Singapore University of Social Sciences

Applications of Regression Analysis

Applications of Regression Analysis (MTH308)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Science & Technology

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C), Resilience 2020

Funding: SkillsFuture

School/Department: School of Science & Technology


MTH308 is the second of two sequential courses on applied regression anlysis (the first one is MTH307: Principles of Regression Analysis). It will focus on the treatment of practical and application issues pertinent to regression analyis. Topics include model building & variable screening, deviations from standard assumptions, and residual analysis.

Level: 3
Credit Units: 5
Presentation Pattern: Every January


  • Model building and variable screening.
  • Models with one, two, three and more qualitative independent variables.
  • Models with both quantitative and qualitative independent variables.
  • All-possible-regression selection procedure.
  • Practical Issues in regression.
  • Data transformations.
  • Residual Analysis.
  • Detecting unequal variances.
  • Checking the normality assumption.
  • Detecting residual correlation: the Durbin-Watson test.
  • Piecewise linear, logistic and ridge regression.
  • Robust regression.

Learning Outcome

  • Apply statistical concepts in regression analysis.
  • Analyze the number of variables in regression models, including variable screening.
  • Use transformations of data to better fit regression models.
  • Verify conclusions from residual analysis, including verification with F and t-tests.
  • Test of variety of general regression models, including nonparametric models.
  • Apply regression models, including those involving binary data.
  • Construct a range of mathematical techniques to solve a variety of quantitative problems.
  • Formulate solutions to problems individually and/or as part of a group.
  • Analyze and solve a number of problem sets within strict deadlines.
  • Verify solutions related to regression analysis using IT.
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