business modelling ii

8/11/2019 Business Modelling II

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BSc(HONS) COMPUTER SCIENCE

T%eor612. Transfera9le ills ills and

%o0 t%e6 are de:elo# andassessed= "ro&ect and #racticale#erience and Interns%i#

13. Teac%ing;learning and

assessment strateg614. 6no#sis T%is unit co:ers tec%ni5ues of

In:estment !##raisal= +inear @Non;linear Cross; sectionalModels= Time;Based Models and+inear "rogramming. B6 e#osureto a num9er of modelingtec%ni5ues= students 0ill gain anunderstanding and a##reciationto t%e use of models. "racticale#erience 0ill 9e gained in

#ro9lem formulation and sol:ing=and students 0ill 9e introduced torele:ant com#uter soft0are= in#articular t%e Microsoft Dcel#reads%eet.

1'. Mode of Eeli:er6 +ecture=Tutorial= Fors%o#= eminar=etc..

+ecture @ Tutorial

1*. !ssessment Met%ods andT6#es

!ssignment I 7 28G!ssignment II 7 28G,inal eamination 7 *8G

1. Ma##ing of t%e course/ moduleto t%e "rogramme !ims

1. Ma##ing of t%e course/ moduleto t%e "rogramme +earningutcome

1. Content outline of t%ecourse/module and t%e +T #erto#ic

28. Main reference su##ortingt%e course

-uide to Business Modellingo%n T @ -ra%am ,2ndedition= 288'"rentice Hall "u9lications

!dditional referencesu##orting t%e course

Mat%s for Com#uting,ran - @ $o9ert +288'!ddison Fesle6 "u9lications

22. t%er additional information

BINARY UNIVERSITY COLLEGE

SCHOOL OF TECHNOLOGY MANAGEMENT (2010)


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WEEK LESSON PLAN HOURS

WEEK 1&

WEEK 2

Investment Appraisal

Net Present Value

Internal Rate of Return

Payback Method

Averae Rate of Return!

" #ours

WEEK $

&WEEK %

Simple Linear Regression

he '()*le l(near Reress(on Model

Model Assu)*t(ons

Method of least '+uares

Est()at(on of Interce*t and 'lo*e ,oeff(c(ents and

Inter*retat(on

'tandard Error

est of '(n(f(cance of ,oeff(c(ents

,oeff(c(ent of -eter)(nat(on

" #ours

WEEK .&

WEEK "

Multiple Regression

he Mult(*le Reress(on Model

Model Assu)*t(ons

Model /(th 2 E0*lanatory Var(ables

Est()at(on of ,oeff(c(ents

ests of '(n(f(cance

Proble)s of Mult(coll(near(ty #eteroscedast(c(ty and

Autocorrelat(on

" #ours

WEEK

&WEEK 13

Linear Programming

-ef(n(t(on & ,ond(t(ons Re+u(red for a 4(near

Prora))(n Model

5or)ulat(on of the Proble)

6ra*h(cal A**roach for Proble)s /(th 2 -ec(s(on

Var(ables

he '()*le0 Method

Ma0()(sat(on and M(n()(sat(on of 7b8ect(ve

5unct(ons 9s(n '()*le0 Method

12 #ours

WEEK 11 Maths for omputing

A**ly ,o)*uter Ar(th)et(c

,onvers(ons bet/een nu)ber syste)s

:(nary ar(th)et(c uses

,o)*uter re*resentat(on of nu)bers

,o)*utat(onal error (llustrated

$ #ours




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WEEK 12 Maths for omputing!""#ont$

A**ly :oolean 4o(c & Karnauh Ma*s

:oolean var(ables and o*erators handl(n

ruth tables

Karnauh )a*s used to s()*l(fy :oolean e0*ress(ons

$ #ours

WEEK 1$

&WEEK 1%

Maths for omputing!""#ont$

A**ly ,once*ts of 9nd(rected 6ra*h heory

er)s def(ned

Iso)or*h(s) e0*la(ned

6ra*hs *roble)s solved

rees def(ned

" #ours

%otal &' Hours



business modelling ii

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