1. Create a table of all variables showing, for each variable, whether it is a predictor or response, its name, its type and scale of measurement (e.g., categorical nominal), its levels (for fixed factors), whether it is fixed or random, and its status as nested within and/or crossed with other variables. For now, assume that all covariates are fixed.
2. Conduct a thorough exploration of your data. Follow the guidelines in your “GLM Cheat Sheet” to remind you what steps to follow (although not all steps will be relevant for all projects). Paste your graphs into your word document. Include a caption below each one explaining your interpretation of the figure.
3. Name the kind of analysis you think you will use to analyze your data (e.g., a General linear model with poisson-distributed error terms; a principle components analysis, …)
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There does not seem to be any strange behavior in our data for H2O2 given the level of C60 concentration levels such as influential data points in the scatter plot below. The plot is also grouped with min by different saturation of blue colour. They clearly show the negative relationship between H2O2 and C60 concentration and a positive relationship between H2O2 and min. The subsequent statistical analysis will reveal whether the interaction between two covariates also have some impact on H2O2. All those data exploration techniques would not be necessarily because two predictors are controlled variables. As long as the response (H2O2) appear to be behaving well and it does, we should be able to feed the data into the statistical analysis as it is....
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