Singapore University of Social Sciences

Applied Statistical Methods and Causal Analysis

Applied Statistical Methods and Causal Analysis (ANL553)

Applications Open: 01 April 2022

Applications Close: 31 May 2022

Next Available Intake: July 2022

Course Types: Modular Graduate Course

Language: English

Duration: 6 months

Fees: $2200 View More Details on Fees

Area of Interest: Business Administration

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed

School/Department: School of Business


Synopsis

ANL553 Applied Statistical Methods and Causal Analysis introduces the concept of statistical inference, empirical methods and causal analyses that are hands-on and practitioner focused. The course will provide an understanding of the fundamental principles of various empirical methods for statistical inference and causal analysis. The course will begin by covering basic concepts in statistics and programming. It will focus on common confounding challenges of causal analysis, and how classical empirical approaches such as linear regression and panel data regression may address them. It will also cover modern approaches for causal analysis such as instrumental variable estimation and difference-in-differences estimation. The course will focus on equipping students with the sound intuition and practical research skills in conducting statistical and causal analysis to address relevant business problems.

Level: 5
Credit Units: 5
Presentation Pattern: Every July

Topics

  • Fundamental statistical concepts
  • Programming for statistics
  • Hypothesis testing and statistical inference
  • Common issues in empirical design
  • Randomized controlled trials and quasi-natural experiments
  • Linear regression concepts
  • Linear regression model design
  • Panel data regression concepts
  • Panel data regression results and interpretation
  • Regressions with dummy variables
  • Difference-in-differences estimation
  • Instrumental variable regression

Learning Outcome

  • Construct testable hypotheses from available data
  • Test various hypotheses with appropriate statistical tests
  • Evaluate the suitability of various empirical approaches for different business problems
  • Assess the advantages and pitfalls of the various empirical approaches
  • Design experiments, research to understand the relationship between variables of interest
  • Construct a programming workflow to execute an empirical method
  • Design an empirical method, interpret and deploy the results of the empirical analysis
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