All Discussions Tagged 'Analysis' - AnalyticBridge2020-09-22T11:53:38Zhttps://www.analyticbridge.datasciencecentral.com/forum/topic/listForTag?tag=Analysis&feed=yes&xn_auth=noNew Webinar: Modeling Time-Series Forecasts with @RISKtag:www.analyticbridge.datasciencecentral.com,2011-10-20:2004291:Topic:1575612011-10-20T16:19:34.992ZEric Torkiahttps://www.analyticbridge.datasciencecentral.com/profile/EricTorkia
<p>Hi Everybody:</p>
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<p>Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.</p>
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<p>This webinar is going to present these approaches and how they can be combined to improve both tactical…</p>
<p>Hi Everybody:</p>
<p> </p>
<p>Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.</p>
<p> </p>
<p>This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm. </p>
<p> </p>
<p>We will discuss these topics as well as present practical models and applications using @RISK.</p>
<p> </p>
<p style="text-align: center;"><a href="http://www.crystalballservices.com/Resources/ConsultantsCornerBlog/EntryId/76/Modeling-Time-Series-Forecasts-with-RISK.aspx">http://www.crystalballservices.com/Resources/ConsultantsCornerBlog/EntryId/76/Modeling-Time-Series-Forecasts-with-RISK.aspx</a></p>
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<p style="text-align: left;">Thanks and I look forward to your feedback!</p>
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<p style="text-align: left;">Eric</p> The Need for Speed: A performance comparison of Crystal Ball, ModelRisk, @RISK and Risk Solvertag:www.analyticbridge.datasciencecentral.com,2011-09-21:2004291:Topic:1519742011-09-21T18:58:16.286ZEric Torkiahttps://www.analyticbridge.datasciencecentral.com/profile/EricTorkia
<div class="discussion"><div class="description"><p><a href="http://storage.ning.com/topology/rest/1.0/file/get/2059716030?profile=original" target="_self"><img class="align-right" src="http://storage.ning.com/topology/rest/1.0/file/get/2059716030?profile=RESIZE_480x480" width="350"></img></a> There are very few performance comparisons available when considering the acquisition of an Excel-based Monte Carlo solution. It is with this in mind and a bit of intellectual curiosity that we decided to evaluate Oracle Crystal Ball, Palisade @Risk, Vose ModelRisk and Frontline Risk Solver in terms of speed, accuracy and…</p>
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<div class="discussion"><div class="description"><p><a target="_self" href="http://storage.ning.com/topology/rest/1.0/file/get/2059716030?profile=original"><img width="350" class="align-right" src="http://storage.ning.com/topology/rest/1.0/file/get/2059716030?profile=RESIZE_480x480" width="350"/></a>There are very few performance comparisons available when considering the acquisition of an Excel-based Monte Carlo solution. It is with this in mind and a bit of intellectual curiosity that we decided to evaluate Oracle Crystal Ball, Palisade @Risk, Vose ModelRisk and Frontline Risk Solver in terms of speed, accuracy and precision. We ran over 20 individual tests and 64 million trials to prepare comprehensive comparison of the top Monte-Carlo Tools.</p>
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<p>We have posted a full length PDF and the test results in Excel.</p>
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<p><a rel="nofollow" href="http://links.visibli.com/37a8e3becd332a23/?web=483803&dst=http%3A//www.crystalballservices.com/Resources/ConsultantsCornerBlog/EntryId/75/The-Need-for-Speed-A-performance-comparison-of-Crystal-Ball-ModelRisk-RISK-and-Risk-Solver.aspx" target="_blank">READ ARTICLE NOW</a></p>
<p> </p>
<p><a rel="nofollow" target="_blank" href="http://links.visibli.com/37a8e3becd332a23/?web=483803&dst=http%3A//www.crystalballservices.com/Resources/ConsultantsCornerBlog/EntryId/75/The-Need-for-Speed-A-performance-comparison-of-Crystal-Ball-ModelRisk-RISK-and-Risk-Solver.aspx">http://www.crystalballservices.com/Resources/ConsultantsCornerBlog/...</a></p>
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<p>I look forward to your comments and feedback!</p>
<p> </p>
<p>Eric</p>
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</div> Check out the 2nd part in the Excel Simulation Show-Down Video Series: Distribution Fitting (Risk Solver Vs. ModelRisk vs. Crystal Ball vs. "@risk")tag:www.analyticbridge.datasciencecentral.com,2011-05-18:2004291:Topic:1019662011-05-18T20:56:43.035ZEric Torkiahttps://www.analyticbridge.datasciencecentral.com/profile/EricTorkia
Just finished preparing the second installment in our video comparison of ModelRisk, Crystal Ball, Risk Solver and @Risk. <br></br> <br></br> This series compares distribution fitting capabilities of each package.<br></br> <br></br> This should prove interesting to the discussion. Please check it out and let me know what features we should look at next.…<br></br> <br></br>
Just finished preparing the second installment in our video comparison of ModelRisk, Crystal Ball, Risk Solver and @Risk. <br/> <br/> This series compares distribution fitting capabilities of each package.<br/> <br/> This should prove interesting to the discussion. Please check it out and let me know what features we should look at next.<br/> <br/> <a target="blank" href="http://www.linkedin.com/redirect?url=http%3A%2F%2Fwww%2Ecrystalballservices%2Ecom%2FResources%2FConsultantsCornerBlog%2FEntryId%2F72%2FExcel-Simulation-Show-Down-Part-2-Distribution-Fitting%2Easpx&urlhash=kd7Y&_t=tracking_anet" rel="nofollow">http://www.crystalballservices.com/Resources/ConsultantsCornerBlog/EntryId/72/Excel-Simulation-Show-Down-Part-2-Distribution-Fitting.aspx</a> Discriminant Analysis on Categorical Variablestag:www.analyticbridge.datasciencecentral.com,2009-10-26:2004291:Topic:562332009-10-26T10:27:40.888ZArunhttps://www.analyticbridge.datasciencecentral.com/profile/Arun
I have a set of Independent Variables - both Categorical Variables and Continuous Variables. There is the predictor variable which have certain classes say C1 to Cn. The aim is to predict the category membership!<br />
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I'm facing two issues. Any discriminant procedure requires only continuous variables for prediciting. And second, logistic regression which can be used produces probability values of category membership, which does not equivalently specify the inter-class variance using distance…
I have a set of Independent Variables - both Categorical Variables and Continuous Variables. There is the predictor variable which have certain classes say C1 to Cn. The aim is to predict the category membership!<br />
<br />
I'm facing two issues. Any discriminant procedure requires only continuous variables for prediciting. And second, logistic regression which can be used produces probability values of category membership, which does not equivalently specify the inter-class variance using distance measures like a Canonical Discriminant Analysis does using %plotit macro.<br />
<br />
Hence, I've got two questions.<br />
1. If I've got mixed variables - both Continuous & Catergorical, can I still predict membership of category in the predictor variable? If yes, how?<br />
2. If the answer to the above is to use Logistic Regression or Genmod/Catmod, can I still obtain a plot of the various observations that are governed by the category in a distance measure plot to find out the between category variance/distance and hence understand visually what is the scenario of the categories.<br />
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Also, I'm not able to plot using %plotit due to the high no. of observations I've got (1.5 Mi). Do I need to consider a downscaling to bring it down to a lesser no? Or can I plot a contour to know the idea of the area coverage? Geordettes Power Analysistag:www.analyticbridge.datasciencecentral.com,2009-08-17:2004291:Topic:533502009-08-17T16:59:32.978ZZeke Davidsonhttps://www.analyticbridge.datasciencecentral.com/profile/ZekeDavidson
Hi all<br />
<br />
I am attempting to use this method to determine the sampling precision and intensity required for surveying predators in Africa. If there is anoyone who can recomend a good and SIMPLE explanation of the method it would be much appreciated. An online resource would be most apprecieated.
Hi all<br />
<br />
I am attempting to use this method to determine the sampling precision and intensity required for surveying predators in Africa. If there is anoyone who can recomend a good and SIMPLE explanation of the method it would be much appreciated. An online resource would be most apprecieated. The Secret Laws of Analytic Projectstag:www.analyticbridge.datasciencecentral.com,2008-04-01:2004291:Topic:93792008-04-01T04:53:23.741ZEdmund Freemanhttps://www.analyticbridge.datasciencecentral.com/profile/EmundFreeman
For April Fool's Day, I present the Secret Laws of Analytic Projects:<br />
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<u>The First Certainty Principle: C~ 1/K</u>; Certainty is inversely proportional to knowledge.<br />
A person who really understands data and analysis will understand all the pitfalls and limitations, and hence be constantly caveating what they say. Somebody who is simple, straightforward, and 100% certain usually has no idea what they are talking about.<br />
<br />
<u>The Second Certainty Principle: A ~ C</u>; The attractiveness of results…
For April Fool's Day, I present the Secret Laws of Analytic Projects:<br />
<br />
<u>The First Certainty Principle: C~ 1/K</u>; Certainty is inversely proportional to knowledge.<br />
A person who really understands data and analysis will understand all the pitfalls and limitations, and hence be constantly caveating what they say. Somebody who is simple, straightforward, and 100% certain usually has no idea what they are talking about.<br />
<br />
<u>The Second Certainty Principle: A ~ C</u>; The attractiveness of results is directly proportional to the certainty of the presenters.<br />
Decision-makers are attracted to certainty. Decision-makers usually have no understanding of the intricacies of data mining. What they often need is simply someone to tell them what they should do.<br />
<br />
Note that #1 and #2 together cause a lot of problems.<br />
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<u>The Time-Value Law: V ~ 1/P</u>; The value of analysis is inversely proportional to the time-pressure to produce it.<br />
If somebody want something right away, that means they want it on a whim not real need. The request that comes in at 4:00 for a meeting at 5:00 will be forgotten by 6:00. The analysis that can really effect a business has been identified through careful thought, and people are willing to wait for it. (A cheery thought for those late-night fire drills.)<br />
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<u>The First Bad Analysis Law: Bad analysis drives out good analysis</u>.<br />
Bad analysis invariably conforms to people's pre-conceived notions, so they like hearing it. It's also 100% certain in it's results, no caveats, nothing hard to understand, and usually gets produced first. This means that good analysis always has an uphill fight.<br />
<br />
<u>The Second Bad Analysis Law: Bad Analysis is worse than no analysis.</u><br />
If there is no analysis, people muddle along by common sense which usually works out OK. To really mess things up requires a common direction which requires persuasive analysis pointing in that direction. If that direction happens to be into a swamp, it doesn't help much.<br />
<br />
<a href="http://tactical-logic.blogspot.com/">Tactical Logic Blog</a>