Paper 05: Quantitative Analysis*

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About Course

The Professional courses are administered at Foundation, Intermediate and Advanced Levels. Each level requires an average of one year, though candidates are advised to provide for an additional one year to meet requirements for internship/ practical experience.

A student must book for a minimum of three papers in a level in any order unless is exempted or has credits.

Prior to certification, candidates will be required to:

  • Attend workshops on ethics, soft skills and emerging issues organised by kasneb and ICPAK and earn IPD hours.
  • Obtain 1-Year practical experience, or alternatively attend workshops on work based simulation organised by kasneb and ICPAK.

This course is aimed at persons who wish to qualify and work or practice as professional accountants, auditors, finance managers, tax managers and consultants in related areas in both public and private sectors.

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Course Content

JAN – APR 2025 CLASS RECORDINGS

  • FEB 7TH
    01:52:00
  • FEB 8TH
    01:42:00
  • FEB 15TH
    50:00
  • FEB 21ST
    47:00
  • FEB 22ND
    01:15:00
  • FEB 28TH
    01:29:00
  • MAR 14TH
    01:03:00
  • MAR 21ST
    01:00:00

April August 2024

online class link

Overview

Week 1,2,3,4 Topic 1. Mathematical Techniques

Week 5 Topic 4. Correlation and Regression Analysis
4.1 Correlation Analysis

Week 6 Topic 5. Regression Analysis
5.1.1 Simple and multiple linear regression analysis 5.1.2 Assumptions of linear regression analysis 5.1.3 Coefficient of determination, standard error of the estimate, standard error of the slope, t and F statistics

Week 7 Topic 6. Time series
6.1 Definition of time series 6.2 Components of time series (circular, seasonal, cyclical, irregular/ random, trend) 6.3 Application of time series 6.4 Methods of fitting trend; freehand, semi-averages, moving averages, least-squares methods 6.5 Models - additive and multiplicative models 6.6 Measurement of seasonal variation using additive and multiplicative models 6.7 Forecasting time series value using moving averages, ordinary least squares method and exponential smoothing

Week 8,9 Topic 7. Linear programming
7.1 Definition of decision variables, objective function and constraints 7.2 Assumptions of linear programming 7.3 Solving linear programming using graphical method 7.4 Solving linear programming using simplex method (basic scenarios)

Week 10,11 Topic 8 Decision Theory
8.1 Definition 8.2 Decision-making process 8.3 Decision-making environment; deterministic situation (certainty) 8.4 Decision making under risk - expected monetary value, expected opportunity loss, risk using the coefficient of variation, the expected value of perfect information 8.5 Decision trees - sequential decision, the expected value of sample information 8.6 Decision making under uncertainty - maximin, maximax, minimax regret, Hurwicz decision rule, Laplace decision rule.

Week 12 Topic 2. Probability
2.1 Set Theory 2.2 Definition 2.3 Types of sets 2.4 Set description; enumeration and descriptive properties of sets 2.5 Venn diagrams (order - Venn diagrams precede operation of sets) 2.6 Operations of sets; union, intersection, complement and difference 2.7 Probability Theory and Distribution

Week 13 Topic 3. Hypothesis Testing and Estimation
3.1 The arithmetic mean and standard deviation 3.2 Hypothesis tests on the mean (when population standard deviation is unknown) 3.3 Hypothesis tests on proportions 3.4 Hypothesis tests on the difference between two proportions using Z and t statistics 3.5 Chi-Square tests of goodness of fit and independence 3.6 Hypothesis testing using R statistical software

past paper

revision sessions

Past sessions

Class recordings- Sept-Dec 2024

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