Industrial Statistics & Mathematical Finance Stream (IS & MF)

The Department of Statistics conducts the Degree Program Industrial Statistics & Mathematical Finance jointly with the Department of Mathematics for a direct intake of 60 students since 2006.

Level I Course Units Offered by The Department of Statistics

Level II Course Units Offered by The Department of Statistics

Level III Course Units Offered by The Department of Statistics

SemesterPre ReqCourse UnitTitleCreditsHoursIS
IST 3006Regression Analysis230Lx
IST 3009Applied Time Series230Lo
IIS 1009IS 3001Sampling Techniques230Lx
IMS 3009Operational Research II330L 30Px
IIIS 3004Applied Multivariate Methods215L 30 Po
IIIS 3005Statistics in Practice I360Px
IIMS 3004Quality Management/Project Management230Lo

Note: Students may take a maximum of 33 credits. Abbreviations : x – core courses, o – electives, L – lectures, P – practicals


IS 1006 – Fundamentals of Statistics (3C, 30L 30P)

Prerequisites: None

Intended learning outcomes:

Upon successful completion of the course the student should be able to explore and interpret data and draw meaningful conclusions using descriptive methods using statistical software packages. The students should also be able to apply the basic concept of statistical inference through sample information and sampling distribution and formulate the inferential methods appropriately as confirmatory analysis for the descriptive methods. The students should also be able to clearly present statistical information, in both written and oral form.

Course content:

Picturing distributions with graphs: individuals and variables, categorical variables (pie charts, bar graphs), quantitative variables (histograms, stemplots, time plots), picturing distributions with graphs using  statistical software; Describing distributions with numbers: measuring center (mean, median, comparing the mean and the median), measuring spread (quartiles, five-number summary, boxplots, spotting suspected outliers, standard deviation), choosing measures of center and spread, describing distributions with numbers using  statistical software; organizing a statistical problem; The Normal distributions: density curve, the 68–95–99.7 rule, the standard Normal distribution, finding Normal proportions; Relationships between two quantitative variables: explanatory and response variables, scatterplots, adding categorical variables to scatterplots, measuring linear association-correlation, facts about correlation, the best fitted line using least-squares, misuses of correlation and least square relationships; Relationship between two categorical variables: marginal distributions, conditional distributions, Simpson’s paradox; Introduction to statistical inference: population versus sample, random sampling designs, observation versus experiment; Sampling distributions: parameters and statistics, statistical estimation and the law of large numbers, sampling distribution of , central limit theorem; Inference in Practice – the basics: the reasoning of statistical estimation, confidence Intervals, tests of significance.

Method/s of evaluation: End of semester examination (50%) and Continuous assessments [At least 4 in class assignments, 1 group project](50%)

References:

  • The Basic Practice of Statistics – 6th edition (Moore, Notz, Fligner)
  • Statistics for Business and Economics,. Eleventh Edition. (David R. Anderson, Dennis J. Sweeney, Thomas A. Williams)

IS 1007 – Introduction to Statistical Computing (1C, 30P)

Prerequisites: None

Intended learning outcomes:
After a successful completion, a student should be able to perform data management using Excel and handle Excel functions and Recording Macros.

Course content:
Introduction to Excel, Manipulate worksheets, Import/Export files, Templates, Excel functions, Pivot tables, Link and embed worksheets and workbooks, Record Macros, Data analysis tool pack.

Method/s of evaluation: Continuous assessments [At least 4 lab assignments](100%)

References:
• Excel for Engineers and Scientists (S. C. Bloch)
• Microsoft Excel 2013 Bible (John Walkenbach)
• Teach Yourself VISUALLY Excel 2007 (Nancy C. Muir)


MS 1001 – Principles of Management (1C, 15L)

Prerequisites: None

Intended learning outcomes:
After the successful completion of the course the students will recognize the basic concepts of management practices and apply them in a business environment.

Course content:
Concept of management and evolution of management: Scientific management and other schools of thought; Socio-Industrial imperatives for evolution of thoughts; Functional areas of Management: Planning, Organizing, Staffing, Monitoring, Evaluation; Modern management practices.

Method/s of evaluation: End of semester examination (60%) and Business Cases (minimum of 3) & Presentations (40%)

References:
• Fundamentals of Management (Stephen P. Robbins, David A. De Cenzo, Mary Coulter)


IS 1008 – Introduction to Probability & Distributions (3C, 45L)

Prerequisites: IS 1006 -Fundamentals of Statistics

Intended learning outcomes:
Upon successful completion of the course the student should be able to compute probabilities by applying basic rules of probability, construct the probability distribution of a random variable, expectation and variance, identify and compute probabilities based on practical situations using commonly used univariate distributions and order statistics. The student should also be able to apply the concept of two-dimensional random variables and be able to compute probabilities under joint distributions, marginal and conditional distributions and compute such probabilities.

Course content:
Basic concepts of probability: probability definitions, counting rules, probability rules, conditional probability, independence, Bayes theorem, probability theorems; One dimensional random variables: discrete and continuous distributions, expected value, expectation of functions of random variables, variance, associated theorems, moment Generating Functions, distributions of functions of random variables; Some discrete probability distributions: Bernoulli, Binomial, Poisson, Geometric, Negative Binomial; Some continuous probability distributions: Uniform, Exponential, Gamma, Normal; Relationships between distributions; Two –dimensional random variables (discrete): joint distribution, marginal and conditional distributions, independence, conditional expectation; Order Statistics: distribution of minimum, distribution of maximum.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:
• Introduction to Probability and Mathematical Statistics (Lee J. Bain and Max Englehardt)
• A Modern Introduction to Probability and Statistics: Understanding Why and How (Dekking, Kraaikamp, Lopuhaa, and Meester)


IS 1009 – Introduction to Survey Design (2C, 15L 30P)

Prerequisites: None

Intended learning outcomes:
Upon successful completion of the course the student should be able to formulate a real life problem emerging from a large complex population by conducting focus group meetings with industry personal.

Course content:
Producing data via surveys: Random & nonrandom sampling methods, cautions about sample surveys, planning and designing surveys, designing a questionnaire, pretesting, margin of error; Producing data via experiments: Randomized experimental methods, cautions about experiments; Planning and designing a complex industry oriented survey: formulate a problem with the collaboration of industry via focus group meetings, develop an appropriate sampling scheme, develop questionnaire, develop implementation plan.

Method/s of evaluation: End of semester examination (40%) and Continuous assessments [At least 2 in class assignments, 1 group project](60%)

References:
• What is a Survey (Fritz Scheuren)
• Sampling Methods for Census & Surveys (Frank Yates)
• Survey Methodology (Robert M. Groves, Floyd J. Fowler, Jr. etal)


MS 1003 – Operational Research I (2C, 30L)

Prerequisites: MS 1002 -Linear Programming

Intended learning outcomes:
Upon successful completion of the course, the student should be able toidentify decision variables and formulate a suitable Linear Programming model for a real situation, obtain a solution for the formulated model using an appropriate technique and use suitable software to solve the proposed models

Course content:
Introduction to Operational Research, Overview of Linear Programming, Integer Programming and Solution Techniques, Zero-One Programming and Solution Techniques, Transportation Models and Solution Techniques, Assignment Models and Solution Techniques.

Method/s of evaluation: End of semester examination (80%) and Mid semester examination(20%)

References:
• Operational Research: An Introduction (Hamdy A. Taha)
• Operational Research (Harvey M. Wagner)
• Operational Research (Hillier and Liebermann)


IS 2005 – Statistical Packages (1C,30P)

Prerequisites: IS 1008-Introduction to Probability and Distributions

Intended learning outcomes:
After a successful completion, a student should be able to perform data management using SPSS, basic data analysis using SPSS, obtain interval estimations, and perform hypothesis tests.

Course content:
Introduction to SPSS, Data Management, Numerical/ graphical summaries, Applications in hypothesis testing and confidence intervals.

Method/s of evaluation: Continuous assessments [At least 4 assignments](100%)

References:
• Performing Data Analysis Using IBM SPSS (Lawrence S. Meyers, Glenn C. Gamst, A. J.Guarino)
• Discovering Statistics Using IBM SPSS Statistics (Andy Field)


ST 2006 – Basic Statistical Inference (3C, 45L)

Prerequisites: IS 1008 – Introduction to Probability and Distributions

Intended learning outcomes:
Upon completion of this course, students should be able to identify and compute probabilities based on sampling distributions and the central limit theorem, understand the theories of statistical inferences and apply theappropriate models in different settings to solve real-life problems, perform statistical inferences involving the mean, variance and proportion and goodness of fit tests.

Course content:
Sampling distributions, applications of central limit theorem; point estimation, bias and mean square error;
interval estimation, margin of error, determination of sample size; types of errors associated with hypothesis testing, power of the test, power curves; sampling from normal distributions, inferences about the mean and variance; large sample inference, inference for proportions; chi-square goodness-of-fit tests, chi-square tests for association.

Method/s of evaluation: End of semester exam (70%) and Continuous assessment (30%)

References:
• The Basic Practice of Statistics – 6th edition (Moore, Notz, Fligner)
• Mathematical Statistics with Applications (Wackerly, Mendenhall & Scheaffer)


MS 2001 – Statistical Quality Control (2C,30L)

Prerequisites: IS 1008 – Introduction to Probability and Distributions

Intended learning outcomes:
After a successful completion, a student should be able to identify the statistical methods for quality control and fundamentals essential for industrial process control.

Course content:
Introduction to Quality Control; Sampling Inspection: examples and definitions, usage of sampling Inspection, classification of inspection plan; Acceptance Sampling: single, OC curve, average run length, method of choosing sampling plans, inspection schemes; Control charts: control charts for variables (X-bar chart, S chart, R chart), control charts for attributes (P chart, C chart, U chart), lot by lot acceptance sampling for attributes.

Method/s of evaluation: End of semester examination (80%) and Continuous Assessments (20%)

References:
• Introduction to Statistical Quality Control (Douglas C. Montgomery)
• Fundamentals of Quality Controls and Implements (Amitava Mitra)


IS 2003 – Design and Analysis of Industrial Experiments (2C, 30L)

Prerequisites: IS 1008 – Introduction to Probability and Distributions & ST 2006 – Basic Statistical Inference

Intended learning outcomes:
After the successful completion a student should be able to identify the appropriate experimental design and apply it in situations especially on industrial applicationswhere a cause and effect relationship has to be established.

Course content:
Basic elements of experimental design: experimental unit, treatments, replication, randomization; Homogeneous experimental units: completely randomized design with one-way and factorial treatment structures; Blocking for increased precision: randomized complete block, Latin square and in-complete block, designs; Factorial treatment designs; Confounding and partial confounding; Fractional replication; Response surface designs; Mixture experiments.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:
• Design of Experiments: Statistical Principles of Research Design and Analysis – 2nd Edition, 2000 (Robert O. Kuehl)
• Design and Analysis of Experiments (Montgomery)


ST 2009 – Applied Non Parametric Methods (2C, 30L)

Prerequisites: IS 1008 – Introduction to Probability and Distributions & ST2006 – Basic Statistical Inference

Intended learning outcomes:
After the successful completion, a student should be able to identify situations where non-parametric methods are applicable, select the appropriate non-parametric statistical method to apply for a particular problem, apply the method and find the solution for the research question.

Course content:
Introduction, One Sample Location Tests, Tests Involving Two Samples, Two Independent Sample Tests for Differences in Location, Two Independent Sample Tests for Differences in Spread, Two Related Samples, Tests Involving more than Two Samples, Miscellaneous Tests, Test of Randomness, Tests using Frequency Data.

Method/s of evaluation: End of the semester examination (80%) and continuous assessment (20%)

References:
• Practical Non-Parametric Statistics (William,Conover)
• Applied Non-parametric Statistics – 2nd Edition (Wayne W. Daniel)
• Non-Parametric Statistical tests based on ranks (Lehmann)


ST 2010 – Introduction to Statistical Modeling (1C,15L)

Prerequisites: IS 1008 – Introduction to Probability and Distributions

Intended learning outcomes:
Upon successful completion of the course the student should be able to recognize and use different forms of statistical models in the given context.

Course content:
Introduction to concept of Statistical Modeling, Building relationships between variables, understanding the systematic and error components in modeling, Exploration of commonly used statistical models.

Method/s of evaluation: End of semester examination (80%) and Continuous assessment (20%)

References:
• Statistical Methods in agriculture and biology (Roger Mead, Robert N Curnow and Anne M Hasted)


MS 2004 – Introduction to Marketing Research (1C,15L)

Prerequisites: IS 1009 – Introduction to Survey Design

Intended learning outcomes:
Upon successful completion of the course students should be able to apply basic statistical methods in a marketing research context. This course should further establish the fundamentals to obtain a comprehensive understanding of advanced concepts of marketing research and thereby familiarizing the student with the marketing research industry.

Course content:
Introduction, Marketing research process, Qualitative and quantitative methods used in marketing research, Defining a marketing research problem, methods of data collection in marketing research, Sampling design in marketing.

Method/s of evaluation: End of semester examination (70%) and Case studies and presentations (30%)

References:
• Marketing Research (Parasuraman, Dhruv Grewal, R. Krishnan)


 


MS 3004 – Quality Management/Project Management (2C, 30L)

Prerequisites:

Intended learning outcomes:
Upon successful completion of the course students should be able to apply key theoretical concepts on quality control and project management practiced by the corporate world.

Course content:
Quality Management: Macro and Micro organizational Environment (PESTEL, Resource Based View)Market Analysis(Porter’s Five Forces Analysis, SWOT Analysis) Project Feasibility Analysis ( Johnson & Schole’s SFA Framework) Stakeholder Analysis, Organizational Change Management; Project Management: Project Selection, Approach Selection, The Work Breakdown Structure, The Network Diagram, Cost Effort Estimation, Optimizing the Network, Gantt Chart, Risk Management, Cost Estimation, Contract Management, Productivity Improvement, Project Management Steps, Making the Budget, Project Monitoring and Control, Human Resource Management, Project Termination.

Method/s of evaluation: End of semester examination (70%) and Case studies[minimum of 2]and presentations (30%)

References:
• Fundamentals of Project Management (Joseph Heagney)