@RISK (pronounced “at risk”) is an add-in to Microsoft Excel that lets you analyze risk using Monte Carlo simulation. @RISK shows you virtually all possible outcomes for any situation—and tells you how likely they are to occur. This means you can judge which risks to take on and which ones to avoid—critical insight in today.
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- When you incorporate variability while the expected profit is positive, can find that there is a high probability of the profit being really a loss. @risk is a software you can use to create this type of models. It is basically an Excel add-in that will allow you to transform your Excel model into a much more versatile one.
- Reference just the lookup values you are interested in. This style of formula will return a dynamic array, but does not work with Excel tables. =VLOOKUP ( A2:A7,A:C,2,FALSE) 2. Reference just the value on the same row, and then copy the formula down. This traditional formula style works in tables, but will not return a dynamic array.
- Integrated range of quantitative risk analysis and management software products, supported by consulting and training services. Understand, analyze, control and monitor the risks that drive your business. Integrate web-enabled corporate risk registers with project schedule risk and analysis in Microsoft Excel using Monte Carlo Simulation.
First, suppose that bikers arrive to Station 1 (station id =1) according to a nonstationary Poisson process. Complete the below table (enclosed picture file) of arrival rates at Station 1.
Hint: Create a timevalue column. Excel ‘Timevalue (time text)’ returns the decimal number of the time represented by a text string. The decimal number is a value ranging from 0 (zero) to 0.99988426, representing the times from 0:00:00 (12:00:00 AM) to 23:59:59 (11:59:59 P.M.). For example, TIMEVALUE(“1-June-2017 6:35 AM”) = 0.2743. Use the histogram of the timevalue to compute the arrival rates.
Second, assign a destination (end station id) to each arrival at Station 1 by using a discrete probability distribution of this form: DISC(p1,1, p2,2,…p12,12)
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Determine the value of p1, p2…p12 using the relative frequency bar chart of ‘end station id’. Copy and paste your Excel worksheet.
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Finally, build a probabilistic model for the trip duration from Station 1 to Station i, with i=1,2,…12
i. First step is to remove outliers. If a bike has been rent out for more than 24 hours at a time, it is considered lost or stolen. Remove any trips longer than 24 hours (86,400 seconds). You can also remove more outliers if you think it is necessary.
ii. Considering the scatterplot below (Word document) and the location of the 12 stations, determine the probability distribution of the trip duration from Station 1 to Station i, with i =1, 2,…12.
You can use Arena Input Analyzer, @RISK, or any other statistical software you like. You may combine some (or even all) end stations to determine input probability distributions. Copy and paste your Arena Input analyzer results or Excel worksheet.
Copy and paste your Excel worksheet.
First, suppose that bikers arrive to Station 1 (station id =1) according to a nonstationary Poisson process. Complete the below table (enclosed picture file) of arrival rates at Station 1.
Hint: Create a timevalue column. Excel ‘Timevalue (time text)’ returns the decimal number of the time represented by a text string. The decimal number is a value ranging from 0 (zero) to 0.99988426, representing the times from 0:00:00 (12:00:00 AM) to 23:59:59 (11:59:59 P.M.). For example, TIMEVALUE(“1-June-2017 6:35 AM”) = 0.2743. Use the histogram of the timevalue to compute the arrival rates.
Second, assign a destination (end station id) to each arrival at Station 1 by using a discrete probability distribution of this form: DISC(p1,1, p2,2,…p12,12)
Determine the value of p1, p2…p12 using the relative frequency bar chart of ‘end station id’. Copy and paste your Excel worksheet.
Finally, build a probabilistic model for the trip duration from Station 1 to Station i, with i=1,2,…12
i. First step is to remove outliers. If a bike has been rent out for more than 24 hours at a time, it is considered lost or stolen. Remove any trips longer than 24 hours (86,400 seconds). You can also remove more outliers if you think it is necessary.
ii. Considering the scatterplot below (Word document) and the location of the 12 stations, determine the probability distribution of the trip duration from Station 1 to Station i, with i =1, 2,…12.
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You can use Arena Input Analyzer, @RISK, or any other statistical software you like. You may combine some (or even all) end stations to determine input probability distributions. Copy and paste your Arena Input analyzer results or Excel worksheet.
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Copy and paste your Excel worksheet.