Derivation of partitioning of total variability

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SUMMARY

The discussion focuses on deriving the sum of squares identity in ANOVA (Analysis of Variance). The key equation presented is -\bar{Y}_{i.} + \bar{Y}_{i.} = 0, which illustrates a common mathematical trick used in statistical analysis. Participants seek clarity on how to manipulate this equation to arrive at the total variability partitioning in ANOVA. Understanding this derivation is crucial for accurately interpreting variance in data analysis.

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  • Understanding of ANOVA (Analysis of Variance)
  • Familiarity with statistical notation and equations
  • Basic knowledge of variance and sum of squares concepts
  • Experience with data analysis techniques
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  • Study the derivation of the sum of squares in ANOVA
  • Learn about the partitioning of variance in statistical models
  • Explore the application of ANOVA in real-world data analysis
  • Review common statistical tricks and manipulations in equations
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Statisticians, data analysts, and researchers involved in data analysis and interpretation, particularly those focusing on variance and ANOVA methodologies.

mcguiry03
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it is a sum of squares in anova(analysis of data). how can i derive this equation?tnx..
 

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[itex]-\bar{Y}_{i.} + \bar{Y}_{i.} = 0[/itex]

So you can place that anywhere and you change nothing, it is a common trick.
 
viraltux said:
[itex]-\bar{Y}_{i.} + \bar{Y}_{i.} = 0[/itex]

So you can place that anywhere and you change nothing, it is a common trick.
what is my working equation so i can arrive at the sum of squares identity?
 

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