Quantitative Methology
Autor: RAHUL RANJAN • December 18, 2016 • Course Note • 366 Words (2 Pages) • 838 Views
PGDM 2015-16, Term-I
Quantitative Techniques – I (Statistics)
Instructor: S K Mitra
Course Description
This course is designed as an introduction to basic statistical tools and quantitative methods for first year PGDM students. As the foundation for more advanced research methodologies and statistical analyses, this introductory course emphasizes developing the necessary skills for expressing statistical ideas in clear simple language, which is an essential skill for Managers.
Learning Objectives
On a regular basis managers are called upon to either collect original data or obtain data from various sources. Therefore, you must be comfortable summarizing, analyzing, and presenting quantitative data, and be comfortable developing logical empirically based arguments using statistical techniques and analytic methods.
After completing this course, students will be able to:
Apply correctly a variety of statistical techniques, both descriptive and inferential.
Interpret in plain language and comment on these statistical techniques.
Interpret computer output to perform statistical techniques.
Text Book
Statistics For Business , Stine & Foster, Second Edition, Pearson
Evaluation
Mid Term: 30%
End Term: 30%
Class Participation and Quizzes: 20%
Practical and Assignments: 20%
Pedagogy
This course uses lectures, case analysis plus practical. The practical will comprise discussion, problem solving activities, individual and group work. Students are required to bring laptop in the class.
Session Plan ( tentative)
Session # | Topic | Chapter from Text Book |
Session 1 -3 | Introduction , Data Structures, Summaries | Chapter 1 - 4 |
Session 4 | Variability | Chapter 5, 6 |
Session 5, 6 | Probability | Chapter 6, 7 |
Session 7, 8 | Random Variables | Chapters 8, 9 |
Session 9, 10 | Normal Probability Model | Chapters 12 |
Session 11 - 14 | Statistical Tests | Chapters 15 - 17 |
Session 15 - 16 | Simple Regression | Chapters 21, 22 |
Session 17 - 18 | Multiple Regression | Chapters 23 - 25 |
Session 19 - 20 | Real Life Applications |
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