Information and Decision Sciences

IDS 465 Analysis of Variance and Experimental Design

Credit hours 3 -Undergraduate 4 -Graduate
Catalog description General theory of design and analysis of experiments. Least squares estimation, multiple regression, analysis of variance, randomization, randomized blocks, Latin squares, factorial designs, replication, incomplete blocks.
Key topics
  • Linear Statistical Models
    Deterministic v.s. Probabilistic Models
    Linear Statistical Models
  • Least-Squares Estimation
    • Inference One-Way Analysis of Variance
  • Factorial Models
    Two-Way
    Higher-Way
  • Incomplete Designs
    Latin Squares
    Fractional Factorials
    Incomplete Blocks
  • Random Models
    Random Effects
    Models with Both fixed and Random Effects
  • Nested Models
    Hierarchical Models
    Mixed Nested and Crossed Models
    Analysis of Covariance
Prerequisites for this course IDS 371 and Math 205 (Advanced Mathematics for Business) or Math 310 (Applied Linear Algebra) or Math 320 (Linear Algebra I).
Course(s) for which this is a prerequisite None
Required course for None
Elective course for Selective for MBA Students with a Specialization/Concentration in Statistics
Frequency of offering Not consistently offered
Recent offerings, instructors, syllabi Last offered Fall 1991
Planned offerings None
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