Is Oracle Crystal Ball still relevant?

Is Oracle Crystal Ball still relevant?

Are Excel Simulation Add-Ins like Oracle Crystal Ball the right tools for decision making? This short blog deliberates on the pros and cons of Oracle Crystal Ball.
Author: Eric Torkia
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Decision Science Developper Stack

Decision Science Developper Stack

What tools should modern analysts master 3 tier design after Excel?

When it comes to having a full fledged developper stack to take your analysis to the next level, its not about tools only, but which tools are the most impactful when automating and sharing analysis for decision making or analyzing risk on projects and business operations. 

Author: Eric Torkia
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The Need For Speed 2019

The Need For Speed 2019

Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK

The Need for Speed 2019 study compares Excel Add-in based modeling using @RISK and Crystal Ball to programming environments such as R and Julia. All 3 aspects of speed are covered [time-to-solution, time-to-answer and processing speed] in addition to accuracy and precision.
Author: Eric Torkia
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Article rating: 3.8
Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

How I Learned to Think of Business as a Scientific Experiment

Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
Author: Robert Brown
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Article rating: 2.5
Bayesian Reasoning using R

Bayesian Reasoning using R

Gender Inference from a Specimen Measurement

Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process for doing this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.
Author: Robert Brown
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Oracle Crystal Ball Spreadsheet Functions For Use in Microsoft Excel Models

May 20 2014

Oracle Crystal Ball has a complete set of functions that allows a modeler to extract information from both inputs (assumptions) and outputs (forecast). Used the right way, these special Crystal Ball functions can enable a whole new level of analytics that can feed other models (or subcomponents of the major model).

Understanding these is a must for anybody who is looking to use the developer kit.

The table below lists Crystal Ball functions that are also available as spreadsheet functions for use in Microsoft Excel spreadsheet models.

To use these functions with Crystal Ball loaded:

  1. Select an empty spreadsheet cell and select Insert, and then Function. In Microsoft Excel 2007 or later, select Formulas, and then Insert Function.

  2. Select the Crystal Ball category in the category list box.

  3. Find the function in the function list. Select it and click OK. Always select the function ending in FN, since the other runs significantly slower in most cases.

  4. In the Function Arguments dialog, enter any required arguments and click OK.

Things to remember

  1. The requested value is displayed in the cell with the function only after running either a trial or a full simulation. Otherwise you will get a #NUM error
  2. CB.GetForeStatFN only pulls N-1 trials during a recalculation for either a single step or a full simulation, but will reflect full trials at the end of the single step or full simulation since a final recalculation is always performed.
  3. If you forget to load CB, your CB.Functions will produce the #NAME error
  4. Be sure to save the workbook with the new function while Crystal Ball is open.

Crystal Ball Functions For Use in Microsoft Excel Models

Name

Description

CB.GetAssumFN

Retrieves information for a specific assumption cell.

CB.GetAssumPercentFN

Returns the value corresponding to a percentile for an assumption cell.

CB.GetAssumStatFN

Calculates the specified statistic for the assumption in the specified cell.

CB.GetCertaintyFN

Returns the certainty level of achieving a forecast value at or smaller than a specific threshold.

CB.GetForeDataFN

Returns the value for the given trial for a specific forecast cell.

CB.GetForePercentFN

Returns the value corresponding to a percentile for a specific forecast

CB.GetForeStatFN

Returns statistic for a specific forecast cell.

CB.GetRunPrefsFN

Returns a Run Preference setting.

CB.IterationsFN

Returns the number of trials run in a simulation.

CB.Spearman

Calculates Spearman rank correlations between pairs of values.


Index Parameters

To see a complete list of index parameters compatible with all the CB Functions, click here

 

 

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