Visual Analytics - An Interaction of Sight and Thought - JMP

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Analytical challenges demand statistical horsepower. JMP is one of best tools available today for combining robust statistical functionality with rich interactive visualizations.
Predictive analysis is one of these challenges. U.S. Dollars (thousands) 4,000 Sales Revenues Sales Revenues 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 One of the analytical challenges that requires a high degree of statistical training is its use for predicting what might happen in the future. Let’s clarify the meaning of predictive analytics. Like many terms that get tossed around by software vendors and even by thought leaders in the field of business intelligence, predictive analytics seems to mean whatever’s convenient at the moment. It might be useful for marketing purposes to keep definitions loose and adaptable to the occasion, but it doesn’t help us at all. We’ll start by turning to everyone’s favorite dictionary these days: Wikipedia. Predictive analytics encompass a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Wikipedia entry for “predictive analytics” as of December 15, 2008.
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Visual Analytics An Interaction of
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Hurray data! Hurray technology! We
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Upon this gifted age, in its dark h
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We must tap into that steady stream
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Data analysts JMP are now sexy. Dat
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To do this we need the right skills
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Two of the right skills • Statist
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What is visual thinking? Temple Gra
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We need greater balance. Modes of r
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Leonardo da Vinci For some four hun
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Complex problems require visual thi
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Tools extend our reach when they’
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Can computers replace us? Our brain
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Visualization empowers statistics.
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“I see!” There is an intimate c
Page 32 and 33: Vision is our dominant sense. 30% 7
Page 34 and 35: Trends, patterns, and exceptions co
Page 36 and 37: This visualization fails to do this
Page 38 and 39: The right balance 38 Information vi
Page 40 and 41: Many visualization tools feature de
Page 42 and 43: Bling your graph. Many software ven
Page 44 and 45: 3-D graphs are rarely useful. One o
Page 46 and 47: Even when there’s a 3 rd variable
Page 48 and 49: We don’t want to confuse people w
Page 50 and 51: Good visualizations and good visual
Page 52 and 53: This arrangement is even better yet
Page 54 and 55: Visual perception works according t
Page 56 and 57: Visual perception is not just camer
Page 58 and 59: Two essential activities • Multi-
Page 60 and 61: Data analysis requires rapid intera
Page 62 and 63: Key interactions of visual analysis
Page 64 and 65: Shneiderman’s Mantra “Overview
Page 66 and 67: Overview first, zoom and filter, th
Page 72 and 73: Attention is limited. Ever saw a ma
Page 74 and 75: Capacity = 3 or 4 chunks at a time
Page 76 and 77: Good analysis tools augment memory
Page 78 and 79: More dimensions through multiple vi
Page 80 and 81: Coordinated views expand what we ca
Page 84 and 85: Predictions rely on statistical mod
Page 86 and 87: Finding and understanding the stori
Page 88 and 89: Wisdom Knowledge Information Inform
Page 90 and 91: O perpetual revolution of configure
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