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Independent Analysis Project. Data Screening - Due April 10 ( 5 points). Instructions: Submit one file by email to [email protected] A ppt file with your name as suffix (e.g., MARS6300_DataScreening_KDH.ppt) containing PC-ORD output text and figures.

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Independent Analysis Project

  • Data Screening - Due April 10 ( 5 points)

Instructions:

Submit one file by email to [email protected]

A ppt file with your name as suffix

(e.g., MARS6300_DataScreening_KDH.ppt) containing PC-ORD output text and figures

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Screening #1 – Data Summary (2 points)

  • Provide a summary of the data sets you will use in your project, making sure you explicitly discuss:
  • How many species / variables selected for analysis ?
  • What criteria did you use to make this selection ?
  • How many environmental variables used ? Are they Q / C ?
  • Summarize environmental variables (skewness) (use PC ORD)
  • Summarize species data (skewness) (use PC ORD)
  • Perform outlier analysis for species / samples (use PC ORD)
  • Summarize sums / ranges for species / variables (use PC ORD)
  • Summarize “zero” data; “empty” species / samples (use PC ORD)
  • Report species dominance (plot / table) – if you have species data
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Screening #2 – Data Transformations (2 points)

  • Provide a summary of the data transformations you will do, making sure you explicitly explain:
  • Which transformations you propose to do and why . Hint: use PC-ORD to identify outliers and to summarize descriptive statistics?
  • Changes in Environmental variable skewness after transformation
  • Changes in Species data skewness after transformation
  • Perform outlier analysis again for species and samples after transformations, to see if they solved problems identified above
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Screening #3 – Cross-Correlations (1 point)

  • Provide a summary of the data cross-correlations, making sure you report the following:
  • Cross-correlations of environmental variables (r and p values)
  • Plot scatterplots of environmental variables (use PC-ORD)
  • Brief narrative explaining what these cross-correlations show
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