olut

ion

may

be

requ

ired.

Link

to v

ecto

rs in

4.7

.

TO

K: M

athe

mat

ics,

sens

e, p

erce

ptio

n an

d re

ason

. If w

e ca

n fin

d so

lutio

ns in

hig

her

dim

ensi

ons,

can

we

reas

on th

at th

ese

spac

es

exis

t bey

ond

our s

ense

per

cept

ion?


Syllabus content

Top

ic 2

—C

ore:

Fun

ctio

ns a

nd e

quat

ions

22

hou

rs

The

aim

s of

thi

s to

pic

are

to e

xplo

re t

he n

otio

n of

fun

ctio

n as

a u

nify

ing

them

e in

mat

hem

atic

s, an

d to

app

ly f

unct

iona

l m

etho

ds t

o a

varie

ty o

f m

athe

mat

ical

situ

atio

ns. I

t is e

xpec

ted

that

ext

ensi

ve u

se w

ill b

e m

ade

of te

chno

logy

in b

oth

the

deve

lopm

ent a

nd th

e ap

plic

atio

n of

this

topi

c.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

2.1

Con

cept

of f

unct

ion

:(

)f

xf

x

: dom

ain,

ra

nge;

imag

e (v

alue

).

Odd

and

eve

n fu

nctio

ns.

In

t: T

he n

otat

ion

for f

unct

ions

was

dev

elop

ed

by a

num

ber o

f diff

eren

t mat

hem

atic

ians

in th

e 17

th a

nd 1

8th c

entu

ries.

How

did

the

nota

tion

we

use

toda

y be

com

e in

tern

atio

nally

acc

epte

d?

TOK

: The

nat

ure

of m

athe

mat

ics.

Is

mat

hem

atic

s sim

ply

the

man

ipul

atio

n of

sy

mbo

ls u

nder

a se

t of f

orm

al ru

les?

Com

posi

te fu

nctio

ns f

g

.

Iden

tity

func

tion.

()(

)(

())

fg

xf

gx

=

. Lin

k w

ith 6

.2.

One

-to-o

ne a

nd m

any-

to-o

ne fu

nctio

ns.

Link

with

3.4

.

Inve

rse

func

tion

1f−

, inc

ludi

ng d

omai

n re

stric

tion.

Sel

f-inv

erse

func

tions

. Li

nk w

ith 6

.2.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

2.2

The

grap

h of

a fu

nctio

n; it

s equ

atio

n (

)y

fx

=.

T

OK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

Doe

s stu

dyin

g th

e gr

aph

of a

func

tion

cont

ain

the

sam

e le

vel o

f mat

hem

atic

al ri

gour

as

stud

ying

the

func

tion

alge

brai

cally

(a

naly

tical

ly)?

App

l: Sk

etch

ing

and

inte

rpre

ting

grap

hs;

Geo

grap

hy S

L/H

L (g

eogr

aphi

c sk

ills)

; C

hem

istry

11.

3.1.

Int:

Bou

rbak

i gro

up a

naly

tical

app

roac

h ve

rsus

M

andl

ebro

t vis

ual a

ppro

ach.

Inve

stiga

tion

of k

ey fe

atur

es o

f gra

phs,

such

as

max

imum

and

min

imum

val

ues,

inte

rcep

ts,

horiz

onta

l and

ver

tical

asym

ptot

es an

d sy

mm

etry

, an

d co

nsid

erat

ion

of d

omai

n an

d ra

nge.

The

grap

hs o

f the

func

tions

(

)y

fx

= a

nd

()

yf

x=

.

The

grap

h of

()

1y

fx

= g

iven

the

grap

h of

()

yf

x=

.

Use

of t

echn

olog

y to

gra

ph a

var

iety

of

func

tions

.

2.3

Tran

sfor

mat

ions

of g

raph

s: tr

ansl

atio

ns;

stre

tche

s; re

flect

ions

in th

e ax

es.

The

grap

h of

the

inve

rse

func

tion

as a

re

flect

ion

in y

x=

.

Link

to 3

.4. S

tude

nts a

re e

xpec

ted

to b

e aw

are

of th

e ef

fect

of t

rans

form

atio

ns o

n bo

th th

e al

gebr

aic

expr

essi

on a

nd th

e gr

aph

of a

fu

nctio

n.

App

l: Ec

onom

ics S

L/H

L 1.

1 (s

hift

in d

eman

d an

d su

pply

cur

ves)

.

2.4

The

ratio

nal f

unct

ion

,ax

bx

cxd

+ +

and

its

grap

h.

The

reci

proc

al fu

nctio

n is

a p

artic

ular

cas

e.

Gra

phs s

houl

d in

clud

e bo

th a

sym

ptot

es a

nd

any

inte

rcep

ts w

ith a

xes.

The

func

tion

xx

a

, 0

a>

, and

its g

raph

.

The

func

tion

log a

xx

,

0x>

, and

its g

raph

.

Expo

nent

ial a

nd lo

garit

hmic

func

tions

as

inve

rses

of e

ach

othe

r.

Link

to 6

.2 a

nd th

e si

gnifi

canc

e of

e.

App

licat

ion

of c

once

pts i

n 2.

1, 2

.2 a

nd 2

.3.

App

l: G

eogr

aphy

SL/

HL

(geo

grap

hic

skill

s);

Phys

ics S

L/H

L 7.

2 (r

adio

activ

e de

cay)

; C

hem

istry

SL/

HL

16.3

(act

ivat

ion

ener

gy);

Econ

omic

s SL/

HL

3.2

(exc

hang

e ra

tes)

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

2.5

Poly

nom

ial f

unct

ions

and

thei

r gra

phs.

The

fact

or a

nd re

mai

nder

theo

rem

s.

The

fund

amen

tal t

heor

em o

f alg

ebra

.

The

grap

hica

l sig

nific

ance

of r

epea

ted

fact

ors.

The

rela

tions

hip

betw

een

the

degr

ee o

f a

poly

nom

ial f

unct

ion

and

the

poss

ible

num

bers

of

x-in

terc

epts

.

2.6

Solv

ing

quad

ratic

equ

atio

ns u

sing

the

quad

ratic

fo

rmul

a.

Use

of t

he d

iscr

imin

ant

24

bac

∆=

− to

de

term

ine

the

natu

re o

f the

root

s.

May

be

refe

rred

to a

s roo

ts o

f equ

atio

ns o

r ze

ros o

f fun

ctio

ns.

App

l: C

hem

istry

17.

2 (e

quili

briu

m la

w).

App

l: Ph

ysic

s 2.1

(kin

emat

ics)

.

App

l: Ph

ysic

s 4.2

(ene

rgy

chan

ges i

n si

mpl

e ha

rmon

ic m

otio

n).

App

l: Ph

ysic

s (H

L on

ly) 9

.1 (p

roje

ctile

m

otio

n).

Aim

8: T

he p

hras

e “e

xpon

entia

l gro

wth

” is

us

ed p

opul

arly

to d

escr

ibe

a nu

mbe

r of

phen

omen

a. Is

this

a m

isle

adin

g us

e of

a

mat

hem

atic

al te

rm?

Solv

ing

poly

nom

ial e

quat

ions

bot

h gr

aphi

cally

an

d al

gebr

aica

lly.

Sum

and

pro

duct

of t

he ro

ots o

f pol

ynom

ial

equa

tions

.

Link

the

solu

tion

of p

olyn

omia

l equ

atio

ns to

co

njug

ate

root

s in

1.8.

For t

he p

olyn

omia

l equ

atio

n 0

0n

rr

ra

x=

=∑

,

the

sum

is

1n na a−

−,

the

prod

uct i

s 0

(1)

n n

aa

−.

Solu

tion

of

x ab

= u

sing

loga

rithm

s.

Use

of t

echn

olog

y to

solv

e a

varie

ty o

f eq

uatio

ns, i

nclu

ding

thos

e w

here

ther

e is

no

appr

opria

te a

naly

tic a

ppro

ach.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

2.7

Solu

tions

of

()

()

gx

fx

≥.

Gra

phic

al o

r alg

ebra

ic m

etho

ds, f

or si

mpl

e po

lyno

mia

ls u

p to

deg

ree

3 .

Use

of t

echn

olog

y fo

r the

se an

d ot

her f

unct

ions

.


Syllabus content

Top

ic 3

—C

ore:

Cir

cula

r fu

nctio

ns a

nd t

rigo

nom

etry

22

hou

rs

The

aim

s of

this

topi

c ar

e to

exp

lore

the

circ

ular

func

tions

, to

intro

duce

som

e im

porta

nt tr

igon

omet

ric id

entit

ies

and

to s

olve

tria

ngle

s us

ing

trigo

nom

etry

. O

n ex

amin

atio

n pa

pers

, rad

ian

mea

sure

shou

ld b

e as

sum

ed u

nles

s oth

erw

ise

indi

cate

d, fo

r exa

mpl

e, b

y si

nx

x

°.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

3.1

The

circ

le: r

adia

n m

easu

re o

f ang

les.

Leng

th o

f an

arc;

are

a of

a se

ctor

.

Rad

ian

mea

sure

may

be

expr

esse

d as

mul

tiple

s of

π, o

r dec

imal

s. Li

nk w

ith 6

.2.

Int:

The

orig

in o

f deg

rees

in th

e m

athe

mat

ics

of M

esop

otam

ia a

nd w

hy w

e us

e m

inut

es a

nd

seco

nds f

or ti

me.

TOK

: Mat

hem

atic

s and

the

know

er. W

hy d

o w

e us

e ra

dian

s? (T

he a

rbitr

ary

natu

re o

f deg

ree

mea

sure

ver

sus r

adia

ns a

s rea

l num

bers

and

the

impl

icat

ions

of u

sing

thes

e tw

o m

easu

res o

n th

e sh

ape

of si

nuso

idal

gra

phs.)

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

If tri

gono

met

ry is

bas

ed o

n rig

ht tr

iang

les,

how

ca

n w

e se

nsib

ly c

onsi

der t

rigon

omet

ric ra

tios

of a

ngle

s gre

ater

than

a ri

ght a

ngle

?

Int:

The

orig

in o

f the

wor

d “s

ine”

.

App

l: Ph

ysic

s SL/

HL

2.2

(for

ces a

nd

dyna

mic

s).

App

l: Tr

iang

ulat

ion

used

in th

e G

loba

l Po

sitio

ning

Sys

tem

(GPS

).

Int:

Why

did

Pyt

hago

ras l

ink

the

stud

y of

m

usic

and

mat

hem

atic

s?

App

l: C

once

pts i

n el

ectri

cal e

ngin

eerin

g.

Gen

erat

ion

of si

nuso

idal

vol

tage

.

(con

tinue

d)

3.2

Def

initi

on o

f co

sθ, s

inθ

and

tanθ

in te

rms

of th

e un

it ci

rcle

.

Exac

t val

ues o

f sin

, cos

and

tan

of

0,,

,,

64

32

ππ

ππ

and

thei

r mul

tiple

s.

Def

initi

on o

f the

reci

proc

al tr

igon

omet

ric

ratio

s se

cθ, c

scθ

and

cotθ

.

Pyth

agor

ean

iden

titie

s:

22

cos

sin

1θ

θ+

=;

22

1ta

nse

cθ

θ+

=;

22

1co

tcs

cθ

θ+

=.

3.3

Com

poun

d an

gle

iden

titie

s.

Dou

ble

angl

e id

entit

ies.

Not

req

uire

d:

Proo

f of c

ompo

und

angl

e id

entit

ies.

Der

ivat

ion

of d

oubl

e an

gle

iden

titie

s fro

m

com

poun

d an

gle

iden

titie

s.

Find

ing

poss

ible

val

ues o

f trig

onom

etric

ratio

s w

ithou

t fin

ding

θ, f

or e

xam

ple,

find

ing

sin

2θ

give

n si

nθ.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

3.4

Com

posi

te fu

nctio

ns o

f the

form

(

)si

n((

))f

xa

bx

cd

=+

+.

App

licat

ions

.

(s

ee n

otes

abo

ve)

TOK

: Mat

hem

atic

s and

the

wor

ld. M

usic

can

be

exp

ress

ed u

sing

mat

hem

atic

s. D

oes t

his

mea

n th

at m

usic

is m

athe

mat

ical

, tha

t m

athe

mat

ics i

s mus

ical

or t

hat b

oth

are

refle

ctio

ns o

f a c

omm

on “

truth

”?

App

l: Ph

ysic

s SL/

HL

4.1

(kin

emat

ics o

f si

mpl

e ha

rmon

ic m

otio

n).

3.5

The

inve

rse

func

tions

ar

csin

xx

,

arcc

osx

x

, ar

ctan

xx

; t

heir

dom

ains

and

ra

nges

; the

ir gr

aphs

.

3.6

Alg

ebra

ic a

nd g

raph

ical

met

hods

of s

olvi

ng

trigo

nom

etric

equ

atio

ns in

a fi

nite

inte

rval

, in

clud

ing

the

use

of tr

igon

omet

ric id

entit

ies

and

fact

oriz

atio

n.

Not

req

uire

d:

The

gene

ral s

olut

ion

of tr

igon

omet

ric

equa

tions

.

TO

K: M

athe

mat

ics a

nd k

now

ledg

e cl

aim

s. H

ow c

an th

ere

be a

n in

finite

num

ber o

f di

scre

te so

lutio

ns to

an

equa

tion?

3.7

The

cosi

ne ru

le

The

sine

rule

incl

udin

g th

e am

bigu

ous c

ase.

Are

a of

a tr

iang

le a

s 1

sin

2ab

C.

TO

K: N

atur

e of

mat

hem

atic

s. If

the

angl

es o

f a

trian

gle

can

add

up to

less

than

180

°, 18

0° o

r m

ore

than

180

°, w

hat d

oes t

his t

ell u

s abo

ut th

e “f

act”

of t

he a

ngle

sum

of a

tria

ngle

and

abo

ut

the

natu

re o

f mat

hem

atic

al k

now

ledg

e?

App

licat

ions

. Ex

ampl

es in

clud

e na

viga

tion,

pro

blem

s in

two

and

thre

e di

men

sion

s, in

clud

ing

angl

es o

f el

evat

ion

and

depr

essi

on.

App

l: Ph

ysic

s SL/

HL

1.3

(vec

tors

and

scal

ars)

; Ph

ysic

s SL/

HL

2.2

(for

ces a

nd d

ynam

ics)

.

Int:

The

use

of t

riang

ulat

ion

to fi

nd th

e cu

rvat

ure

of th

e Ea

rth in

ord

er to

settl

e a

disp

ute

betw

een

Engl

and

and

Fran

ce o

ver

New

ton’

s gra

vity

.


Syllabus content

Top

ic 4

—C

ore:

Vec

tors

24

hou

rs

The

aim

of t

his t

opic

is to

intro

duce

the

use

of v

ecto

rs in

two

and

thre

e di

men

sion

s, an

d to

faci

litat

e so

lvin

g pr

oble

ms i

nvol

ving

poi

nts,

lines

and

pla

nes.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

4.1

Con

cept

of a

vec

tor.

Rep

rese

ntat

ion

of v

ecto

rs u

sing

dire

cted

line

se

gmen

ts.

Uni

t vec

tors

; bas

e ve

ctor

s i, j

, k.

A

im 8

: Vec

tors

are

use

d to

solv

e m

any

prob

lem

s in

posi

tion

loca

tion.

Thi

s can

be

used

to

save

a lo

st sa

ilor o

r des

troy

a bu

ildin

g w

ith a

la

ser-g

uide

d bo

mb.

Com

pone

nts o

f a v

ecto

r:

1 21

23

3

.v v

vv

vv

==

++

vi

jk

A

ppl:

Phys

ics S

L/H

L 1.

3 (v

ecto

rs a

nd sc

alar

s);

Phys

ics S

L/H

L 2.

2 (f

orce

s and

dyn

amic

s).

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

You

can

per

form

som

e pr

oofs

usi

ng d

iffer

ent

mat

hem

atic

al c

once

pts.

Wha

t doe

s thi

s tel

l us

abou

t mat

hem

atic

al k

now

ledg

e?

Alg

ebra

ic a

nd g

eom

etric

app

roac

hes t

o th

e fo

llow

ing:

• th

e su

m a

nd d

iffer

ence

of t

wo

vect

ors;

• th

e ze

ro v

ecto

r 0

, the

vec

tor −v

;

• m

ultip

licat

ion

by a

scal

ar, k

v;

• m

agni

tude

of a

vec

tor,

v;

• po

sitio

n ve

ctor

s O

A→

=a

.

Proo

fs o

f geo

met

rical

pro

perti

es u

sing

vec

tors

.

AB→

=−

ba

D

ista

nce

betw

een

poin

ts A

and

B is

the

mag

nitu

de o

f A

B→

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

4.2

The

defin

ition

of t

he sc

alar

pro

duct

of t

wo

vect

ors.

Prop

ertie

s of t

he sc

alar

pro

duct

:

⋅=

⋅v

ww

v;

()

⋅+

=⋅+

⋅u

vw

uv

uw

;

()

()

kk

⋅=

⋅v

wv

w;

2⋅=

vv

v.

The

angl

e be

twee

n tw

o ve

ctor

s.

Perp

endi

cula

r vec

tors

; par

alle

l vec

tors

.

cosθ

⋅=

vw

vw

, whe

re θ

is th

e an

gle

betw

een

vand

w.

Link

to 3

.6.

For n

on-z

ero

vect

ors,

0⋅

=v

w is

equ

ival

ent t

o th

e ve

ctor

s bei

ng p

erpe

ndic

ular

.

For p

aral

lel v

ecto

rs,

⋅=

vw

vw

.

App

l: Ph

ysic

s SL/

HL

2.2

(for

ces a

nd

dyna

mic

s).

TO

K: T

he n

atur

e of

mat

hem

atic

s. W

hy th

is

defin

ition

of s

cala

r pro

duct

?

4.3

Vec

tor e

quat

ion

of a

line

in tw

o an

d th

ree

dim

ensi

ons:

λ

=r

a+

b.

Sim

ple

appl

icat

ions

to k

inem

atic

s.

The

angl

e be

twee

n tw

o lin

es.

Kno

wle

dge

of th

e fo

llow

ing

form

s for

eq

uatio

ns o

f lin

es.

Para

met

ric fo

rm:

0x

xlλ

=+

, 0

yy

mλ=

+,

0z

znλ

=+

.

Car

tesi

an fo

rm:

00

0x

xy

yz

zl

mn

−−

−=

=.

App

l: M

odel

ling

linea

r mot

ion

in th

ree

dim

ensi

ons.

App

l: N

avig

atio

nal d

evic

es, e

g G

PS.

TO

K: T

he n

atur

e of

mat

hem

atic

s. W

hy m

ight

it

be a

rgue

d th

at v

ecto

r rep

rese

ntat

ion

of li

nes

is su

perio

r to

Carte

sian

?

4.4

Coi

ncid

ent,

para

llel,

inte

rsec

ting

and

skew

lin

es; d

istin

guis

hing

bet

wee

n th

ese

case

s.

Poin

ts o

f int

erse

ctio

n.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

4.5

The

defin

ition

of t

he v

ecto

r pro

duct

of t

wo

vect

ors.

Prop

ertie

s of t

he v

ecto

r pro

duct

:

×=−

×v

ww

v;

()

×+

=×

+×

uv

wu

vu

w;

()

()

kk

×=

×v

wv

w;

×=

0v

v.

sinθ

×=

vw

vw

n, w

here

θ is

the

angl

e be

twee

n v

and

w a

nd n

is th

e un

it no

rmal

ve

ctor

who

se d

irect

ion

is g

iven

by

the

right

-ha

nd sc

rew

rule

.

App

l: Ph

ysic

s SL/

HL

6.3

(mag

netic

forc

e an

d fie

ld).

Geo

met

ric in

terp

reta

tion

of

×vw

. A

reas

of t

riang

les a

nd p

aral

lelo

gram

s.

4.6

Vec

tor e

quat

ion

of a

pla

ne

λµ

=+

+r

ab

c.

Use

of n

orm

al v

ecto

r to

obta

in th

e fo

rm

⋅=

⋅r

na

n.

Car

tesi

an e

quat

ion

of a

pla

ne a

xby

czd

++

=.

4.7

Inte

rsec

tions

of:

a lin

e w

ith a

pla

ne; t

wo

plan

es; t

hree

pla

nes.

Ang

le b

etw

een:

a li

ne a

nd a

pla

ne; t

wo

plan

es.

Link

to 1

.9.

Geo

met

rical

inte

rpre

tatio

n of

solu

tions

.

TO

K: M

athe

mat

ics a

nd th

e kn

ower

. Why

are

sy

mbo

lic re

pres

enta

tions

of t

hree

-dim

ensi

onal

ob

ject

s eas

ier t

o de

al w

ith th

an v

isua

l re

pres

enta

tions

? W

hat d

oes t

his t

ell u

s abo

ut

our k

now

ledg

e of

mat

hem

atic

s in

othe

r di

men

sion

s?


Syllabus content

Top

ic 5

—C

ore:

Sta

tistic

s an

d pr

obab

ility

36

hou

rs

The

aim

of t

his

topi

c is

to in

trodu

ce b

asic

con

cept

s. It

may

be

cons

ider

ed a

s th

ree

parts

: man

ipul

atio

n an

d pr

esen

tatio

n of

sta

tistic

al d

ata

(5.1

), th

e la

ws

of

prob

abili

ty (5

.2–5

.4),

and

rand

om v

aria

bles

and

thei

r pro

babi

lity

dist

ribut

ions

(5.5

–5.7

). It

is e

xpec

ted

that

mos

t of t

he c

alcu

latio

ns re

quire

d w

ill b

e do

ne o

n a

GD

C. T

he e

mph

asis

is o

n un

ders

tand

ing

and

inte

rpre

ting

the

resu

lts o

btai

ned.

Sta

tistic

al ta

bles

will

no

long

er b

e al

low

ed in

exa

min

atio

ns.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

5.1

Con

cept

s of p

opul

atio

n, sa

mpl

e, ra

ndom

sa

mpl

e an

d fr

eque

ncy

dist

ribut

ion

of d

iscr

ete

and

cont

inuo

us d

ata.

Gro

uped

dat

a: m

id-in

terv

al v

alue

s, in

terv

al

wid

th, u

pper

and

low

er in

terv

al b

ound

arie

s.

Mea

n, v

aria

nce,

stan

dard

dev

iatio

n.

Not

req

uire

d:

Estim

atio

n of

mea

n an

d va

rianc

e of

a

popu

latio

n fr

om a

sam

ple.

For e

xam

inat

ion

purp

oses

, in

pape

rs 1

and

2

data

will

be

treat

ed a

s the

pop

ulat

ion.

In e

xam

inat

ions

the

follo

win

g fo

rmul

ae sh

ould

be

use

d:

1k

ii

ifx n

µ=

=∑

,

22

22

11

()

kk

ii

ii

ii

fx

fx

nn

µσ

µ=

=

−=

=−

∑∑

.

TO

K: T

he n

atur

e of

mat

hem

atic

s. W

hy h

ave

mat

hem

atic

s and

stat

istic

s som

etim

es b

een

treat

ed a

s sep

arat

e su

bjec

ts?

TO

K: T

he n

atur

e of

kno

win

g. Is

ther

e a

diff

eren

ce b

etw

een

info

rmat

ion

and

data

?

Aim

8: D

oes t

he u

se o

f sta

tistic

s lea

d to

an

over

emph

asis

on

attri

bute

s tha

t can

eas

ily b

e m

easu

red

over

thos

e th

at c

anno

t?

App

l: Ps

ycho

logy

SL/

HL

(des

crip

tive

stat

istic

s); G

eogr

aphy

SL/

HL

(geo

grap

hic

skill

s); B

iolo

gy S

L/H

L 1.

1.2

(sta

tistic

al

anal

ysis

).

App

l: M

etho

ds o

f col

lect

ing

data

in re

al li

fe

(cen

sus v

ersu

s sam

plin

g).

App

l: M

isle

adin

g st

atis

tics i

n m

edia

repo

rts.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

5.2

Con

cept

s of t

rial,

outc

ome,

equ

ally

like

ly

outc

omes

, sam

ple

spac

e (U

) and

eve

nt.

The

prob

abili

ty o

f an

even

t A a

s (

)P(

)(

)n

AA

nU

=.

The

com

plem

enta

ry e

vent

s A a

nd A′ (

not A

).

Use

of V

enn

diag

ram

s, tre

e di

agra

ms,

coun

ting

prin

cipl

es a

nd ta

bles

of o

utco

mes

to so

lve

prob

lem

s.

A

im 8

: Why

has

it b

een

argu

ed th

at th

eorie

s ba

sed

on th

e ca

lcul

able

pro

babi

litie

s fou

nd in

ca

sino

s are

per

nici

ous w

hen

appl

ied

to

ever

yday

life

(eg

econ

omic

s)?

Int:

The

dev

elop

men

t of t

he m

athe

mat

ical

th

eory

of p

roba

bilit

y in

17th

cen

tury

Fra

nce.

5.3

Com

bine

d ev

ents

; the

form

ula

for

P()

AB

∪.

Mut

ually

exc

lusi

ve e

vent

s.

5.4

Con

ditio

nal p

roba

bilit

y; th

e de

finiti

on

()

P()

P|

P()

AB

AB

B∩=

.

A

ppl:

Use

of p

roba

bilit

y m

etho

ds in

med

ical

st

udie

s to

asse

ss ri

sk fa

ctor

s for

cer

tain

di

seas

es.

TO

K: M

athe

mat

ics a

nd k

now

ledg

e cl

aim

s. Is

in

depe

nden

ce a

s def

ined

in p

roba

bilis

tic te

rms

the

sam

e as

that

foun

d in

nor

mal

exp

erie

nce?

In

depe

nden

t eve

nts;

the

defin

ition

(

)(

)(

)P

|P

P|

AB

AA

B′=

=.

Use

of B

ayes

’ the

orem

for a

max

imum

of t

hree

ev

ents

.

Use

of

P()

P()P

()

AB

AB

∩=

to sh

ow

inde

pend

ence

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

5.5

Con

cept

of d

iscr

ete

and

cont

inuo

us ra

ndom

va

riabl

es a

nd th

eir p

roba

bilit

y di

strib

utio

ns.

Def

initi

on an

d us

e of p

roba

bilit

y de

nsity

func

tions

.

TO

K: M

athe

mat

ics a

nd th

e kn

ower

. To

wha

t ex

tent

can

we

trust

sam

ples

of d

ata?

Expe

cted

val

ue (m

ean)

, mod

e, m

edia

n,

varia

nce

and

stan

dard

dev

iatio

n.

For a

con

tinuo

us ra

ndom

var

iabl

e, a

val

ue a

t w

hich

the

prob

abili

ty d

ensi

ty fu

nctio

n ha

s a

max

imum

val

ue is

cal

led

a m

ode.

App

licat

ions

. Ex

ampl

es in

clud

e ga

mes

of c

hanc

e.

App

l: Ex

pect

ed g

ain

to in

sura

nce

com

pani

es.

5.6

Bin

omia

l dis

tribu

tion,

its m

ean

and

varia

nce.

Pois

son

dist

ribut

ion,

its m

ean

and

varia

nce.

Link

to b

inom

ial t

heor

em in

1.3

.

Con

ditio

ns u

nder

whi

ch ra

ndom

var

iabl

es h

ave

thes

e di

strib

utio

ns.

TOK

: Mat

hem

atic

s and

the

real

wor

ld. I

s the

bi

nom

ial d

istri

butio

n ev

er a

use

ful m

odel

for

an a

ctua

l rea

l-wor

ld si

tuat

ion?

Not

req

uire

d:

Form

al p

roof

of m

eans

and

var

ianc

es.

5.7

Nor

mal

dis

tribu

tion.

Pr

obab

ilitie

s and

val

ues o

f the

var

iabl

e m

ust b

e fo

und

usin

g te

chno

logy

.

The

stan

dard

ized

val

ue (z

) giv

es th

e nu

mbe

r of

stan

dard

dev

iatio

ns fr

om th

e m

ean.

App

l: C

hem

istry

SL/

HL

6.2

(col

lisio

n th

eory

); Ps

ycho

logy

HL

(des

crip

tive

stat

istic

s); B

iolo

gy

SL/H

L 1.

1.3

(sta

tistic

al a

naly

sis)

.

Aim

8: W

hy m

ight

the

mis

use

of th

e no

rmal

di

strib

utio

n le

ad to

dan

gero

us in

fere

nces

and

co

nclu

sion

s?

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

To

wha

t ext

ent c

an w

e tru

st m

athe

mat

ical

mod

els

such

as t

he n

orm

al d

istri

butio

n?

Int:

De

Moi

vre’

s der

ivat

ion

of th

e no

rmal

di

strib

utio

n an

d Q

uete

let’s

use

of i

t to

desc

ribe

l’hom

me

moy

en.

Prop

ertie

s of t

he n

orm

al d

istrib

utio

n.

Stan

dard

izat

ion

of n

orm

al v

aria

bles

.

Link

to 2

.3.


Syllabus content

Top

ic 6

—C

ore:

Cal

culu

s 48

hou

rs

The

aim

of t

his t

opic

is to

intro

duce

stud

ents

to th

e ba

sic

conc

epts

and

tech

niqu

es o

f diff

eren

tial a

nd in

tegr

al c

alcu

lus a

nd th

eir a

pplic

atio

n.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

6.1

Info

rmal

idea

s of l

imit,

con

tinui

ty a

nd

conv

erge

nce.

Def

initi

on o

f der

ivat

ive

from

firs

t prin

cipl

es

0

()

()

()

lim h

fx

hf

xf

xh

→

+−

′=

.

The

deriv

ativ

e in

terp

rete

d as

a g

radi

ent

func

tion

and

as a

rate

of c

hang

e.

Find

ing

equa

tions

of t

ange

nts a

nd n

orm

als.

Iden

tifyi

ng in

crea

sing

and

dec

reas

ing

func

tions

.

Incl

ude

resu

lt 0

sin

lim1

θ

θθ

→=

.

Link

to 1

.1.

Use

of t

his d

efin

ition

for p

olyn

omia

ls o

nly.

Link

to b

inom

ial t

heor

em in

1.3

.

Bot

h fo

rms o

f not

atio

n, d dy x

and

()

fx

′, f

or th

e

first

der

ivat

ive.

TOK

: The

nat

ure

of m

athe

mat

ics.

Doe

s the

fa

ct th

at L

eibn

iz a

nd N

ewto

n ca

me

acro

ss th

e ca

lcul

us a

t sim

ilar t

imes

supp

ort t

he a

rgum

ent

that

mat

hem

atic

s exi

sts p

rior t

o its

dis

cove

ry?

Int:

How

the

Gre

eks’

dis

trust

of z

ero

mea

nt

that

Arc

him

edes

’ wor

k di

d no

t lea

d to

cal

culu

s.

Int:

Inve

stig

ate

atte

mpt

s by

Indi

an

mat

hem

atic

ians

(500

–100

0 C

E) to

exp

lain

di

visi

on b

y ze

ro.

TOK

: Mat

hem

atic

s and

the

know

er. W

hat

does

the

disp

ute

betw

een

New

ton

and

Leib

niz

tell

us a

bout

hum

an e

mot

ion

and

mat

hem

atic

al

disc

over

y?

App

l: Ec

onom

ics H

L 1.

5 (th

eory

of t

he fi

rm);

Che

mis

try S

L/H

L 11

.3.4

(gra

phic

al

tech

niqu

es);

Phys

ics S

L/H

L 2.

1 (k

inem

atic

s).

The

seco

nd d

eriv

ativ

e.

Hig

her d

eriv

ativ

es.

Use

of b

oth

alge

bra

and

tech

nolo

gy.

B

oth

form

s of n

otat

ion,

2

2

d dy x

and

(

)f

x′′

, for

the

seco

nd d

eriv

ativ

e.

Fam

iliar

ity w

ith th

e no

tatio

n d dn

ny x a

nd

() (

)n

fx

. Lin

k w

ith in

duct

ion

in 1

.4.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

6.2

Der

ivat

ives

of

n x, s

inx

, cos

x, t

anx

, ex a

nd

lnx.

Diff

eren

tiatio

n of

sum

s and

mul

tiple

s of

func

tions

.

The

prod

uct a

nd q

uotie

nt ru

les.

The

chai

n ru

le fo

r com

posi

te fu

nctio

ns.

Rel

ated

rate

s of c

hang

e.

Impl

icit

diff

eren

tiatio

n.

Der

ivat

ives

of

secx

, csc

x, c

otx,

x a, l

oga

x,

arcs

inx,

arc

cosx

and

arc

tan

x.

A

ppl:

Phys

ics H

L 2.

4 (u

nifo

rm ci

rcul

ar m

otio

n);

Phys

ics 1

2.1

(indu

ced

elec

trom

otiv

e for

ce (e

mf))

.

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

Eule

r was

abl

e to

mak

e im

porta

nt a

dvan

ces i

n m

athe

mat

ical

ana

lysi

s bef

ore

calc

ulus

had

bee

n pu

t on

a so

lid th

eore

tical

foun

datio

n by

Cau

chy

and

othe

rs. H

owev

er, s

ome

wor

k w

as n

ot

poss

ible

unt

il af

ter C

auch

y’s w

ork.

Wha

t doe

s th

is te

ll us

abo

ut th

e im

porta

nce

of p

roof

and

th

e na

ture

of m

athe

mat

ics?

TOK

: Mat

hem

atic

s and

the

real

wor

ld. T

he

seem

ingl

y ab

strac

t con

cept

of c

alcu

lus a

llow

s us

to cr

eate

mat

hem

atic

al m

odel

s tha

t per

mit

hum

an

feat

s, su

ch as

get

ting

a m

an o

n th

e Moo

n. W

hat

does

this

tell

us ab

out t

he li

nks b

etw

een

mat

hem

atic

al m

odel

s and

phy

sical

real

ity?

6.3

Loca

l max

imum

and

min

imum

val

ues.

Opt

imiz

atio

n pr

oble

ms.

Poin

ts o

f inf

lexi

on w

ith z

ero

and

non-

zero

gr

adie

nts.

Gra

phic

al b

ehav

iour

of f

unct

ions

, inc

ludi

ng th

e re

latio

nshi

p be

twee

n th

e gr

aphs

of

f,

f′ a

nd f

′′ .

Not

req

uire

d:

Poin

ts o

f inf

lexi

on, w

here

(

)f

x′′

is n

ot

defin

ed, f

or e

xam

ple,

1

3y

x=

at

(0,0

).

Test

ing

for t

he m

axim

um o

r min

imum

usi

ng

the

chan

ge o

f sig

n of

the

first

der

ivat

ive

and

usin

g th

e si

gn o

f the

seco

nd d

eriv

ativ

e.

Use

of t

he te

rms “

conc

ave

up”

for

()

0f

x′′

>,

“con

cave

dow

n” fo

r (

)0

fx

′′<

.

At a

poi

nt o

f inf

lexi

on,

()

0f

x′′

= a

nd c

hang

es

sign

(con

cavi

ty c

hang

e).


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

6.4

Inde

finite

inte

grat

ion

as a

nti-d

iffer

entia

tion.

Inde

finite

inte

gral

of

n x, s

inx

, cos

x an

d ex

.

Oth

er in

defin

ite in

tegr

als u

sing

the

resu

lts fr

om

6.2.

The

com

posi

tes o

f any

of t

hese

with

a li

near

fu

nctio

n.

Inde

finite

inte

gral

inte

rpre

ted

as a

fam

ily o

f cu

rves

. 1

dln

xx

cx

=+

∫.

Exam

ples

incl

ude

()5

21

dx

x−

∫,

1d

34

xx+

∫

and

2

1d

25

xx

x+

+∫

.

6.5

Ant

i-diff

eren

tiatio

n w

ith a

bou

ndar

y co

nditi

on

to d

eter

min

e th

e co

nsta

nt o

f int

egra

tion.

Def

inite

inte

gral

s.

Are

a of

the

regi

on e

nclo

sed

by a

cur

ve a

nd th

e x-

axis

or y

-axi

s in

a gi

ven

inte

rval

; are

as o

f re

gion

s enc

lose

d by

cur

ves.

The

valu

e of

som

e de

finite

inte

gral

s can

onl

y be

foun

d us

ing

tech

nolo

gy.

Vol

umes

of r

evol

utio

n ab

out t

he x

-axi

s or y

-axi

s.

App

l: In

dust

rial d

esig

n.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

6.6

Kin

emat

ic p

robl

ems i

nvol

ving

dis

plac

emen

t s,

velo

city

v a

nd a

ccel

erat

ion

a.

Tota

l dis

tanc

e tra

velle

d.

d dsv

t=

, 2 2

dd

dd

dd

vs

va

vt

ts

==

=.

Tota

l dis

tanc

e tra

velle

d 2 1

dt t

vt

=∫

.

App

l: Ph

ysic

s HL

2.1

(kin

emat

ics)

.

Int:

Doe

s the

incl

usio

n of

kin

emat

ics a

s cor

e m

athe

mat

ics r

efle

ct a

par

ticul

ar c

ultu

ral

herit

age?

Who

dec

ides

wha

t is m

athe

mat

ics?

6.7

Inte

grat

ion

by su

bstit

utio

n O

n ex

amin

atio

n pa

pers

, non

-sta

ndar

d su

bstit

utio

ns w

ill b

e pr

ovid

ed.

Inte

grat

ion

by p

arts

. Li

nk to

6.2

.

Exam

ples

: si

nd

xx

x∫

and

ln

dxx

∫.

Rep

eate

d in

tegr

atio

n by

par

ts.

Exam

ples

: 2 e

dx

xx

∫ a

nd

esi

nd

xx

x∫

.


Syllabus content

Top

ic 7

—O

ptio

n: S

tatis

tics

and

prob

abili

ty

48 h

ours

Th

e ai

ms

of t

his

optio

n ar

e to

allo

w s

tude

nts

the

oppo

rtuni

ty t

o ap

proa

ch s

tatis

tics

in a

pra

ctic

al w

ay;

to d

emon

strat

e a

good

lev

el o

f st

atis

tical

un

ders

tand

ing;

and

to u

nder

stan

d w

hich

situ

atio

ns a

pply

and

to in

terp

ret t

he g

iven

resu

lts. I

t is e

xpec

ted

that

GD

Cs w

ill b

e us

ed th

roug

hout

this

opt

ion,

and

th

at th

e m

inim

um re

quire

men

t of a

GD

C w

ill b

e to

find

pro

babi

lity

dist

ribut

ion

func

tion

(pdf

), cu

mul

ativ

e di

strib

utio

n fu

nctio

n (c

df),

inve

rse

cum

ulat

ive

dist

ribut

ion

func

tion,

p-v

alue

s an

d te

st s

tatis

tics,

incl

udin

g ca

lcul

atio

ns f

or t

he f

ollo

win

g di

strib

utio

ns:

bino

mia

l, Po

isso

n, n

orm

al a

nd t

. St

uden

ts a

re

expe

cted

to se

t up

the

prob

lem

mat

hem

atic

ally

and

then

read

the

answ

ers

from

the

GD

C, i

ndic

atin

g th

is w

ithin

thei

r writ

ten

answ

ers.

Calc

ulat

or-s

peci

fic o

r br

and-

spec

ific

lang

uage

shou

ld n

ot b

e us

ed w

ithin

thes

e ex

plan

atio

ns.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

7.1

Cum

ulat

ive

dist

ribut

ion

func

tions

for b

oth

disc

rete

and

con

tinuo

us d

istri

butio

ns.

Geo

met

ric d

istri

butio

n.

Neg

ativ

e bi

nom

ial d

istri

butio

n.

Prob

abili

ty g

ener

atin

g fu

nctio

ns fo

r dis

cret

e ra

ndom

var

iabl

es.

()

E()

()

Xx

xG

tt

PX

xt

==

=∑

. In

t: A

lso

know

n as

Pas

cal’s

dis

tribu

tion.

Usi

ng p

roba

bilit

y ge

nera

ting

func

tions

to fi

nd

mea

n, v

aria

nce

and

the

distr

ibut

ion

of th

e su

m

of n

inde

pend

ent r

ando

m v

aria

bles

.

A

im 8

: Sta

tistic

al c

ompr

essi

on o

f dat

a fil

es.

7.2

Line

ar tr

ansf

orm

atio

n of

a sin

gle r

ando

m v

aria

ble.

Mea

n of

line

ar c

ombi

natio

ns o

f n ra

ndom

va

riabl

es.

Var

ianc

e of

line

ar c

ombi

natio

ns o

f n

inde

pend

ent r

ando

m v

aria

bles

.

E()

E()

aXb

aX

b+

=+

, 2

Var

()

Var

()

aXb

aX

+=

.

Expe

ctat

ion

of th

e pr

oduc

t of i

ndep

ende

nt

rand

om v

aria

bles

. E(

)E(

)E(

)XY

XY

=.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

7.3

Unb

iase

d es

timat

ors a

nd e

stim

ates

.

Com

paris

on o

f unb

iase

d es

timat

ors b

ased

on

varia

nces

.

T is

an

unbi

ased

est

imat

or fo

r the

par

amet

er

θ if

E(

)T

θ=

.

1T is

a m

ore

effic

ient

esti

mat

or th

an

2T if

12

Var

()

Var

()

TT

<.

TO

K: M

athe

mat

ics a

nd th

e w

orld

. In

the

abse

nce

of k

now

ing

the

valu

e of

a p

aram

eter

, w

ill a

n un

bias

ed e

stim

ator

alw

ays b

e be

tter

than

a b

iase

d on

e?

X a

s an

unbi

ased

est

imat

or fo

r µ

.

2 S a

s an

unbi

ased

est

imat

or fo

r 2

σ.

1ni

i

XX

n=

=∑

.

()2

2

11

ni

i

XX

Sn

=

−=

−∑

.

7.4

A li

near

com

bina

tion

of in

depe

nden

t nor

mal

ra

ndom

var

iabl

es is

nor

mal

ly d

istri

bute

d. In

pa

rticu

lar,

2~

N(

,)

Xµσ

⇒2

~N

,X

nσµ

.

The

cent

ral l

imit

theo

rem

.

A

im 8

/TO

K: M

athe

mat

ics a

nd th

e w

orld

. “W

ithou

t the

cen

tral l

imit

theo

rem

, the

re c

ould

be

no

stat

istic

s of a

ny v

alue

with

in th

e hu

man

sc

ienc

es.”

TOK

: Nat

ure

of m

athe

mat

ics.

The

cent

ral

limit

theo

rem

can

be

prov

ed m

athe

mat

ical

ly

(for

mal

ism

), bu

t its

trut

h ca

n be

con

firm

ed b

y its

app

licat

ions

(em

piric

ism

).


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

7.5

Con

fiden

ce in

terv

als f

or th

e m

ean

of a

nor

mal

po

pula

tion.

U

se o

f the

nor

mal

dis

tribu

tion

whe

n σ

is

know

n an

d us

e of

the

t-dis

tribu

tion

whe

n σ

is

unkn

own,

rega

rdle

ss o

f sam

ple

size

. The

cas

e of

mat

ched

pai

rs is

to b

e tre

ated

as a

n ex

ampl

e of

a si

ngle

sam

ple

tech

niqu

e.

TOK

: Mat

hem

atic

s and

the

wor

ld. C

laim

ing

bran

d A

is “

bette

r” o

n av

erag

e th

an b

rand

B

can

mea

n ve

ry li

ttle

if th

ere

is a

larg

e ov

erla

p be

twee

n th

e co

nfid

ence

inte

rval

s of t

he tw

o m

eans

.

App

l: G

eogr

aphy

.

7.6

Nul

l and

alte

rnat

ive

hypo

thes

es,

0H

and

1

H.

Sign

ifica

nce

leve

l.

Crit

ical

regi

ons,

criti

cal v

alue

s, p-

valu

es, o

ne-

taile

d an

d tw

o-ta

iled

test

s.

Type

I an

d II

erro

rs, i

nclu

ding

cal

cula

tions

of

thei

r pro

babi

litie

s.

Test

ing

hypo

thes

es fo

r the

mea

n of

a n

orm

al

popu

latio

n.

Use

of t

he n

orm

al d

istri

butio

n w

hen σ

is

know

n an

d us

e of

the

t-dis

tribu

tion

whe

n σ

is

unkn

own,

rega

rdle

ss o

f sam

ple

size

. The

cas

e of

mat

ched

pai

rs is

to b

e tre

ated

as a

n ex

ampl

e of

a si

ngle

sam

ple

tech

niqu

e.

TOK

: Mat

hem

atic

s and

the

wor

ld. I

n pr

actic

al

term

s, is

sayi

ng th

at a

resu

lt is

sign

ifica

nt th

e sa

me

as sa

ying

that

it is

true

?

TOK

: Mat

hem

atic

s and

the

wor

ld. D

oes t

he

abili

ty to

test

onl

y ce

rtain

par

amet

ers i

n a

popu

latio

n af

fect

the

way

kno

wle

dge

clai

ms i

n th

e hu

man

scie

nces

are

val

ued?

App

l: W

hen

is it

mor

e im

porta

nt n

ot to

mak

e a

Type

I er

ror a

nd w

hen

is it

mor

e im

porta

nt n

ot

to m

ake

a Ty

pe II

err

or?


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

7.7

Intro

duct

ion

to b

ivar

iate

dis

tribu

tions

. In

form

al d

iscu

ssio

n of

com

mon

ly o

ccur

ring

situ

atio

ns, e

g m

arks

in p

ure

mat

hem

atic

s and

st

atis

tics e

xam

s tak

en b

y a

clas

s of s

tude

nts,

sala

ry a

nd a

ge o

f tea

cher

s in

a ce

rtain

scho

ol.

The

need

for a

mea

sure

of a

ssoc

iatio

n be

twee

n th

e va

riabl

es a

nd th

e po

ssib

ility

of p

redi

ctin

g th

e va

lue

of o

ne o

f the

var

iabl

es g

iven

the

valu

e of

the

othe

r var

iabl

e.

App

l: G

eogr

aphi

c sk

ills.

Aim

8: T

he c

orre

latio

n be

twee

n sm

okin

g an

d lu

ng c

ance

r was

“di

scov

ered

” us

ing

mat

hem

atic

s. Sc

ienc

e ha

d to

just

ify th

e ca

use.

Cov

aria

nce

and

(pop

ulat

ion)

pro

duct

mom

ent

corr

elat

ion

coef

ficie

nt ρ

. C

ov(

,)

E[(

)

,

()]

E()

xy

xy

XY

XY

XYµ

µµ

µ

=−

−

=−

whe

re

E(),

E()

xy

XY

µµ

==

. C

ov(

,)

Var

()V

ar(

)X

YX

Yρ=

.

App

l: U

sing

tech

nolo

gy to

fit a

rang

e of

cur

ves

to a

set o

f dat

a.

Proo

f tha

t ρ =

0 in

the

case

of i

ndep

ende

nce

and ±1

in th

e ca

se o

f a li

near

rela

tions

hip

betw

een

X an

d Y.

The

use

of ρ

as a

mea

sure

of a

ssoc

iatio

n be

twee

n X

and

Y, w

ith v

alue

s nea

r 0 in

dica

ting

a w

eak

asso

ciat

ion

and

valu

es n

ear +

1 or

nea

r –1

indi

catin

g a

stro

ng a

ssoc

iatio

n.

TOK

: Mat

hem

atic

s and

the

wor

ld. G

iven

that

a se

t of d

ata

may

be a

ppro

xim

atel

y fit

ted

by a

ra

nge

of cu

rves

, whe

re w

ould

we s

eek

for

know

ledg

e of

whi

ch eq

uatio

n is

the “

true”

m

odel

?

Def

initi

on o

f the

(sam

ple)

pro

duct

mom

ent

corr

elat

ion

coef

ficie

nt R

in te

rms o

f n p

aire

d ob

serv

atio

ns o

n X

and

Y. It

s app

licat

ion

to th

e es

timat

ion

of ρ

.

1

22

11

1

22

22

1

()(

)

()

()

.

n

ii

in

n

ii

ii

n

ii

i

n

ii

i

XX

YY

RX

XY

Y

XY

nXY

XnX

YnY

=

==

=

=

−−

=

−−

−=

−

−

∑ ∑∑

∑

∑∑

Aim

8: T

he p

hysi

cist

Fra

nk O

ppen

heim

er

wro

te: “

Pred

ictio

n is

dep

ende

nt o

nly

on th

e as

sum

ptio

n th

at o

bser

ved

patte

rns w

ill b

e re

peat

ed.”

Thi

s is t

he d

ange

r of e

xtra

pola

tion.

Th

ere

are

man

y ex

ampl

es o

f its

failu

re in

the

past

, eg

shar

e pr

ices

, the

spre

ad o

f dis

ease

, cl

imat

e ch

ange

.

(con

tinue

d)


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

Info

rmal

inte

rpre

tatio

n of

r, th

e ob

serv

ed v

alue

of

R. S

catte

r dia

gram

s. V

alue

s of r

nea

r 0 in

dica

te a

wea

k as

soci

atio

n be

twee

nX

and

Y, a

nd v

alue

s nea

r 1

indi

cate

a

stro

ng a

ssoc

iatio

n.

(see

not

es a

bove

)

The

follo

win

g to

pics

are

bas

ed o

n th

e as

sum

ptio

n of

biv

aria

te n

orm

ality

. It

is e

xpec

ted

that

the

GD

C w

ill b

e us

ed

whe

reve

r pos

sibl

e in

the

follo

win

g w

ork.

Use

of t

he t-

stat

istic

to te

st th

e nu

ll hy

poth

esis

= 0.

22

1nR

R h

as th

e st

uden

t’s t-

dist

ribut

ion

with

(2)

n

deg

rees

of f

reed

om.

Kno

wle

dge

of th

e fa

cts t

hat t

he re

gres

sion

of X

onY

E(

|)

XY

y

and

Y o

n X

E(

|)

YX

x

are

linea

r.

Leas

t-squ

ares

est

imat

es o

f the

se re

gres

sion

lin

es (p

roof

not

requ

ired)

.

The

use

of th

ese

regr

essi

on li

nes t

o pr

edic

t the

va

lue

of o

ne o

f the

var

iabl

es g

iven

the

valu

e of

th

e ot

her.

1

2

1

1

22

1()(

) (

)(

)

(),

n

ii

in

ii

n

ii

in

ii

xx

yy

xx

yy

yy

xy

nxy

yy

yny

1

2

1

1

22

1()(

) (

)(

)

().

n

ii

in

ii

n

ii

in

ii

xx

yy

yy

xx

xx

xy

nxy

xx

xnx


Syllabus content

Top

ic 8

—O

ptio

n: S

ets,

rel

atio

ns a

nd g

roup

s 48

hou

rs

The

aim

s of

thi

s op

tion

are

to p

rovi

de t

he o

ppor

tuni

ty t

o st

udy

som

e im

porta

nt m

athe

mat

ical

con

cept

s, an

d in

trodu

ce t

he p

rinci

ples

of

proo

f th

roug

h ab

stra

ct a

lgeb

ra.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

8.1

Fini

te a

nd in

finite

sets

. Sub

sets

.

Ope

ratio

ns o

n se

ts: u

nion

; int

erse

ctio

n;

com

plem

ent;

set d

iffer

ence

; sym

met

ric

diff

eren

ce.

TO

K: C

anto

r the

ory

of tr

ansf

inite

num

bers

, R

usse

ll’s p

arad

ox, G

odel

’s in

com

plet

enes

s th

eore

ms.

De

Mor

gan’

s law

s: d

istri

butiv

e, a

ssoc

iativ

e an

d co

mm

utat

ive

law

s (fo

r uni

on a

nd in

ters

ectio

n).

Illus

tratio

n of

thes

e la

ws u

sing

Ven

n di

agra

ms.

Stud

ents

may

be a

sked

to p

rove

that

two

sets

are

the s

ame b

y es

tabl

ishin

g th

at A

B⊆

and

BA

⊆.

App

l: Lo

gic,

Boo

lean

alg

ebra

, com

pute

r ci

rcui

ts.

8.2

Ord

ered

pai

rs: t

he C

arte

sian

prod

uct o

f tw

o se

ts.

Rel

atio

ns: e

quiv

alen

ce re

latio

ns; e

quiv

alen

ce

clas

ses.

An

equi

vale

nce

rela

tion

on a

set f

orm

s a

parti

tion

of th

e se

t. A

ppl,

Int:

Sco

ttish

cla

ns.

8.3

Func

tions

: inj

ectio

ns; s

urje

ctio

ns; b

iject

ions

. Th

e te

rm c

odom

ain.

Com

posi

tion

of fu

nctio

ns a

nd in

vers

e fu

nctio

ns.

Kno

wle

dge

that

the

func

tion

com

posi

tion

is n

ot

a co

mm

utat

ive

oper

atio

n an

d th

at if

f is

a

bije

ctio

n fr

om se

t A to

set B

then

1

f− e

xist

s an

d is

a b

iject

ion

from

set B

to se

t A.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

8.4

Bin

ary

oper

atio

ns.

A b

inar

y op

erat

ion ∗

on a

non

-em

pty

set S

is a

ru

le fo

r com

bini

ng a

ny tw

o el

emen

ts

,ab

S∈

to

giv

e a

uniq

ue e

lem

ent c

. Tha

t is,

in th

is

defin

ition

, a b

inar

y op

erat

ion

on a

set i

s not

ne

cess

arily

clo

sed.

Ope

ratio

n ta

bles

(Cay

ley

tabl

es).

8.5

Bin

ary

oper

atio

ns: a

ssoc

iativ

e, d

istri

butiv

e an

d co

mm

utat

ive

prop

ertie

s. Th

e ar

ithm

etic

ope

ratio

ns o

n

and

.

Exam

ples

of d

istri

butiv

ity c

ould

incl

ude

the

fact

that

, on

, mul

tiplic

atio

n is

dis

tribu

tive

over

add

ition

but

add

ition

is n

ot d

istri

butiv

e ov

er m

ultip

licat

ion.

TO

K: W

hich

are

mor

e fu

ndam

enta

l, th

e ge

nera

l mod

els o

r the

fam

iliar

exa

mpl

es?

8.6

The

iden

tity

elem

ent e

.

The

inve

rse

1a−

of a

n el

emen

t a.

Proo

f tha

t lef

t-can

cella

tion

and

right

-ca

ncel

latio

n by

an

elem

ent a

hol

d, p

rovi

ded

that

a h

as a

n in

vers

e.

Proo

fs o

f the

uni

quen

ess o

f the

iden

tity

and

inve

rse

elem

ents

.

Bot

h th

e rig

ht-id

entit

y a

ea

∗=

and

left-

iden

tity

ea

a∗

= m

ust h

old

if e

is a

n id

entit

y el

emen

t.

Bot

h 1

aa

e−

∗=

and

1

aa

e−∗

= m

ust h

old.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

8.7

The

defin

ition

of a

gro

up {

,}

G∗

.

The

oper

atio

n ta

ble

of a

gro

up is

a L

atin

sq

uare

, but

the

conv

erse

is fa

lse.

For t

he se

t G u

nder

a g

iven

ope

ratio

n ∗:

• G

is c

lose

d un

der ∗;

• ∗

is a

ssoc

iativ

e;

• G

con

tain

s an

iden

tity

elem

ent;

• ea

ch e

lem

ent i

n G

has

an

inve

rse

in G

.

App

l: Ex

iste

nce

of fo

rmul

a fo

r roo

ts o

f po

lyno

mia

ls.

App

l: G

aloi

s the

ory

for t

he im

poss

ibili

ty o

f su

ch fo

rmul

ae fo

r pol

ynom

ials

of d

egre

e 5

or

high

er.

Abe

lian

grou

ps.

ab

ba

∗=

∗, f

or a

ll ,ab

G∈

.

8.8

Exam

ples

of g

roup

s:

•

, ,

and

u

nder

add

ition

;

• in

tege

rs u

nder

add

ition

mod

ulo

n;

• no

n-ze

ro in

tege

rs u

nder

mul

tiplic

atio

n,

mod

ulo

p, w

here

p is

prim

e;

A

ppl:

Rub

ik’s

cub

e, ti

me

mea

sure

s, cr

ysta

l st

ruct

ure,

sym

met

ries o

f mol

ecul

es, s

trut a

nd

cabl

e co

nstru

ctio

ns, P

hysi

cs H

2.2

(spe

cial

re

lativ

ity),

the

8–fo

ld w

ay, s

uper

sym

met

ry.

sym

met

ries o

f pla

ne fi

gure

s, in

clud

ing

equi

late

ral t

riang

les a

nd re

ctan

gles

;

inve

rtibl

e fu

nctio

ns u

nder

com

posi

tion

of

func

tions

.

The

com

posi

tion

21

TT

den

otes

1T fo

llow

ed

by2T.

8.9

The

orde

r of a

gro

up.

The

orde

r of a

gro

up e

lem

ent.

Cyc

lic g

roup

s.

Gen

erat

ors.

Proo

f tha

t all

cycl

ic g

roup

s are

Abe

lian.

A

ppl:

Mus

ic c

ircle

of f

ifths

, prim

e nu

mbe

rs.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

8.10

Pe

rmut

atio

ns u

nder

com

posi

tion

of

perm

utat

ions

.

Cyc

le n

otat

ion

for p

erm

utat

ions

.

Res

ult t

hat e

very

per

mut

atio

n ca

n be

writ

ten

as

a co

mpo

sitio

n of

dis

join

t cyc

les.

The

orde

r of a

com

bina

tion

of c

ycle

s.

On

exam

inat

ion

pape

rs: t

he fo

rm

12

33

12

p

=

or i

n cy

cle

nota

tion

(132

) will

be u

sed

to re

pres

ent t

he p

erm

utat

ion

13

→,

21

→, 3

2.→

App

l: C

rypt

ogra

phy,

cam

pano

logy

.

8.11

Su

bgro

ups,

prop

er su

bgro

ups.

A p

rope

r sub

grou

p is

nei

ther

the

grou

p its

elf

nor t

he su

bgro

up c

onta

inin

g on

ly th

e id

entit

y el

emen

t.

Use

and

pro

of o

f sub

grou

p te

sts.

Supp

ose

that

{,

}G∗

is a

gro

up a

nd H

is a

no

n-em

pty

subs

et o

f G. T

hen

{,

}H

∗ is

a

subg

roup

of {

,}

G∗

if

1a

bH

−∗

∈ w

hene

ver

,ab

H∈

.

Supp

ose

that

{,

}G∗

is a

fini

te g

roup

and

H is

a

non-

empt

y su

bset

of G

. The

n {

,}

H∗

is a

su

bgro

up o

f {,

}G∗

if H

is c

lose

d un

der ∗ .

Def

initi

on a

nd e

xam

ples

of l

eft a

nd ri

ght c

oset

s of

a su

bgro

up o

f a g

roup

.

Lagr

ange

’s th

eore

m.

Use

and

pro

of o

f the

resu

lt th

at th

e or

der o

f a

finite

gro

up is

div

isib

le b

y th

e or

der o

f any

el

emen

t. (C

orol

lary

to L

agra

nge’

s the

orem

.)

A

ppl:

Prim

e fa

ctor

izat

ion,

sym

met

ry b

reak

ing.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

8.12

Def

initi

on o

f a g

roup

hom

omor

phis

m.

Infin

ite g

roup

s as w

ell a

s fin

ite g

roup

s.

Let {

,*}

Gan

d {

,}H

be g

roup

s, th

en th

e fu

nctio

n:fG

H→

is a

hom

omor

phism

if(

*)

()

()

fa

bf

af

b=

fo

r all

,ab

G∈

.

Def

initi

on o

f the

ker

nel o

f a h

omom

orph

ism

.Pr

oof t

hat t

he k

erne

l and

rang

e of

a

hom

omor

phis

m a

re su

bgro

ups.

If :fG

H→

is a

gro

up h

omom

orph

ism

, the

n K

er(

)fis

the

set o

f a

G∈

such

that

()

Hf

ae

=.

Proo

f of h

omom

orph

ism

pro

perti

es fo

r id

entit

ies a

nd in

vers

es.

Iden

tity:

let

Gean

d He

be th

e id

entit

y el

emen

ts of

{,*

}G

and

{,}

H

,res

pect

ivel

y, th

en(

)G

Hf

ee

=.

Inve

rse:

(

)11

()

()

fa

fa

−−

=fo

r all

aG

∈.

Isom

orph

ism

of g

roup

s.In

finite

gro

ups a

s wel

l as f

inite

gro

ups.

The

hom

omor

phis

m

:fG

H→

is a

n is

omor

phism

if f

is b

iject

ive.

The

orde

r of a

n el

emen

t is u

ncha

nged

by

an

isom

orph

ism.


Syllabus content

Topic 9—

Option: C

alculus

48 hours

The

aim

s of t

his o

ptio

n ar

e to

intro

duce

lim

it th

eore

ms a

nd c

onve

rgen

ce o

f ser

ies,

and

to u

se c

alcu

lus r

esul

ts to

solv

e di

ffer

entia

l equ

atio

ns.

9.1

Infin

ite se

quen

ces o

f rea

l num

bers

and

thei

r co

nver

genc

e or

div

erge

nce.

In

form

al tr

eatm

ent o

f lim

it of

sum

, diff

eren

ce,

prod

uct,

quot

ient

; squ

eeze

theo

rem

.

Div

erge

nt is

take

n to

mea

n no

t con

verg

ent.

TO

K: Z

eno’

s par

adox

, im

pact

of i

nfin

ite

sequ

ence

s and

lim

its o

n ou

r und

erst

andi

ng o

f th

e ph

ysic

al w

orld

.

9.2

Con

verg

ence

of i

nfin

ite se

ries.

Test

s for

con

verg

ence

: com

paris

on te

st; l

imit

com

paris

on te

st; r

atio

test

; int

egra

l tes

t.

The

sum

of a

serie

s is t

he li

mit

of th

e se

quen

ce

of it

s par

tial s

ums.

Stud

ents

shou

ld b

e aw

are

that

if li

mn

nx

=0 th

en

the

serie

s is n

ot n

eces

saril

y co

nver

gent

, but

if

limn

nx

0,

the

serie

s div

erge

s.

TO

K: E

uler

’s id

ea th

at

1 21

11

1

. W

as it

a m

ista

ke o

r jus

t an

alte

rnat

ive

view

?

The

p-se

ries,

1 p n

. 1 p n

is

conv

erge

nt fo

r 1

p

and

dive

rgen

t

othe

rwise

. Whe

n1

p

, thi

s is t

he h

arm

onic

serie

s.

Serie

s tha

t con

verg

e ab

solu

tely

.

Serie

s tha

t con

verg

e co

nditi

onal

ly.

Con

ditio

ns fo

r con

verg

ence

.

Alte

rnat

ing

serie

s.

Pow

er se

ries:

radi

us o

f con

verg

ence

and

in

terv

al o

f con

verg

ence

. Det

erm

inat

ion

of th

e ra

dius

of c

onve

rgen

ce b

y th

e ra

tio te

st.

The

abso

lute

val

ue o

f the

trun

catio

n er

ror i

s le

ss th

an th

e ne

xt te

rm in

the

serie

s.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

9.3

Con

tinui

ty a

nd d

iffer

entia

bilit

y of

a fu

nctio

n at

a

poin

t. Te

st fo

r con

tinui

ty:

()

()

()

limlim

xa–

xa+

fx

=f

a=

fx

→→

.

Con

tinuo

us fu

nctio

ns a

nd d

iffer

entia

ble

func

tions

. Te

st fo

r diff

eren

tiabi

lity:

f is c

ontin

uous

at a

and

()

0

()

lim h

fa

h–

fa

h→

−

+ a

nd

()

0

()

lim h+

fa

h–

fa

h→

+ e

xist

and

are

equ

al.

Stud

ents

shou

ld b

e aw

are

that

a fu

nctio

n m

ay

be c

ontin

uous

but

not

diff

eren

tiabl

e at

a p

oint

, eg

()

fx

=x

and

sim

ple

piec

ewis

e fu

nctio

ns.

9.4

The

inte

gral

as a

lim

it of

a su

m; l

ower

and

up

per R

iem

ann

sum

s.

Int:

How

clo

se w

as A

rchi

med

es to

inte

gral

ca

lcul

us?

Int:

Con

tribu

tion

of A

rab,

Chi

nese

and

Indi

an

mat

hem

atic

ians

to th

e de

velo

pmen

t of c

alcu

lus.

Aim

8: L

eibn

iz v

ersu

s New

ton

vers

us th

e “g

iant

s” o

n w

hose

shou

lder

s the

y st

ood—

who

de

serv

es c

redi

t for

mat

hem

atic

al p

rogr

ess?

TOK

: Con

side

r

1f

x=

x,

∞≤

≤x

1.

An

infin

ite a

rea

swee

ps o

ut a

fini

te v

olum

e. C

an

this

be re

conc

iled

with

our

intu

ition

? W

hat d

oes

this

tell

us a

bout

mat

hem

atic

al k

now

ledg

e?

Fund

amen

tal t

heor

em o

f cal

culu

s. d

()d

()

d

x a

fy

y=

fx

x

∫

.

Impr

oper

inte

gral

s of t

he ty

pe

()d

a

fx

x∞ ∫

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

9.5

Firs

t-ord

er d

iffer

entia

l equ

atio

ns.

Geo

met

ric in

terp

reta

tion

usin

g sl

ope

field

s, in

clud

ing

iden

tific

atio

n of

isoc

lines

.

A

ppl:

Rea

l-life

diff

eren

tial e

quat

ions

, eg

New

ton’

s law

of c

oolin

g,

popu

latio

n gr

owth

,

carb

on d

atin

g.

Num

eric

al so

lutio

n of

d(

,)

dy=

fx

yx

usi

ng

Eule

r’s m

etho

d.

Var

iabl

es se

para

ble.

Hom

ogen

eous

diff

eren

tial e

quat

ion

d dyy

=f

xx

usin

g th

e su

bstit

utio

n y

= vx

.

Solu

tion

of y′ +

P(x

)y =

Q(x

), us

ing

the

inte

grat

ing

fact

or.

1(

,)

nn

nn

yy

hfx

y+=

+,

1n

nx

xh

+=

+, w

here

h

is a

con

stan

t.

9.6

Rol

le’s

theo

rem

. M

ean

valu

e th

eore

m.

In

t, TO

K: I

nflu

ence

of B

ourb

aki o

n un

ders

tand

ing

and

teac

hing

of m

athe

mat

ics.

Int:

Com

pare

with

wor

k of

the

Ker

ala

scho

ol.

Tayl

or p

olyn

omia

ls; t

he L

agra

nge

form

of t

he

erro

r ter

m.

App

licat

ions

to th

e app

roxi

mat

ion

of fu

nctio

ns;

form

ula f

or th

e erro

r ter

m, i

n te

rms o

f the

val

ue

of th

e (n

+ 1)

th d

eriv

ativ

e at

an

inte

rmed

iate

poi

nt.

Mac

laur

in se

ries f

or e

x, s

inx

, cos

x,

ln(1

)x+

, (1

)px

+,

p∈

. U

se o

f sub

stitu

tion,

pro

duct

s, in

tegr

atio

n an

d di

ffer

entia

tion

to o

btai

n ot

her s

erie

s. Ta

ylor

serie

s dev

elop

ed fr

om d

iffer

entia

l eq

uatio

ns.

Stud

ents

shou

ld b

e aw

are

of th

e in

terv

als o

f co

nver

genc

e.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

9.7

The

eval

uatio

n of

lim

its o

f the

form

()

()

lim xa

fx

gx

→ a

nd

()

()

lim x

fx

gx

→∞

. Th

e in

dete

rmin

ate

form

s 0 0

and

∞ ∞.

Usi

ng l’

Hôp

ital’s

rule

or t

he T

aylo

r ser

ies.

Rep

eate

d us

e of

l’H

ôpita

l’s ru

le.


Syllabus content

Top

ic 1

0—O

ptio

n: D

iscr

ete

mat

hem

atic

s 48

hou

rs

The

aim

of t

his o

ptio

n is

to p

rovi

de th

e op

portu

nity

for s

tude

nts t

o en

gage

in lo

gica

l rea

soni

ng, a

lgor

ithm

ic th

inki

ng a

nd a

pplic

atio

ns.

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

10.1

St

rong

indu

ctio

n.

Pige

on-h

ole

prin

cipl

e.

For e

xam

ple,

pro

ofs o

f the

fund

amen

tal

theo

rem

of a

rithm

etic

and

the

fact

that

a tr

ee

with

n v

ertic

es h

as n

– 1

edg

es.

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

The

diff

eren

ce b

etw

een

proo

f and

con

ject

ure,

eg

Gol

dbac

h’s c

onje

ctur

e. C

an a

mat

hem

atic

al

stat

emen

t be

true

befo

re it

is p

rove

n?

TOK

: Pro

of b

y co

ntra

dict

ion.

10.2

|

ab

bna

⇒=

for s

ome

n∈

.

The

theo

rem

|

ab

and

|

|()

ac

abx

cy⇒

±

whe

re

,xy∈

. Th

e di

visi

on a

lgor

ithm

abq

r=

+,

0r

b≤

<.

Div

isio

n an

d Eu

clid

ean

algo

rithm

s.

The

grea

test

com

mon

div

isor

, gcd

(,

)a

b, a

nd

the

leas

t com

mon

mul

tiple

, lcm

(,

)a

b, o

f in

tege

rs a

and

b.

Prim

e nu

mbe

rs; r

elat

ivel

y pr

ime

num

bers

and

th

e fu

ndam

enta

l the

orem

of a

rithm

etic

.

The

Eucl

idea

n al

gorit

hm fo

r det

erm

inin

g th

e gr

eate

st c

omm

on d

ivis

or o

f tw

o in

tege

rs.

Int:

Euc

lidea

n al

gorit

hm c

onta

ined

in E

uclid

’s

Elem

ents

, writ

ten

in A

lexa

ndria

abo

ut

300

BC

E.

Aim

8: U

se o

f prim

e nu

mbe

rs in

cry

ptog

raph

y.

The

poss

ible

impa

ct o

f the

dis

cove

ry o

f po

wer

ful f

acto

rizat

ion

tech

niqu

es o

n in

tern

et

and

bank

secu

rity.

10.3

Li

near

Dio

phan

tine

equa

tions

ax

byc

+=

. G

ener

al so

lutio

ns re

quire

d an

d so

lutio

ns

subj

ect t

o co

nstra

ints

. For

exa

mpl

e, a

ll so

lutio

ns m

ust b

e po

sitiv

e.

Int:

Des

crib

ed in

Dio

phan

tus’

Ari

thm

etic

a w

ritte

n in

Ale

xand

ria in

the

3rd c

entu

ry C

E.

Whe

n st

udyi

ng A

rith

met

ica,

a F

renc

h m

athe

mat

icia

n, P

ierr

e de

Fer

mat

(160

1–16

65)

wro

te in

the

mar

gin

that

he

had

disc

over

ed a

si

mpl

e pr

oof r

egar

ding

hig

her-o

rder

D

ioph

antin

e eq

uatio

ns—

Ferm

at’s

last

theo

rem

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

10.4

M

odul

ar a

rithm

etic

.

The

solu

tion

of li

near

con

grue

nces

.

Solu

tion

of si

mul

tane

ous l

inea

r con

grue

nces

(C

hine

se re

mai

nder

theo

rem

).

Int:

Dis

cuss

ed b

y C

hine

se m

athe

mat

icia

n Su

n Tz

u in

the

3rd c

entu

ry C

E.

10.5

R

epre

sent

atio

n of

inte

gers

in d

iffer

ent b

ases

. O

n ex

amin

atio

n pa

pers

, que

stio

ns th

at g

o be

yond

bas

e 16

will

not

be

set.

Int:

Baby

loni

ans d

evel

oped

a ba

se 6

0 nu

mbe

r sy

stem

and

the M

ayan

s a b

ase 2

0 nu

mbe

r sys

tem

.

10.6

Fe

rmat

’s li

ttle

theo

rem

. (m

od)

p aa

p=

, whe

re p

is p

rime.

TO

K: N

atur

e of

mat

hem

atic

s. A

n in

tere

st m

ay

be p

ursu

ed fo

r cen

turie

s bef

ore

beco

min

g “u

sefu

l”.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

10.7

G

raph

s, ve

rtice

s, ed

ges,

face

s. A

djac

ent

verti

ces,

adja

cent

edg

es.

Deg

ree

of a

ver

tex,

deg

ree

sequ

ence

.

Han

dsha

king

lem

ma.

Two

verti

ces a

re a

djac

ent i

f the

y ar

e jo

ined

by

an e

dge.

Tw

o ed

ges a

re a

djac

ent i

f the

y ha

ve a

co

mm

on v

erte

x.

Aim

8: S

ymbo

lic m

aps,

eg M

etro

and

U

nder

grou

nd m

aps,

stru

ctur

al fo

rmul

ae in

ch

emis

try, e

lect

rical

circ

uits

.

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

Proo

f of t

he fo

ur-c

olou

r the

orem

. If a

theo

rem

is

pro

ved

by c

ompu

ter,

how

can

we

clai

m to

kn

ow th

at it

is tr

ue?

Sim

ple

grap

hs; c

onne

cted

gra

phs;

com

plet

e gr

aphs

; bip

artit

e gra

phs;

plan

ar g

raph

s; tre

es;

wei

ghte

d gr

aphs

, inc

ludi

ng ta

bula

r re

pres

enta

tion.

Subg

raph

s; c

ompl

emen

ts o

f gra

phs.

It sh

ould

be

stre

ssed

that

a g

raph

shou

ld n

ot b

e as

sum

ed to

be

sim

ple

unle

ss sp

ecifi

cally

stat

ed.

The

term

adj

acen

cy ta

ble

may

be

used

.

Aim

8: I

mpo

rtanc

e of

pla

nar g

raph

s in

cons

truct

ing

circ

uit b

oard

s.

Eule

r’s r

elat

ion:

2

ve

f−

+=

; the

orem

s for

pl

anar

gra

phs i

nclu

ding

3

6e

v≤

−,

24

ev

≤−

, le

adin

g to

the

resu

lts th

at

5κ a

nd

3,3

κ a

re n

ot

plan

ar.

If th

e gr

aph

is si

mpl

e an

d pl

anar

and

3

v≥

, th

en

36

ev

≤−

.

If th

e gr

aph

is si

mpl

e, p

lana

r, ha

s no

cycl

es o

f le

ngth

3 a

nd

3v≥

, the

n2

4e

v≤

−.

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

App

licat

ions

of t

he E

uler

cha

ract

eris

tic

()

ve

f−

+ to

hig

her d

imen

sion

s. Its

use

in

unde

rsta

ndin

g pr

oper

ties o

f sha

pes t

hat c

anno

t be

vis

ualiz

ed.

10.8

W

alks

, tra

ils, p

aths

, circ

uits,

cyc

les.

Eule

rian

trails

and

circ

uits

. A

con

nect

ed g

raph

con

tain

s an

Eule

rian

circ

uit

if an

d on

ly if

eve

ry v

erte

x of

the

grap

h is

of

even

deg

ree.

Int:

The

“Br

idge

s of K

önig

sber

g” p

robl

em.

Ham

ilton

ian

path

s and

cyc

les.

Sim

ple

treat

men

t onl

y.

10.9

G

raph

alg

orith

ms:

Kru

skal

’s; D

ijkst

ra’s

.


Syllabus content

Co

nten

t Fu

rthe

r gui

danc

e Li

nks

10.1

0 C

hine

se p

ostm

an p

robl

em.

Not

req

uire

d:

Gra

phs w

ith m

ore

than

four

ver

tices

of o

dd

degr

ee.

To d

eter

min

e th

e sh

orte

st ro

ute

arou

nd a

w

eigh

ted

grap

h go

ing

alon

g ea

ch e

dge

at le

ast

once

.

Int:

Pro

blem

pos

ed b

y th

e C

hine

se

mat

hem

atic

ian

Kw

an M

ei-K

o in

196

2.

Trav

ellin

g sa

lesm

an p

robl

em.

Nea

rest

-nei

ghbo

ur a

lgor

ithm

for d

eter

min

ing

an u

pper

bou

nd.

Del

eted

ver

tex

algo

rithm

for d

eter

min

ing

a lo

wer

bou

nd.

To d

eter

min

e th

e H

amilt

onia

n cy

cle

of le

ast

wei

ght i

n a

wei

ghte

d co

mpl

ete

grap

h.

TOK

: Mat

hem

atic

s and

kno

wle

dge

clai

ms.

How

long

wou

ld it

take

a c

ompu

ter t

o te

st a

ll H

amilt

onia

n cy

cles

in a

com

plet

e, w

eigh

ted

grap

h w

ith ju

st 3

0 ve

rtice

s?

10.1

1 R

ecur

renc

e re

latio

ns. I

nitia

l con

ditio

ns,

recu

rsiv

e de

finiti

on o

f a se

quen

ce.

TO

K: M

athe

mat

ics a

nd th

e w

orld

. The

co

nnec

tions

of s

eque

nces

such

as t

he F

ibon

acci

se

quen

ce w

ith a

rt an

d bi

olog

y.

Solu

tion

of fi

rst-

and

seco

nd-d

egre

e lin

ear

hom

ogen

eous

recu

rren

ce re

latio

ns w

ith

cons

tant

coe

ffic

ient

s.

The

first

-deg

ree

linea

r rec

urre

nce

rela

tion

1n

nu

aub

−=

+.

Incl

udes

the

case

s whe

re a

uxili

ary

equa

tion

has

equa

l roo

ts o

r com

plex

root

s.

Mod

ellin

g w

ith re

curr

ence

rela

tions

. So

lvin

g pr

oble

ms s

uch

as c

ompo

und

inte

rest

, de

bt re

paym

ent a

nd c

ount

ing

prob

lem

s.


Syllabus

Glossary of terminology: Discrete mathematics

IntroductionTeachers and students should be aware that many different terminologies exist in graph theory, and that different textbooks may employ different combinations of these. Examples of these are: vertex/node/junction/point; edge/route/arc; degree/order of a vertex; multiple edges/parallel edges; loop/self-loop.

In IB examination questions, the terminology used will be as it appears in the syllabus. For clarity, these terms are defined below.

TerminologyBipartite graph A graph whose vertices can be divided into two sets such that no two vertices in the

same set are adjacent.

Circuit A walk that begins and ends at the same vertex, and has no repeated edges.

Complement of a graph G

A graph with the same vertices as G but which has an edge between any two vertices if and only if G does not.

Complete bipartite graph

A bipartite graph in which every vertex in one set is joined to every vertex in the other set.

Complete graph A simple graph in which each pair of vertices is joined by an edge.

Connected graph A graph in which each pair of vertices is joined by a path.

Cycle A walk that begins and ends at the same vertex, and has no other repeated vertices.

Degree of a vertex The number of edges joined to the vertex; a loop contributes two edges, one for each of its end points.

Disconnected graph A graph that has at least one pair of vertices not joined by a path.

Eulerian circuit A circuit that contains every edge of a graph.

Eulerian trail A trail that contains every edge of a graph.

Graph Consists of a set of vertices and a set of edges.

Graph isomorphism between two simple graphs G and H

A one-to-one correspondence between vertices of G and H such that a pair of vertices in G is adjacent if and only if the corresponding pair in H is adjacent.

Hamiltonian cycle A cycle that contains all the vertices of the graph.

Hamiltonian path A path that contains all the vertices of the graph.

Loop An edge joining a vertex to itself.


Glossary of terminology: Discrete mathematics

Minimum spanning tree

A spanning tree of a weighted graph that has the minimum total weight.

Multiple edges Occur if more than one edge joins the same pair of vertices.

Path A walk with no repeated vertices.

Planar graph A graph that can be drawn in the plane without any edge crossing another.

Simple graph A graph without loops or multiple edges.

Spanning tree of a graph

A subgraph that is a tree, containing every vertex of the graph.

Subgraph A graph within a graph.

Trail A walk in which no edge appears more than once.

Tree A connected graph that contains no cycles.

Walk A sequence of linked edges.

Weighted graph A graph in which each edge is allocated a number or weight.

Weighted tree A tree in which each edge is allocated a number or weight.


Assessment in the Diploma Programme

Assessment

GeneralAssessment is an integral part of teaching and learning. The most important aims of assessment in the Diploma Programme are that it should support curricular goals and encourage appropriate student learning. Both external and internal assessment are used in the Diploma Programme. IB examiners mark work produced for external assessment, while work produced for internal assessment is marked by teachers and externally moderated by the IB.

There are two types of assessment identified by the IB.

• Formative assessment informs both teaching and learning. It is concerned with providing accurate and helpful feedback to students and teachers on the kind of learning taking place and the nature of students’ strengths and weaknesses in order to help develop students’ understanding and capabilities. Formative assessment can also help to improve teaching quality, as it can provide information to monitor progress towards meeting the course aims and objectives.

• Summative assessment gives an overview of previous learning and is concerned with measuring student achievement.

The Diploma Programme primarily focuses on summative assessment designed to record student achievement at or towards the end of the course of study. However, many of the assessment instruments can also be used formatively during the course of teaching and learning, and teachers are encouraged to do this. A comprehensive assessment plan is viewed as being integral with teaching, learning and course organization. For further information, see the IB Programme standards and practices document.

The approach to assessment used by the IB is criterion-related, not norm-referenced. This approach to assessment judges students’ work by their performance in relation to identified levels of attainment, and not in relation to the work of other students. For further information on assessment within the Diploma Programme, please refer to the publication Diploma Programme assessment: Principles and practice.

To support teachers in the planning, delivery and assessment of the Diploma Programme courses, a variety of resources can be found on the OCC or purchased from the IB store (http://store.ibo.org). Teacher support materials, subject reports, internal assessment guidance, grade descriptors, as well as resources from other teachers, can be found on the OCC. Specimen and past examination papers as well as markschemes can be purchased from the IB store.


Assessment in the Diploma Programme

Methods of assessmentThe IB uses several methods to assess work produced by students.

Assessment criteriaAssessment criteria are used when the assessment task is open-ended. Each criterion concentrates on a particular skill that students are expected to demonstrate. An assessment objective describes what students should be able to do, and assessment criteria describe how well they should be able to do it. Using assessment criteria allows discrimination between different answers and encourages a variety of responses. Each criterion comprises a set of hierarchically ordered level descriptors. Each level descriptor is worth one or more marks. Each criterion is applied independently using a best-fit model. The maximum marks for each criterion may differ according to the criterion’s importance. The marks awarded for each criterion are added together to give the total mark for the piece of work.

MarkbandsMarkbands are a comprehensive statement of expected performance against which responses are judged. They represent a single holistic criterion divided into level descriptors. Each level descriptor corresponds to a range of marks to differentiate student performance. A best-fit approach is used to ascertain which particular mark to use from the possible range for each level descriptor.

MarkschemesThis generic term is used to describe analytic markschemes that are prepared for specific examination papers. Analytic markschemes are prepared for those examination questions that expect a particular kind of response and/or a given final answer from the students. They give detailed instructions to examiners on how to break down the total mark for each question for different parts of the response. A markscheme may include the content expected in the responses to questions or may be a series of marking notes giving guidance on how to apply criteria.

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