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The cereal dataset is a flat table with 14 attributes and 77 items of data. “Manufacturer name” is categorical, as manufacturer names do not have an implicit ordering to them; thus external information is necessary for them to be sorted. “Type” refers to whether Hot or Cold, hence categorical. “Calories”, “Protein”, “Fat”, “Sodium”, “Fibre”, “Complex_carbohydrates”, “Sugars”, “Potassium”, “Weight”, “Cups” - these are all quantitative attributes. These are all measures of magnitude, for a given attribute for a given item, that support arithmetic comparison. “Display shelf” is ordinal; that is, its ordering has implicit meaning. The shelf numbering can be used as a base for an analysis, as the data can be interpreted as say distance of the product from the floor. “Vitamin_minerals” are ordinal. Though numeric, this attribute does not represent a measurable quantity that pertains to the item. Thus it is unlikely for this “quantity” (0,25,100) to be used in the context of a meaningful arithmetic comparison....
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