MSC / P.G. DIPLOMA IN BUSINESS STATISTICS

Department of Mathematics

Faculty of Engineering

University of Moratuwa

OBJECTIVE OF THE COURSE

Statistics is a much versatile subject which can be applied in almost any field. During the recent past, there has been an increasing demand to train executives in the government departments and private organizations to use statistics as a business and management tool. Thus, this course is designed for graduates working in a business or an industrial environment who wish to extend their knowledge through a postgraduate qualification in statistics, irrespective of the discipline of the first degree. Therefore, the course is structured with more emphasis paid on handson skills in applying statistics to industrial problems and the use of common statistical packages used in data analysis.

The course content includes a wide range of core modules that deal with theoretical and practical statistical topics and day to day application of these under a business environment. The course content has been designed to train executives with a strong background in the application of methodology with some basic knowledge in theory of statistics to be used in any business and finance context. While the course covers all necessary theory, the emphasisis given throughout the course for applications of statistics to business-related problems.

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There will be an independent study at the end of taught courses of which the candidates are encouraged to tackle a practical problem in their working environment, preferably a business/management problem which can be handled by statistics knowledge acquired during the course.

The ultimate goal of the course is to produce management statisticians with a strong background in the methodology and theory of statistics.

DOCUMENT 1 - ELIGIBILITY REQUIREMENTS

Candidates applying for the Master of Science in Business Statistics or Postgraduate Diploma in Business Statistics shall have

(a)The Degree of Bachelor of Science of Engineering of the University of Moratuwa.

OR

(b)Any other Engineering or Science degree of at least four year duration in a relevant field of specialization, from a recognized university; the recognition of the university, the acceptability of the course, and the relevancy of the field to be judged by the Faculty and approved by the Senate of University of Moratuwa.

OR

(c)Any other Engineering or Science degree of at least three year duration from a recognized university, AND a minimum of one year of experience in a relevant field after obtaining such degree; the recognition of the university, the acceptability of the course, and the relevancy of the experience to be judged by the Faculty and approved by the Senate of University of Moratuwa.

OR

(d)At least the Associate Membership (satisfying the educational requirements for Corporate Membership or similar graduate membership) of a recognized professional institute in a relevant field AND a minimum of one year of appropriate experience after obtaining such membership; the acceptability of the Associate Membership status of the candidate, and the recognition of the institute and the relevancy of the field for this purpose shall be judged by the Faculty and approved by the Senate of University of Moratuwa.

DOCUMENT 2 - CURRICULUM AND SCHEME OF EVALUATION

Compulsory Modules

Code / Compulsory Module / Credits / Evaluation (%)
Continuous Assignment / Final Exam
MA 5201 / Probability and Statistics / 4 / 40±10 / 60±10
MA 5202 / Statistical Modeling in Business / 4 / 40±10 / 60±10
MA 5203 / Time Series Analysis for Business Forecasting / 4 / 40±10 / 60±10
MA 5204 / Quantitative Analysis for Management / 4 / 40±10 / 60±10
MA 5205 / Survey Samplings for Business / 4 / 40±10 / 60±10
MA 5206 / Statistical Quality Control / 4 / 40±10 / 60±10
MA 5207 / Applied Multivariate Techniques in Business / 5 / 40±10 / 60±10
MA 5208 / Operational Research Techniques / 4 / 40±10 / 60±10
MA 5209 / Business and Financial Mathematics / 4 / 40±10 / 60±10
MA 5390 / Projects on Business Statistics
(Post Graduate Diploma only) / 4 / - / 100
MA 5391 / Research Project (MSc only) / 20 / - / 100

Elective Modules

Code / Elective Modules / Credits / Evaluation (%)
Assignment / Exam
MA 5231 / Computer Software in Business and Management / 3 / 40±10 / 60±10
MA 5232 / Principles of Marketing / 3 / 40±10 / 60±10

DOCUMENT 3- SYLLABI OF COURSE UNITS

Complusory Modules

MA 5201Probability and Statistics (4 Credits)

Learning Objectives:

The aim of this course is to train students to carry out explanatory data analysis using descriptive statistics.

Outline Syllabus:

Probability distribution theory, conditional probability, Bayes theorem, discreate and continous random varaiables, estimations, bias and unbiased estimators,confidence intervals under different conditions, properties of common probability distributions (Binomial, Normal, Poisson, Exponetial, Gamma), sample and population properties, testing statistical hypothesis, decision theory and utility theory, describing data sets using various statistical indicatorrs, summarizing data, methods of presenting variability in data series, non-parametric tests, use of MINITAB, and MATLABsoftwares for explantory data analysis.

MA 5202Statistical and Modeling inBusiness(4 Credits)

Learning Objectives:

The aim of this course is to introduce statistical linear methods for analyzing quantitative response data in businessenvironment.

Outline Syllabus:

Regression Analysis: Introduction to simple linear regression,parameter estimation using least square methods, coefficient of determination, properties of the parameters, inferences in regression, matrix approach to linear regression, lack of fit tests, multiple linear regression, model selection procedures in multiple regression, residuals and influence diagnostics, detecting and combating multicollinearity, linear and non linear transformations, Box-Cox transformation, non-linear regression, comparison of regression models and use of dummy variables in regression.

Experimental Designs: Basic concepts of experimental design, Completely Randomized Design, Randomized Complete BlockDesign, LatinSquare Design, Incomplete BlockDesign, factorial experiments; concept of confounding, confounding in 2nfactorial experiments, partial confounding, fractional replication; response surface designs.

Data Analysis: Real data sets are analyzed using SAS, SPSS and Minitab

MA 5203Time Series Analysis for Business Forecasting(4 Credits)

Learning Objectives:

The purpose of this course is to provide students various statistical tools for forecasting of production and financial time series data.

Outline Syllabus:

Introduction of time series, concept of autocorrelation and partial autocorrelation, casual models in forecasting, decomposition analysis in forecasting, smoothing techniques in forecasting, concept of stationary time-series data, Box-Jenkins models in forecasting, use of seasonal ARIMA models, filtering techniques, heteroskedasticity in financial time series, ARCH and GARCH models, concept of multivariate time series, co-integration modeling, Dickey-Fuller test.

Data Analysis: Real data sets are analyzed using SAS,SPSS and Minitab

MA 5204Quantitative Analysis for Management(4 Credits)

Learning Objectives:

The objective of this course is tointroduce the use of mathematical approaches to solve managerial problems.

Outline Syllabus: Linear programming problems, graphical method, Simplex method, economic interpretation of LPP, transportation algorithms, balanced and unbalanced transportation problems, degeneracy, assignment problems, transshipment problems, network flows, maximal flow, minimal flow, minimum spanning tree, and shortest path algorithm in the network, labeling technique, connection between network flow and transportation, matrix solution, and inventory control.

MA 5205Survey Samplings for Business (4 Credits)

Learning Objectives:

This objective of this course is to provide sound knowledge inconducting and analyzing a survey for business/marketing project.

Outline Syllabus:

Basic ideas in sampling, simple random sampling, probability proportional sampling, systematic sampling, stratified sampling, cluster sampling, multistage-sampling, double sampling procedures, allocation of sample, estimation problems, ratio and regression estimators, questionnaire design,management of surveys, coding variables, computerizing data, preparing tables and figures, common techniques in analysis of survey data andwriting survey reports.

Data Analysis: Real data sets are analyzed using SAS and SPSS

MA 5206Statistical Quality Control (4 Credits)

Learning Objectives:

The objective of the course is to provide students statistical quality control techniques for producing affordable products that meet customer and consumer expectations.

Outline Syllabus:

Fundamental concepts of quality control, concepts of statistical quality control, quality improvement tools, systematic variation, random variation. chance and assignable causes, statistical process control, setting up operating control charts for , R, and S, control charts for attributes, control charts for fractional rejected, control charts for nonconformities, cumulative sum control charts, exponentially weighted moving average(EWMA) control charts, accepting sampling problem, operating characteristic curve, single sampling plan for attributes, double, multiple, andsequential sampling, the Dodge-Roming sampling plans (AOQLand LTPD plans), Duncon’s model for the economic control chart, capable process, capability & performance indices, control charts for multiple assignable causes, models for quality management and problem solving

MA 5207Applied Multivariate Techniques in Business(5 Credits)

Learning Objectives:

The aim of the course is to focus on the analysis of multivariate data in business environment.

Outline Syllabus:

Types of measurement scales, geometric concept of multivariate data, multivariate plots and diagrams, basicconcept of matrices and eigen values, introduction todata mining and warehousing, properties of multivariate normal distribution, multiple regression techniques, principal component analysis, factor analysis, confirmatory factor analysis, cluster analysis, two group and multiple group cluster analysis, canonical correlation analysis, logistics regression, multivariate analysis of variance.

Data Analysis: Real data are analyzed using statistical software such as Minitab, SAS and SPSS

MA 5208Operational Research Techniques (4 Credits)

Learning Objectives:

The aim of the course is to introduce students the probabilistic approach to managerial decision-making.

Outline Syllabus:
Revised simplex algorithm, Dual Simplex Algorithm, sensitivity analysis, parametric programming,integer programming, Gomory's cutting plane, branch and bound, the Knacpsack problem, delayed column generation, the cutting stock problem, decision theory, structuring the decision situations, decision making under uncertainty, utility theory,dynamic programming, waiting line theory, queue discipline, introduction to stochastic processes, M/M/1, M/M/m systems with finite & infinite population, introduction to other queuing models and queuing networks,Markov chains and introduction to simulation.

MA 5209Business and Financial Mathematics(4 Credits)

Learning Objectives:

The purpose of this course is to provide mathematical and financial skills in planning and managing financial activities in business environment.

Outline Syllabus:

Forward contracts, future contracts, options, types of trades, hedgers, seculators,on-step binomial models, risk neutral valuation, 2-step binomial trees, American options, the Markov property, continuous time processes, process for stock price, parameters, Ito’s Lemma.

The Black-Schole-Merton model: Lognormal property of stock price, distribution of the rate return, expected return, volatility, concept underlying Black-Schole-Merton differential equation, risk neutral valuation, Black-Schole pricing formula.

Options of stock indices:Results for stock paying a known dividend yield, options pricing formulas, options on stock indices, currency indices, currency options, future options, evaluation of future options using a binomial tree, Black’s model for valuing future’s options.

Elective Modules

MA 5231Computer Software in Business and Management(3 Credits)

Learning Objectives:

The aim of this course is to make the students confident to use common statistical and spreadsheet computerpackages in business environment

Outline Syllabus:

Data management in SPSS (data entry and definition, retrieving and saving SPSS files, transforming data and creating new variables), summarizing data in SPSS (graphical descriptions, tabular presentations, basic statistical indicators), data mangemnt, summarizing and analysis in Excel and mathematical progamming in Matlab.

MA 5232Principles ofMarketing (3 Credits)

Learning Objectives:

The objective of the course is to introduce the principles of marketing management within a business context.

Outline Syllabus:
The role of marketing at the corporate and business level, marketing information and marketing research, marketing intelligence, marketing research process, junctions, consumer markets and buyer behavior, industrial markets and organizational buyer behavior, market segmentation, targeting and positioning, new product development, managing the product line, selecting and managing marketing channels, design of marketing communication and sales promotion, marketing services, international marketing, organization implementation and control of marketing programs.

MA 5290 Project on Business Statistics (PG Diploma only)

Learning Objective:

The aim of the project is to provide an opportunity of further practicing in analyzing a set of real data in the field of Business using statistical techniques and interpretation results in order tomake the students more comfortable to tackle the analytical problem independently. The students have to write a short report on the data analysis of which consists of minimum of 20 pages.

Assessment: Report (60%) and Oral presentation (40%)

MA 5291 Research Project for M Sc

Learning Objective:

This subject aims students in the development of research methodology appropriate to the practice of Business Statistics, and gives students the opportunity to work on problems of Business Statistics that have realsignificance value. The work should usually relates to the any subject area on Business Statistics, and requires knowledge and skill acquired in the course. A thesis and an oral presentation are required upon completion of the project

Assessment: Thesis (70%) and Oral presentation (30%)

DOCUMENT 4 - PERFORMANCE CRITERIA

4.1: Postgraduate Diploma

4.1.1Title of the Award: Postgraduate Diploma in Business Statistics

Field of Specialization: Business Statistics

4.1.2 Participation in Academic Program:

  1. The candidate is required to have attended at least 80% in lectures, tutorial classes, seminars and other components.
  2. Undertake an individual project, as assigned by the Department, on a specific subject area.
  3. No postponement of the course or course modules is allowed without the prior approval of the Senate.

4.1.3 Pass in the Postgraduate Diploma:

  1. A candidate is deemed to have passed the Postgraduate Diploma if the candidate has:

a)successfully completed the required course units,AND

b)successfully completed the prescribed seminars AND

c)successfully completed all the prescribed assignments, laboratory work, AND

d)successfully completed the prescribed project/research component [MA5290]

Note: In order to be considered successful and earn credit for the course unit, the candidate must earn grade C+ or above. Where a course unit consists of more than one component (written examination, seminars, laboratory work, assignments etc) the pass mark for each component is 40%.

  1. If the candidate is unsuccessful in any of the parts 1(a) through 1(d), he/she may be re-examined. Normally only one re-examination will be allowed and this shall be at the next holding of the examinations or assessments. No postponement shall be allowed without approval from the Senate.
  1. Classes will not be awarded.

4.1.4Credit Rating: A credit is defined as one hour of lectures per week for the duration of one Semester which will usually be of 14 weeks duration. A credit will also be equivalent to 2 hours of assignments, tutorials, practical work etc. or equivalent per week for one semester.

4.1.5Grading of Marks: Performance of the candidate in each course unit shall be graded based on the following benchmarks:

Grade / Benchmark / Grade Point / Description
A+ / >= 85% / 4.2
A / 75% - 84% / 4.0 / Excellent
A- / 70% - 74% / 3.7
B+ / 65% - 69% / 3.3
B / 60% - 64% / 3.0 / Good
B- / 55% - 59% / 2.7
C+ / 50% - 54% / 2.3 / Pass
C / 50% - 54% / 2.0 / Pass (Repeat Candidate)
I / 0 / Incomplete
F / 0 / Fail

A candidate who has not earned a grade of C+ or above in a particular course unit at the first attempt, but has obtained minimum marks for at least one component, receives the grade I; otherwise he receives the grade F. By repeating the incomplete component for those obtaining the grade I, or all the components for those obtaining the grade F, the candidate can upgrade grade to C only and this will be used for calculating the grade point average (GPA).

4.1.6Calculation of Grade Point Average: The overall grade point average (GPA) of the postgraduate examination will be calculated according to the following formula.

Note: All credits offered by the student, irrespective of whether completed or not will be considered in the evaluation of the Overall GPA.

4.1.7Release of Result of Written Examination: Performance of a candidate at the written examination shall be released after the Board of Examiners meeting, subject to confirmation of the Senate, unless the Board of Examiners recommends withholding of the results for specific reasons.

4.1.8Criteria for the Award of the Postgraduate Diploma:

  1. Passed the Postgraduate Diploma as specified in clause 4.1.3 and obtain minimum of 44 credits.

AND

  1. Not desirous of proceeding to the Master's dissertation, either before commencement or thereafter, as indicated in writing to the Head of the Department OR Not able to undertake/complete the Master's dissertation under the prescribed conditions.

4.1.9Date of Award: The effective date of the Postgraduate Diploma shall be the first day of the following month after the successful completion of all of the following components of the postgraduate diploma:

  1. written examinations
  2. seminars, assignments, laboratory work and projects
  3. successfully completed the project [MA 5290]
  4. Obtain a minimum of 44 Credits

4.2: Master of Science Degree

4.2.1 Title of the Award: Master of Science in Business Statistics

Field of Specialization: Business Statistics

4.2.2 Participation in Academic Program:

  1. The candidate is required to have attended at least 80% in lectures, tutorial classes, seminars and other components
  2. Passed the postgraduate examination as specified in clause 1(a), 1(b) and 1(c) of 4.1.3.
  3. Undertake an individual research project, as assigned by the Department, on a specific subject area, for a period of not less than one academic year duration on a part time basis or equivalent [MA 5391]
  4. The postponement of the dissertation will only be allowed with prior approval from the Senate.

4.2.3 Pass in the Dissertation:

  1. The candidate will be graded based on the evaluation of the final seminar and oral examination by a panel of examiners.
  2. The grading of the dissertation is directly on a letter Grade. The benchmark performance given in clause 4.1.5 may be used for guidance.
  3. A candidate is deemed to have passed the dissertation, if the candidate earns the Grade C+ or above at the first attempt.
  4. If the candidate is unsuccessful in dissertation, he/she may be re-examined and given the pass grade C, if successful. Normally only one re-examination will be allowed, usually after a minimum of three months but not exceeding 12 months after the initial examination/assessment.

4.2.4Criteria for the Award of the M.Sc. Degree: