Lect2_2700_s09

Lect2_2700_s09 - Overview Scatter& Time Series Freq&...

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Unformatted text preview: Overview Scatter & Time Series Freq & Hist Numerical summaries . . . Title Page JJ II J I Page 1 of 32 Go Back Full Screen Close Quit ENGRD 2700 Engineering Probability and Statistics Lecture 2: Graphical Methods; Data Summarization David S. Matteson School of Operations Research and Information Engineering Rhodes Hall, Cornell University Ithaca NY 14853 USA [email protected] January 21, 2009 Overview Scatter & Time Series Freq & Hist Numerical summaries . . . Title Page JJ II J I Page 2 of 32 Go Back Full Screen Close Quit 1. Graphical Methods and Data Summarization Pictorial and graphical methods for data summarization. Generalities: • Large amounts of data hard to interpret. • Reduction of the full data set to either a picture or to a small set of numerical summaries (mean, standard deviation, ... ).. • Often the full data not recoverable from the pictorial or numerical summa- rization; exception–stem and leaf plot. • Often some features not initially evident pop out immediately with the right kind of pictorial representation. Examples of techniques: 1. Stem and leaf plots 2. Simple plots: Scatter plots; Time series plots. 3. Dot plots 4. Histograms 5. Density estimates. Overview Scatter & Time Series Freq & Hist Numerical summaries . . . Title Page JJ II J I Page 3 of 32 Go Back Full Screen Close Quit Features we hope to find from simple plots like the time series plot: • Is there a trend? • Is variability changing? Features we hope revealed by stem & leaf, histograms or density estimates: • What are the most typical or representative values? (mean & median) Are there several values that might be typical (modes). • Summarize the spread of the data: range and variability. • Are there notable gaps in the data? • Is there symmetry in the data around some typical or representative value? • Are there outliers–values so atypical as to be possibly measurement error or so noteworthy as to attract attention. Notation Univariate case: outcomes of experiment or observational study are real numbers. n = size of the data set; sample size x 1 ,x 2 ,...,x n data values . Example: observe voter behavior (R=0, D=1) and with coding record becomes 0,0,1,0,...,0 (say). Overview Scatter & Time Series Freq & Hist Numerical summaries . . . Title Page JJ II J I Page 4 of 32 Go Back Full Screen Close Quit Multivariate case: outcomes of experiment or observational study are vectors of real numbers: n = size of the data set; sample size ( x 1 ,y 1 ) , ( x 2 ,y 2 ) ,..., ( x n ,y n ) data values . Example: In an Internet study, x i = size of i-th file downloaded y i = time necessary to download the i-th file . Overview Scatter & Time Series Freq & Hist Numerical summaries . . ....
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This note was uploaded on 03/05/2009 for the course ENGRD 2700 taught by Professor Staff during the Spring '05 term at Cornell.

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Lect2_2700_s09 - Overview Scatter& Time Series Freq&...

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