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Understanding ANOVA: Partitioning Variance and Analyzing Effects with MATLAB

DESCRIPTION

This lecture focuses on the concepts of ANOVA (Analysis of Variance), breaking down overall variation into components attributable to different sources such as factors, levels, treatments, and replicates. It discusses the importance of randomization in experimental design and introduces fixed effects models with practical examples. The session also covers interpretation of the standard ANOVA table and the significance of residuals examination. Additionally, it showcases how to create informative box plots in MATLAB to visualize ore grade data and enhance analysis.

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Understanding ANOVA: Partitioning Variance and Analyzing Effects with MATLAB

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Presentation Transcript

  1. Lec. 23 - ANOVA Partitioning variance into components attributable to different sources of variation

  2. factors levels treatments replicates Some Relevant Terminology

  3. Randomization of the experimental material

  4. ANOVA objective: Splitting the overall variation into parts

  5. Fixed Effects Models - Example

  6. Fixed Effects Models – Example cont’d MATLAB makes nice boxplots too: Ex_BoxPlot_OreGrade.m

  7. Fixed Effects Models – Example cont’d

  8. How to interpret the “Standard” ANOVA Table

  9. How to interpret the “Standard” ANOVA Table cont’d

  10. Examination of Residuals

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