Quantitative Techniques and Operations Research Syllabus in MBA

Quantitative Techniques and Operations Research is also known as QTOR in short form, is an important subject in management courses like PGDM, MBA, B.com, M.com, BBA. Here we have shared all the topics that are in the syllabus of Quantitative Techniques and Operations Research in Master of Business Administration.

Why we study Quantitative Techniques and Operations Research?

Quantitative techniques and operations research is a combined study of two subjects. The first one is Statistics for Management and another is Operations Research.

Statistics is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions.

Operations research is a scientific approach to problems solving and an analytical method of decision-making that is useful in the management of organizations.

In operations research, business problems are broken down into basic components and then solved in defined steps by mathematical analysis.

Quantitative Techniques and Operations Research – QTOR Syllabus

Syllabus of QTOR in Masters of Business Administration – UTU Dehradun

Subject Name: Quantitative Techniques and Operation Research
Subject Code: MBAT 103
Course: MBA First Semester 2020-21
University: Uttarakhand Technical University
Total Credit: 3
Internal Marks: 30
External Marks: 70
Total Marks: 100

QTOR Syllabus in MBA – 2 Year

Note: This QTOR Syllabus is as per MBA Academic Session 2020-21 of Uttarakhand Technical University, UTU Dehradun. New Scheme of Examination as per AICTE Flexible Curricula.

Unit 1 – Introduction

Definition of Statistics,
Characteristics,
Functions,
Importance,
Limitations and Types of Statistics,
Uses of Statistics in Functional Areas of Management,
Introduction to Sampling.

Classification and Presentation of Data
Frequency Distribution- Discrete and Continuous Frequency Distribution;
Diagrammatic and Graphic Representation-
Line, Bar, Rectangle and Pie Diagram, Graphs-Histograms, Frequency Polygon,
Cumulative Frequency Curves or Ogives
Advantages and Limitations of Diagrams and Graph,
Tabulation- Types of tables.

Unit 2 – Measures of Central Tendency and Dispersion

Average-Concept,
Types,
Mathematical Averages-
Arithmetic, Geometric, and Harmonic mean,
Position and Locational Averages,
Median,
Mode.

Measures of Dispersion:
Range, Quartile Deviation- Mean and Standard Deviation,

Variance-
Coefficient of Variance-
Comparison of various measures of Dispersion,

Skewness-
Relative Measures of Skewness-
Karl Pearson,
Bowley, Kelly-Coefficient of Skewness,
Kurtosis.

Unit 3 – Correlation and Regression

Correlation-
Scatter Diagram,
Karl Pearson’s Coefficient of Correlation,
Spearman’s Coefficient of Rank Correlation;

Concurrent Deviation;
Regression-
Method of Least Squares,
Method of Regression Coefficient,
Properties of Regression Coefficient,
Partial and Multiple Correlation and Regression Coefficient.

Unit 4 – Time Series and Forecasting

Time Series-
Introduction,
Objectives of Time Series,
Identification of Trend,
Variation in Time Series Secular Variation,
Cyclical Variation,
Seasonal Variation, and Irregular Variation,
Methods of Estimating Trend,
Choosing Appropriate Forecasting Model.

Unit 5 – Probability and Probability Distribution

Classical and Axiomatic Approaches,
Basic Theorems- Addition,
Multiplication- Conditional and Bayes Theorem,
Random variables and concept of Probability Distribution.

Theoretical Probability Distributions:
Binomial,
Poisson, and Normal,
Expontial Distribution and its problems.