PAPER NO. 2 COMPUTER MATHEMATICS
GENERAL OBJECTIVE
This paper is intended to equip the candidate with the knowledge, skills and attitude that will enable him/her to apply computer mathematical approaches to solve business problems.
LEARNING OUTCOMES
A candidate who passes this paper should be able to:
- Perform binary arithmetic operations
- Draw simple deductions and conclusions from given data
- Use matrix algebra to solve real life problems
- Solve basic linear equations
- Relate probability and statistics to computing
- Apply set theory in solving computing problems
- Solve computer related problems using logic and truth table
CONTENT
Data representation and number systems
- Computer codes: BCD, ASCII, EBCDIC
- Bit, byte, nibble, word
- Number systems; Decimal numbers, Binary numbers, Octal numbers, Hexadecimal numbers
- Number conversions
Binary arithmetic
- Addition, subtraction
- Multiplication, division
- Complements
Set theory
- Introduction; definitions and purpose
- Types of sets: Universal set, empty/null set, sub-sets, finite, infinite, power sets, partition
- Description of sets; enumeration method and descriptive method
- Operations: Union and intersection, complements, difference
- Duality
- Sets and elements
- Venn diagrams
- Ordered pairs, product sets, relations
Logic and truth tables
- Introduction
- Conjunction and disjunction
- Negation
- Proportions and truth tables
- Tautology and contradiction
- Logical equivalence
Elementary matrices
- Introduction to matrices: definitions and importance of matrices
- Matrix addition and subtraction
- Dimensions/order of matrices
- Types of matrices
- Identity matrix
- Matrix operations: addition, subtraction, multiplication, inversion of 2×2 matrices
- Applications of matrices to business problems
Linear equations
- Linear equations in one unknown
- System of two linear equations in two unknowns
Elementary statistics
- Sources of data: primary and secondary
- Methods of collecting primary data: observation, interviews, questionnaires
- Sampling methods; probabilistic and non-probabilistic
- Data presentation: frequency tables and histograms
- Measures of central tendency: arithmetic mean, mode, median
- Measures of dispersion: range, mean deviation, standard deviation, variance, coefficient of variation
Introduction to probability
- Definitions: events, outcome, experiment, sample space
- Types of events: simple, elementary, mutually exclusive, mutually inclusive, dependent and independent
- Laws of probability: addition and multiplication
- Basic probability trees
- Finite probability spaces and conditional probability
Emerging issues and trends
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