GradStat

Introduction to Statistical Research Methods
(MBA, MBA(IT), MA (Educ. Mgt))

[Summary]|[ Objectives ]|[ Modules ]|[ Teaching]|[References ]|[Assignments]|[ Exams]|[ Grades]

Course lecturer:               Denis Ssebuggwawo

Teaching Assistant(s):     
 Consultation hours:          By appointment: E-mail

Lecture schedule:            Monday 2.00 – 4.00 p.m.Tuesday 2.00 – 4.00 p.m. Saturday 10.00 a.m. – 1.00 p.m.

SUMMARY

Statistics is the science that deals with the collection, organization, analysis and interpretation of both numerical and non-numerical data. Collection of Data is the process of obtaining measurements or counts or observations. Organization of Data is the task of presenting the collected data in a form suitable for deriving logical conclusions. Analysis of Data is the process of extracting from the given measurements, counts or observations relevant information from which a summarized and comprehensive numerical description can be formulated. Interpretation of Data is the task of drawing logical conclusions from the analysis of the data and usually involves the formulation of predictions concerning a large collection of objects from information available for a small collection of similar objects.

Statistics, therefore, is an investigative scientific technique that deals with problems capable of being answered, to some degree, by numerical information which is obtained by measuring or counting. In today’s global and economic environment, vast amounts of statistical information are available. The most successful managers and decision-makers are the ones who understand information and use it effectively.

This course introduces the student to the basic statistical techniques used in the collection, organization, analysis and interpretation of statistical data.

OBJECTIVES
The main objectives of the course are to impart knowledge and skills to students. At the end of the course the student should:
  • Know the different techniques used in data collection
  • Be able to properly organize and present the collected data in a suitable format, tabular, graphical etc.
  • Learn how to analyse the collected (and well-organized) data using statistical analysis techniques
  • Know how to interpret the analysis results and how to draw conclusions thereof.
  • Know some common forecasting techniques
  • Learn how to use statistical packages such as SPSS, Stata, Staistica, MINITAB, S-Plus or EPI-Info, etc.
MODULES & LECTURE SLIDES
The course is divided in two modules: Module 1 and Module 2. Module 1 consists of the following units:
  • Unit 1: Statistics as a Scientific Investigative and Research Technique
  • Unit 2: Data Classification, Data Sources and Data Collection
  • Unit 3: Data Organization and Presentation
  • Unit 4: Descriptive Statistics: Numerical Methods
  • Unit 5: Introduction to Probability Theory
Module 2 consists of the following units:
  • Unit 6: Random Variables and Probability Distributions
  • Unit 7: Hypothesis Testing
  • Unit 8: Linear Regression Analysis
  • Unit 9: Time Series and Forecasting Techniques
The details are found in the course outline. Lecture Slides are given in the table below.
Unit 1 Statistics as a Scientific Investigative and Research Technique Lab 1
Unit 2 Data Classification, Data Sources and Data Collection Lab 2
Unit 3 Data Organization and Presentation Lab 3
Unit 4 Descriptive Statistics: Numerical Methods Lab 4
Unit 5 Introduction to Probability Theory Lab 5
Unit 6 Random Variables and Probability Distributions Lab 6
Unit 7 Hypothesis Testing Lab 7
Unit 8 Linear Regression Analysis Lab 8
Unit 9 Time Series and Forecasting Techniques Lab 9
TEACHING & ASSESSMENT
Lectures Notes, Slides and/or Hand-outs and practical sessions with a statistical package.
REFERENCES & RESOURCES

Course Text Book: Levin and Rubin, Statistics for Management, Sixth Edition, Prentice-Hall, 1994 ISBN-0-13-847781-7

References:

1)      S. P. Gupta, Introduction to Statistical Methods 2)      S. C. Gupt, Fundamentals of Statistics 3)      Ronald Walpole, Introduction to Statistics 4)      Allen L. Webster, Applied Statistics for Business and Economics 5)    Abelson R. P. (1995). Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum Associates. 6) Dallal, G. E. (1997). Some Aspects of Study Design. http://www.tufts.edu/~gdallal/STUDY.HTM 7) Fisher, R. (1973). Statistical Methods and Scientific Inference. (3rd ed.). New York: Macmillan.

ASSIGNMENTS/TEST, LAB SESSIONS & GROUP DISCUSIONS

Lectures Notes and/or Hand-outs. At least 4 assignments (35%) and 2 tests (15%) which will contribute 50% of the final assessment and the remaining 50% will be in the form
of a three-hour examination.
EXAMS
GRADES

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