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ECON 2B03 (45)
Lecture 2

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Jeff Racine

Lecture 2 Chapter 1 & 2 Presenting Data: Tables and Graphs When presenting data either in the form of tables or graphs, it is often desirable to first split the data into groups/ classes How we split the data often depends on the data type Data types: Categorical (nominal or ordinal) Quantitative (discrete or continuous) Regardless of the data type, data classes should be: Collectively exhaustive (must exhaust all logical possibilities for classifying available data) Mutually exclusive (must not overlap or have data in common) One immediately confronts the issue of how many calluses to create Desirable class number: Should fit data type Often recommended: between 5 and 20 Sturgess’s rule: desirable number of classes = k, an integer, where k is the integer closet to (use standard rules for rounding) 1 + 3.3log10 7 Where n is the sample size, and where log n i10the power to which the base (10) is raised to yield n Example (Sturgess’s rule) If n = 100, then log10000 = 3, so 1 + 3.3log 1010 = 10.9 and therefore k = 11 so by Sturgess’s rule you would use 11 classes Data Classes Desirable class widths: Class width = the difference between the lower and upper limits of a class To achieve uniform class widths in a table, divide data set width by desirable class number Approximate class width: (Largest value – smallest value)/ (desirable class number) Tabular Components We first consider creating effective tabular summaries An effective table includes: Number (often based on chapter or page numbers) Titles (focus on what, where, and when) Caption (brief verbal summary) Footnotes (e.g., size of sampling error, likely extent of systematic error, etc.) Decimals (consistent number of decimals) Rounding (consistent rounding rules) Class sums (sum of data pertaining to each class; crucial for open-ended classes) Frequency Distributions Freq
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