PH1010 – Statistical Analysis & Advanced Quantitative Methods
Doctoral Business Administration
Core Course
PH1010 – Statistical Analysis & Advanced Quantitative Methods
Course Unit Code: PH1010
Type Of Unit: Core
Level of Course Unit: Doctoral
Year of Study: First year
Semester: Fall
Number of ECTS Credits: 10
Mode of Delivery
Face to Face
Prerequisites
Linear Algebra and Basic Statistics
- Introduction to Linear Algebra, Statistics and Statistical Software Platforms CILO 1, 2, 3
- Descriptive Statistics CILO 3, 4
- Inferential Statistics (e.g., Confidence Interval, Hypothesis Testing, Chi-square test) CILO 5
- Simple and Multiple Regression Analysis: Estimation and Inference CILO 3, 6, 7, 9
- Misspecification Tests and Heteroscedasticity CILO 3, 6, 7, 9
- Endogeneity and Instrumental Variables Estimators CILO 3, 6, 7, 9
- Time Series Model: Estimation, Inference and Serial Correlation CILO 3, 6, 8, 9
- Panel Data Models: Estimation and Inference CILO 3, 6, 8, 9
- Binary Choice and Limited Dependent Variable Models: Estimation, Inference and Sample Selection Corrections CILO 3, 6, 8
Course Objectives
This subject is aimed at students in doctoral and other research postgraduate programs. The subject focuses on quantitative research methodology and related inferential statistical techniques including but not limited to regression models, critical assumptions, mediation and moderation, limited dependent variables, panel data, endogeneity, instrumental variable estimation. This subject will include opportunities to apply one or more of these techniques in a research project using specialised computer software. While creative use of technical skills is required, this course emphasizes the mastery of specific methodological and statistical knowledge and skills. The course will address the following topics: the framing of research questions; the selection of appropriate research methods and designs; the selection of appropriate statistics for data analysis; the principles of analysis; interpretation of findings; and the presentation of results. In addition, laboratory time will give each student an opportunity to learn to use statistical packages available on microcomputers and to apply the material presented to actual research problems and data.
Learning Outcomes
For each of the methodologies discussed, we expect that students will be able to:
- Define and explain key elements of statistics and
- Recognize and use the basic concepts of probability theory, and probability and sampling distribution
- Analyse data and present results using Statistical Software Platforms
- Apply tabular, graphical, and descriptive methods in order to study and understand a variable and examine the relationship between two or more variables
- Analyse data results and draw conclusions using a confidence interval and a hypothesis test.
- Recognize the proper data and estimation method to be used, and select the most appropriate models.
- Interpret the results of advanced empirical analyses.
- Discuss published research articles and develop written communication skills.
Course Content
Course Features
Lectures, exercises, lab work and presentations; exam
Examination & Research Paper
Readings
Textbook:
Anderson, D. (2020). Essentials of Statistics for Business and Economics, 9th ed.
Wooldridge, J. (2020). Introductory Econometrics: A Modern Approach, 7th ed.
Optional textbook:
DeGroot, M. and Schervish, M. (2012). Probability and Statistics, 4th ed.
Richard H. (2013). Mathematical Statistics for Economics and Business, 2nd ed.
Stock, J. and Watson, M. (2019). Introduction to Econometrics, 4th ed.
Greene, W. (2018). Econometric Analysis, 8th ed.