# Quantitative Techniques

### A Proven Way To Pass R

R

#### Mock Exam (a) Handling Statistical Data
(i) Source and Collection of Statistical Data
– Primary and secondary data
– discrete and continuous data
– internal and external sources of data
each method.
(ii) Sampling Methods
– purpose of sampling
– methods of sampling: simple random, stratified random, systematic random, quota,
multistage, cluster
(iii) Tabulation and Classification of Data
– tabulation of data including guidelines for constructing tables
(iv) Data Presentation
– frequency table construction and cross tabulation
– charts: bar charts (simple, component, percentage component and multiple), pie chart, Zchart and Gantt chart
– graphs: histogram, polygon, Ogives, Lorenz curve
(b) Measures of Location
(i) Measures of Central Tendency
– arithmetic mean, median, mode and geometric mean
– characteristic features of each measure
(ii) Measures of partition
– percentiles, deciles and quartiles(use of formulae)
– range, mean deviation, standard deviation, coefficient of variation, quartile
deviation and skewness (grouped and ungrouped data)
– estimation of quartiles and percentiles from Ogives
(d) Measures of Relationships
(i) Correlation (Linear)
– Meaning and uses of correlation
– scatter diagrams, nature of correlation (positive, non-correlated, Negative)
– meaning of correlation coefficient and its determination and interpretation
– Spearman’s rank Correlation Coefficient, Pearson’s Product
Moment Correlation.
(ii) Regression Analysis (Linear)
– normal equations, least squares method and the determination of the regression line
– interpretation of regression constant and regression coefficients
– use of regression line for estimation purposes
(e) Time Series
(i) Meaning of time series
(ii) Basic components and two models; Addictive and multiplicative
(iii) Methods for constructing trend line, ege fitting, moving averages, least squares, regression
line, semi-averages
(iv) Methods of determining seasonal indices i.e. average percentage, moving average, link
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relative, ratio to trend and smoothening.
(v) Application to forecasting. Adjusted seasonal variations
(f) Index Numbers
(i) meaning
(ii) problems associated with the construction of index numbers.
(iii) unweighted index i.e. sample aggregative index, mean of price relatives.
(iv) Weighted index numbers e.g. use Laspeyre, Paasche, Fisher and Marshall Edgeworth.
(g) Probability
(i) Definition of probability
(ii) Measurement (addition and multiplication laws applied to mutually exclusive, independent
and conditional events)
(iii) Expectated Values
(h) Values Hypothesis Testing
(i) Hypothesis
– Concept and meaning
– types (Null and alternative)
(ii) Type l and type II errors; level of significance
(i) Testing of hypothesis about single population mean and single proportions for small and
large samples. Differences between means.

(a) Functional Relationships
(i) definition of a function
(ii) types of functions: linear, quadratic, polynomial, logarithmic, exponential and
solutions of their equations including graphical treatment
(iii) applications involving cost, revenue and profit functions
(iv) break-even analysis, concept of equilibrium
(v) determination of break-even point in quantity and value, significance of break-even
point
(vi) simple linear inequalities in only one variable including graphical approach
(b) Mathematics of Finance
(i) Sequences and series (limited to arithmetic and geometric progressions), sum to infinity of a
(ii) simple and compound interests
– net present value of single amount
– present value of series amounts
(iii) Annuities and amortisation
– types of annuities e.g. ordinary and annuity due
– sum of an ordinary annuity (sinking finds)
– present value of an annuity and amortisation
(iv) Net Present Value (NPV)
(v) Internal Rate of Return (IRR)
(c) Differentiation
(i) meaning of slope or gradient or derivative
(ii) rules for differentiating polynomials in one variables
(ii) applications of differentiation e.g. funding marginals, elasticity, maximum and minimum values.
(d) Integration
(i) rules for integrating polynomial (in one variable only) as a reverse of differentiation
(ii) applications of integration in business e.g. finding functions from marginal functions,
determination of consumers and producers surpluses

(a) Introduction
(i) main stages of an Operation Research (OR) project
(ii) relevance of Operations Research in business
(b) Linear Programming
(i) concept and meaning (as a resource allocation tool)
(ii) underlying basic assumptions
(iii) problem formulation in linear programming
(iv) methods of solution
– graphical methods (for 2 decision variables)
(v) Interpretation of results
– Simpleax tableau in three variables only
– Results from simplex method, shadow price, marginal value, worth of resources
c) Inventory and Production Control
(i) Meaning of an inventory
(ii) Functions of inventory
(iii) Inventory costs e.g. holding cost, ordering costs, shortage costs, cost of materials.
(iv) General inventory models e.g. deterministic and stochastic model: periodic review
system and re-order level system.(Limited to one channel)
Functional Relationship
Mathematics of Finance
Differentiation
Integration
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(v) Basic Economic Order Quantity (EOQ) model including assumptions of the model
(d) Network Analysis .
(i) Critical Path Analysis (CPA) and Programme Evaluation and Review Technique (PERT)
(ii) Drawing the network diagram
(iii) Meaning of critical path and how to determine it and its duration
(iv) Calculation of floats or spare times
(e) Replacement Analysis
(i) Replacement of items that wear gradually
ii) Replacement of items that fail suddenly
(f) Transportation and Assignment Models
(i) Nature of transportation and assignment models
(ii) Balanced and unbalanced transportation problems
(iii) Methods for funding initial basic feasible transportation cost: North West Corner Method
(NWCM),Use Hungarian method (UHM),Least Cost Method (LCM) and Vogel’s Approximation
Method (VAM)
(g) Simulation
State, explain and apply simulation technique to business oriented situations
i. Describing simulation as the imitation of the operation of a real-world process or system
over time.
ii. The use of probabilities to assign a random number range.
iii. Explaining the Monte Carlo as a method of simulation.
iv. Construction and running of simple simulations

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This course provides candidates with a sound foundation in Quantitative Techniques which will assist
understanding and competence in business decision-making processes that are encountered in
practice and more.

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