Singapore University of Social Sciences

Basic Statistical Methods in Experimental Design (MTH353)

Applications Open: 01 April 2019

Applications Close: 31 May 2019

Next Available Intake: July 2019

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1312 View More Details on Fees

Area of Interest: Science & Technology

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed


Synopsis

MTH353 Basic Statistical Methods in Experimental Design examines how to design experiments, carry them out and analyse the data they yield. Various designs are discussed and their respective differences, advantages and disadvantages noted. Moreover, it focuses on the connection between the experiment and the model that the experimenter can develop from the results of the experiment. There are numerous examples based on real-world applications of experimental design. The course is relevant to those interested in in the design, conduct and analysis of experiments in the engineering and social sciences.

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

Topics

  • Basic Principles and Guidelines for designing experiments
  • Simple Comparative Experiments
  • Analysis of the Fixed Effects Model
  • Practical Interpretation of Results
  • Randomized Blocks, Latin Squares and Related Designs
  • Two-Factor Factorial Design
  • Fitting Response Curves and Surfaces
  • The General 2^k Design
  • Optimality of 2^k Designs
  • The addition of Center Points to the 2^k Design
  • Blocking a Replicated 2^k Factorial Design
  • Confounding the 2^k Factorial Design in 2^p Blocks

Learning Outcome

  • Determine the experimental unit, response variable, factor(s) and level(s) of a basic experiment
  • Demonstrate the role of randomisation and replication in inferring causation
  • Implement a completely randomised design
  • Construct the ANOVA table in R
  • Compute the minimum number of replicates in a completely randomised design to achieve a given level of power
  • Execute pairwise tests of differences in means in R to understand a significant overall F-test
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