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[]
[null,null,null,null,null,null,null,null,null,null,null,null,"Example and Worksheet Checklist"]
[null,null,null,null,null,null,"\t\nHow to Measure Anything in Project Management"]
[]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,"Simple Monte Carlo Example"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,"The Probability of Project Cancellation Using the Beta Distribution"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,"Estimating a Population Mean"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,"Project Planner"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,"Project Risk"]
[]
[]
[null,null,null,null,null,"Hubbard Decision Research"]
[null,null,null,null,null,"2S410 Canterbury Ct"]
[null,null,null,null,null,"Glen Ellyn, IL 60137"]
[null,null,null,null,null,"www.hubbardresearch.com"]

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[null,"Check SIPMath for a 1,000 Trial Interactive Simulation",null,null,null,null,{"$f":"IF(SIPmath,\"Trial \"&BA1,\"\")","v":"Trial 7"},null,null," Period Highlight:",60,"Percentile",null,null,null,"Plan Duration",null,null,null,null,null,"Actual Start",null,null,null,null,"% Complete",null,null,null,null,"Actual (beyond plan)",null,null,null,null,null,null,null,"% Complete (beyond plan)"]
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[null,"Project Complete",null,null,null,null,{"$f":"I14","v":19},1,{"$f":"G15","v":19},null,null,{"$f":"IF(SIPmath,INT( IF( $L$2=\"Average\", AVERAGE(FinishProject), _xlfn.PERCENTILE.EXC(FinishProject, $L$1 ))),\"\")","v":24}]
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[null,null,null,null,null,{"$f":"IF(SIPmath,Sheet1!D18,\"\")","v":"%ile"},{"$f":"IF(SIPmath,Sheet1!E18,\"\")","v":0.9}]
[null,null,null,null,null,{"$f":"IF(SIPmath,Sheet1!D19,\"\")","v":"% Value"},{"$f":"IF(SIPmath,Sheet1!E19,\"\")","v":23},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"DRAFT"]
[null,null,null,null,null,{"$f":"IF(SIPmath,Sheet1!D20,\"\")","v":"Target Completion Period"},{"$f":"IF(SIPmath,Sheet1!E20,\"\")","v":20},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Not for Distribution"]
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{"sheet":"Risk Register","index":3,"n_rows":29,"n_cols":17,"rows_kept":29,"cut":false,"hidden":false,"merged":[[6,4,6,5],[6,10,6,11],[7,4,7,5],[1,1,3,1],[1,2,1,3],[1,4,3,13],[2,2,2,3],[3,2,3,3],[5,1,5,8],[5,9,5,15]]}
["One-For-One Substitution Model (Chapter 4 and Chapter 11)",null,null,null,null,null,null,null,"← Uncollapse for Control Input",null,null,null,"← Uncollapse for HDR PRNG",null,null,null,"← Uncollapse for Residual Loss and ROC"]
[null,null,"Contact HDR to develop custom quantitative methods for your firm.",null,"Introduction: The One-For-One Substitution Model shows how we can replace the basic heat map using purely quantitative methods.  Like the heat map, this approach relies entirely on the subjective estimates of likelihood and impact from experts.  Unlike the heat map, it uses mathematically unambiguous probabilistic expressions of likelihood and impact which can be used in simulations where risks can be properly \"added up\" to compute risks for a larger portfolio. The first tab requires user input. Simply describe the risk or event that is being considered in column B under \"Risk Name\". Then, estimate the probability that the event will happen over a 1 year period and a 90% confidence interval for the impact of the event, if it did occur. To view the results of the simulation, see the \"Loss Exceedance Curve\" tab. Make sure that calculation options are set to \"Automatic Except for Data Tables\" (Formulas>Calculation Options)."]
[null,null,"www.hubbardresearch.com"]
[null,null,"info@hubbardresearch.com"]
[null,"Trial Slider Bar>>",null,"Trial ID:",{"$f":"TrialID","v":184}]
[null,"Input Values",null,null,null,null,null,null,null,"Output Values"]
[null,"The \"Risk Name\" should be a short, unique identifier for the risk being considered.","Enter the \"Risk Classification\" that best matches. If the category needs explanation  please use the \"Notes\" column.","Estimate the likelihood of the event occurring at least one time in a year.","Estimate the total impact of the event, given that it occurred, with a 90% confidence interval. That is, estimate a lower bound and upper bound such that you believe there is a 90% chance the actual impact will be between the bounds. Note that the lower bound must be greater than 0.",null,"For the control being applied to the risk, provide an estimate of the cost of implementing the control. ","Estimate the effectiveness of the controls being applied as a percent reduction in the likelihood of the event occurring. For example, if the risk has a 10% chance per year of occurring and a control is applied with 25% effectiveness, the mitigated likelihood of the event occurring would be 7.5%.","Record any relevant notes about the risk, such as assumptions made when estimating likelihood and impact, or the controls, such as what controls are included in the total cost.","The expected value, or probability-weighted average, of the loss due to a risk. A lognormal distribution is assumed for impact.","Pseudo-random numbers between 0 and 1 are generated for each variable using a unique variable ID. These numbers are then used in other formulas to generate values for each scenario.",null,"A random draw against the likelihood of the event determines if the event occurs. If it does occur, total losses are randomly drawn from the impact ranges. If the event does not occur, there is $0 simulated loss.","The expected value, or probability-weighted average, of the loss after applying controls. A lognormal distribution is assumed for impact.","The loss simulated in this trial, if there is one, after the implementation of the control.","Return on control is computed by comparing the monetized risk reduction (change in expected loss) to the total cost of controls."]
["Examples of Enterprise Risks",null,null,null,"90% Confidence Interval of Impact",null,null,null,null,null,100000,100001]
["Risk ID","Risk Name","Risk Classification","Probability of a Loss Over 1 Year","Lower Bound","Upper Bound","Total Cost of Controls","Control Effectiveness","Notes","Expected Inherent Loss","Probability Rand","Impact Rand","Simulated Inherent Loss","Expected  Residual Loss","Simulated Residual Loss","Return on Control"]
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[{"$f":"ROW($B11)-ROW($B$9)","v":2},"Failure of a major project","Project risk",0.15,2000000,5000000,500000,0.25,null,{"$f":"IFERROR(EXP(((LN($F11)+LN($E11))/2)+((LN($F11)-LN($E11))/3.28971)^2/2)*$D11, 0)","v":493102.95303230168},{"$f":" ((MOD((MOD(MOD(999999999999989,MOD($E$5*2499997+$A11*1800451+K$8*2000371,7450589)*4658+7450581)*383,99991)*7440893+MOD(MOD(999999999999989,MOD($E$5*2246527+$A11*2399993+K$8*2100869,7450987)*7580+7560584)*17669,7440893))*1343,4294967296))+0.5)/2^32","v":0.2194150771247223},0.7938396082026884,{"$f":"IFERROR(IF($K11<$D11,_xlfn.LOGNORM.INV($L11,(LN($F11)+LN($E11))/2, (LN($F11)-LN($E11))/3.29),0), 0)","v":0},{"$f":"IFERROR((1-$H11)*$J11, $J11)","v":369827.21477422625},{"$f":"IFERROR(IF($K11<(1-$H11)*$D11,_xlfn.LOGNORM.INV($L11,(LN($F11)+LN($E11))/2, (LN($F11)-LN($E11))/3.29),0), 0)","v":0},{"$f":"IFERROR(($J11-$N11)/$G11-1, 0)","v":-0.75344852348384916}]
[3,"Increased cost of capital","Interest rate risk",0.35,150000,300000,25000,0.5,null,75912.726375788377,0.58520069776568562,0.43454430589918047,0,37956.363187894189,0,0.5182545275157675]
[4,"Industrial accident","Legal risk",0.1,200000,500000,10000,0.1,null,32873.530202153474,0.93879144603852183,0.54388105717953295,0,29586.177181938128,0,-0.67126469797846555]
[5,"Exposure to a large lawsuit","Legal risk",0.05,500000,2000000,15000,0.3,null,54642.574904049863,0.52745539380703121,0.24443393049295992,0,38249.802432834898,0,9.2851498080997574E-2]
[6,"Loss of major supplier","Operational risk",0.12,500000,10000000,250000,0.7,null,406198.00826613855,0.75871733680833131,0.90458592341747135,0,121859.40247984158,0,0.13735442314518798]
[7,"Data breach","Cyber security risk",3.5000000000000003E-2,1000000,25000000,150000,0.85,null,282445.53794623696,0.24924676667433232,0.25922412297222763,0,42366.830691935553,0,0.6005247150286761]
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[10,null,null,null,null,null,null,null,null,0,0.42005898559000343,0.28983895771671087,0,0,0,0]
[11,null,null,null,null,null,null,null,null,0,0.90623997605871409,0.34309464029502124,0,0,0,0]
[12,null,null,null,null,null,null,null,null,0,0.87493195699062198,0.73924195242580026,0,0,0,0]
[13,null,null,null,null,null,null,null,null,0,4.5124876196496189E-2,0.29296369303483516,0,0,0,0]
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[15,null,null,null,null,null,null,null,null,0,0.36134821700397879,6.2552224728278816E-2,0,0,0,0]
[16,null,null,null,null,null,null,null,null,0,2.9774027294479311E-2,0.48822156840469688,0,0,0,0]
[17,null,null,null,null,null,null,null,null,0,0.34003326192032546,7.4788722093217075E-2,0,0,0,0]
[18,null,null,null,null,null,null,null,null,0,0.64732184272725135,4.6196260140277445E-2,0,0,0,0]
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["One-For-One Substitution Model",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Uncollapse for Residual Loss Data Table -->",null,null,null,null,"Uncollapse for Residual Loss Histogram -->"]
[null,null,null,null,"Contact HDR to develop custom quantitative methods for your firm.",null,null,null,null,null,"Introduction: The Loss Exceedance Curve tab shows the results of the Monte Carlo simulation. Excel's What-if Data Tables are used to compute 1000 random trials based on the Risk Estimates provided and a histogram counts the number of times the total losses were within specified bins. Expand columns S-U and X-Y to include the residual risk curve on the Loss Exceedance Curve chart. Make sure that calculations are set to \"Automatic Except for Data Tables\" (Formulas>Calculation Options) so that results update."]
[null,null,null,null,"www.hubbardresearch.com"]
[null,null,null,null,"info@hubbardresearch.com"]
[null,"Enter a Trial ID between 1 and 1000 to see the details of that trial on the Risk Estimates tab.",null,null,"Trial ID:",184]
[null,"The loss exceedance curve displays the probability of exceeding a given loss in one year compared to your organization's willingness to accept that magnitude of a loss (your risk tolerance). Below the chart, the user can enter a loss in the gold cell and the probability of exceeding that loss will be shown.",null,null,null,null,null,null,null,null,"Your organization's risk tolerance can be drawn by estimating at least 3 points on this curve. One way to start with this is to leave the Acceptable Prob. Loss Exceeded where it is and adjust the Loss amounts to match your tolerance. Are you willing to accept a 99% chance of losing $1MM or more in a given year? If not, what about $500,000 or more?",null,null,null,null,"This data table calculates the inherent loss that occurs in each trial. A total of 1000 trials are calculated. Note that these are the losses that occur with no controls present.",null,null,"This data table calculates the residual loss that occurs in each trial. A total of 1000 trials are calculated. Note that these are the losses that occur after controls are applied.",null,null,"This table calculates the probability of inherent and residual (if controls are included) losses in pre-defined intervals (e.g., the probability of a loss between $500,000 and $999,999). These values are used to plot the loss exceedance curve."]
[null,"Figure 3.3 \"Inherent Risk, Residual Risk and Risk Tolerance\"",null,null,null,null,null,null,null,null,"Loss Exceedance Tolerance",null,null,null,null,"Table 3.3 \"Excel Data Table Showing Scenarios of  Losses\"",null,null,"Total Simulated Residual Losses",null,null,"Table 3.4 \"Histogram for a Loss Exceedance Curve\""]
[null,"To update the Loss Exceedance Curve, run the model by calculating the Excel sheet. This can be done by pressing the F9 key or \"Calculate Now\" under the Formulas Ribbon.",null,null,null,null,null,null,null,null,null,null,"Acceptable P Loss Exceeded",null,null,"Trial","Loss",null,"Trial","Loss"]
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{"sheet":"Simple MC WS","index":5,"n_rows":159,"n_cols":1072,"rows_kept":159,"cut":false,"hidden":false,"merged":[[49,2,49,8],[62,0,62,1],[1,0,4,8],[8,20,12,20],[28,0,29,8],[30,2,30,5],[30,6,30,8],[48,0,48,1],[48,2,48,8]]}
["Worksheet: Simple Monte Carlo ",null,null,null,null,"More calculations to the right.  Un-collapse columns. >>>",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Excel What-If Data Table to Compute Monte Carlo Simulation"]
["This worksheet provides an example of a simple Monte Carlo Model. In this example we model a project that would reduce potential labor costs for an upfront investment. Describe the Variables you want to use in your simulation here.  Each distribution has a \"Shape\" (aka, probability distribution) described in the table below.  Variables declared on this page can be used as random values on the Decision Model Summary table.  Always recalculate by pressing the \"Recalculate\" button in cell I6. after making changes.  Detailed calculations are grouped under hidden columns. Click on \"+\" signs in column headings to ungroup hidden columns. Below one will also find the model results as well as inputs for risk tolerance. "]
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["Decision Model Summary",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Normal"]
["Use this space to describe your decision model or cash flow. ",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Binary"]
[null,null,null,"Years",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Uniform"]
[null,null,0,1,2,3,4,5,null,null,null,null,null,null,null,null,null,null,null,null,null,"Lognorm"]
["Costs",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Triangular"]
["Initial Development Costs",null,{"$f":"-DevC","v":-5998.9440488325145},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Constant"]
["Cancellation? (1=cancelled)",null,{"$f":"Canc","v":0}]
[]
["Benefits"]
["Labor savings",null,null,{"$f":"LoadLab*Reduc*(1-$C21)","v":5418.0444293797937},{"$f":"LoadLab*Reduc*(1-$C21)","v":5418.0444293797937},{"$f":"LoadLab*Reduc*(1-$C21)","v":5418.0444293797937},{"$f":"LoadLab*Reduc*(1-$C21)","v":5418.0444293797937},{"$f":"LoadLab*Reduc*(1-$C21)","v":5418.0444293797937}]
[]
["Net Cash Flow",null,{"$f":"C24+C20","v":-5998.9440488325145},{"$f":"D24+D20","v":5418.0444293797937},{"$f":"E24+E20","v":5418.0444293797937},5418.0444293797937,5418.0444293797937,5418.0444293797937]
[]
["Results Summary"]
["This section shows the output of the simulation in two charts: a histogram of NPVs and a loss exceedance curve.  Certain Monetary Equivalent (CME) is also computed. Enter chart min and increment size to modify how the histogram is displayed."]
[]
[null,null,"Histogram of Simulated NPV",null,null,null,"Loss Exceedance Curve"]
[]
["Average NPV",{"$f":"IFERROR(AVERAGE(X7:AMI7), 0)","v":4858.0525273455551}]
["Average Loss",{"$f":"IFERROR(SUMIF(X7:AMI7,\"<0\")/COUNTIF(X7:AMI7,\"<0\"), 0)","v":-3573.8198529589699}]
["Base EOL",{"$f":"IFERROR(IF(B38=1,-AVERAGEIF(X7:AMI7,\"<=0\")*B36,AVERAGEIF(X7:AMI7,\">=0\")*(1-B36)),0)","v":814.83092647464514}]
["P(NPV<0)",{"$f":"COUNTIF(X7:AMI7,\"<0\")/1000","v":0.22800000000000001}]
["CME",{"$f":"IFERROR(-B51*LN(1-AVERAGE(X8:AMI8)), 0)","v":716.10598073401422}]
["Default Action (1=Invest)",{"$f":"IF(B33>0,1,0)","v":1}]
["Sum of Individual EVPIs",{"$f":"SUM(J9:J13)","v":605.28063087939222}]
[]
["Chart Min",-20000]
["Increment",1000]
["Chart Max",{"$f":"B41+B42*30","v":10000}]
[]
["The probability of losing more than",-4000]
["is",{"$f":"_xlfn.PERCENTRANK.INC(X7:AMI7,B45,)","v":7.9000000000000001E-2}]
[]
[]
["Constant Absolute Risk Aversion",null,"Data for Graphs"]
[null,null,"The table below ais used to construct the above histogram and loss exceedance tolerance curve. The size of the bins can be changed using the above yellow cells"]
["X",5000,"Increment Midpoint","Increment Lower Bound","Increment Upper Bound","Count less than Upper Bound","Number within Increment","Loss Exceedance Probability","Loss Exceeded"]
["Award","Probability",{"$f":"B41","v":-20000},{"$f":"C52-B$42/2","v":-20500},{"$f":"C52+B$42/2","v":-19500},{"$f":"COUNTIF(X$7:AMI$7,\"<=\"&E52)","v":0},{"$f":"F52","v":0},{"$f":"COUNTIF(X$7:AMI$7,\"<\"&C52)/1000","v":0},{"$f":"-C52","v":20000}]
[2000,0.25,{"$f":"C52+B$42","v":-19000},{"$f":"C53-B$42/2","v":-19500},{"$f":"C53+B$42/2","v":-18500},{"$f":"COUNTIF(X$7:AMI$7,\"<=\"&E53)","v":0},{"$f":"F53-F52","v":0},{"$f":"COUNTIF(X$7:AMI$7,\"<\"&C53)/1000","v":0},19000]
["Utility","Certain Monetary Equivalent",{"$f":"C53+B$42","v":-18000},{"$f":"C54-B$42/2","v":-18500},{"$f":"C54+B$42/2","v":-17500},{"$f":"COUNTIF(X$7:AMI$7,\"<=\"&E54)","v":0},0,{"$f":"COUNTIF(X$7:AMI$7,\"<\"&C54)/1000","v":0},18000]
[{"$f":"1-EXP(-A53/B51)","v":0.32967995396436067},{"$f":"-B51*LN(1-A55*B53)","v":430.07748425686401},-17000,-17500,-16500,0,0,0,17000]
["Flip a coin…",null,-16000,-16500,-15500,0,0,0,16000]
[null,null,-15000,-15500,-14500,0,0,0,15000]
[null,null,-14000,-14500,-13500,2,2,0,14000]
[null,null,-13000,-13500,-12500,5,3,2E-3,13000]
[null,null,-12000,-12500,-11500,7,2,6.0000000000000001E-3,12000]
["Win $X","Lose $X/2",-11000,-11500,-10500,11,4,8.9999999999999993E-3,11000]
["Risk Tolerance",null,-10000,-10500,-9500,18,7,1.4999999999999999E-2,10000]
["To use the stated loss exceedance tolerance curve with the loss exceedance curve above fill in a set of loss and probabilities such that the loss at each probability is barely acceptable.",null,-9000,-9500,-8500,19,1,1.7999999999999999E-2,9000]
["Loss (per year)","Acceptable P Loss Exceeded",-8000,-8500,-7500,29,10,2.3E-2,8000]
[1000,0.1,-7000,-7500,-6500,37,8,3.1E-2,7000]
[2000,0.05,-6000,-6500,-5500,53,16,4.3999999999999997E-2,6000]
[10000,0.01,-5000,-5500,-4500,71,18,6.0999999999999999E-2,5000]
[50000,1E-4,-4000,-4500,-3500,93,22,0.08,4000]
[null,null,-3000,-3500,-2500,119,26,0.104,3000]
[null,null,-2000,-2500,-1500,158,39,0.13800000000000001,2000]
[null,null,-1000,-1500,-500,197,39,0.17299999999999999,1000]
[null,null,0,-500,500,255,58,0.22800000000000001,0]
[null,null,1000,500,1500,314,59,0.28299999999999997,-1000]
[null,null,2000,1500,2500,380,66,0.35299999999999998,-2000]
[null,null,3000,2500,3500,445,65,0.41499999999999998,-3000]
[null,null,4000,3500,4500,516,71,0.48899999999999999,-4000]
[null,null,5000,4500,5500,566,50,0.54100000000000004,-5000]
[null,null,6000,5500,6500,625,59,0.60099999999999998,-6000]
[null,null,7000,6500,7500,675,50,0.64400000000000002,-7000]
[null,null,8000,7500,8500,727,52,0.70499999999999996,-8000]
[null,null,9000,8500,9500,768,41,0.746,-9000]
[null,null,10000,9500,10500,799,31,0.78100000000000003,-10000]
[null,null,11000,10500,11500,833,34,0.81699999999999995,-11000]
[null,null,12000,11500,12500,856,23,0.84699999999999998,-12000]
[null,null,13000,12500,13500,888,32,0.876,-13000]
[null,null,14000,13500,14500,910,22,0.89900000000000002,-14000]
[null,null,15000,14500,15500,934,24,0.92,-15000]
[null,null,16000,15500,16500,949,15,0.94199999999999995,-16000]
[null,null,17000,16500,17500,966,17,0.96199999999999997,-17000]
[null,null,18000,17500,18500,974,8,0.97199999999999998,-18000]
[null,null,19000,18500,19500,982,8,0.97899999999999998,-19000]
[null,null,20000,19500,20500,988,6,0.98699999999999999,-20000]
[null,null,21000,20500,21500,991,3,0.98899999999999999,-21000]
[null,null,22000,21500,22500,993,2,0.99199999999999999,-22000]
[null,null,23000,22500,23500,995,2,0.99399999999999999,-23000]
[null,null,24000,23500,24500,997,2,0.995,-24000]
[null,null,25000,24500,25500,998,1,0.997,-25000]
[null,null,26000,25500,26500,998,0,0.998,-26000]
[null,null,27000,26500,27500,998,0,0.998,-27000]
[null,null,28000,27500,28500,999,1,0.999,-28000]
[null,null,29000,28500,29500,999,0,0.999,-29000]
[null,null,30000,29500,30500,999,0,0.999,-30000]
[null,null,31000,30500,31500,999,0,0.999,-31000]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IFERROR(IF(ISBLANK(Variables[[#This Row],[Simulated Value]]), \"\", Variables[[#This Row],[Simulated Value]]), \"\")","v":""}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IFERROR(IF(ISBLANK(Variables[[#This Row],[Simulated Value]]), \"\", Variables[[#This Row],[Simulated Value]]), \"\")","v":""}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IFERROR(IF(ISBLANK(Variables[[#This Row],[Simulated Value]]), \"\", Variables[[#This Row],[Simulated Value]]), \"\")","v":""}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IFERROR(IF(ISBLANK(Variables[[#This Row],[Simulated Value]]), \"\", Variables[[#This Row],[Simulated Value]]), \"\")","v":""}]

{"sheet":"Beta Distribution","index":6,"n_rows":1019,"n_cols":6,"rows_kept":1019,"cut":false,"hidden":false,"merged":[[0,0,0,5],[1,0,1,5],[3,4,3,5],[18,0,18,2],[19,4,19,5]]}
["The Beta Distribution for Project Cancellation"]
["This spreadsheet computes how uncertainty about an estimate of a \"population proportion\" (e.g., \"what percentage of customers want X?\").  It uses a method called a beta distrubtion, which allows for establishing a \"prior\" uncertainty which is updated with new data.  Enter values in the yellow \"prior\" fields to set the initial estimate.  Enter \"hits\" and \"misses\" from a random sample in the yellow \"Observations\" fields.  We are using it here as a method of estimating cancellation frequency of projects given a prior of various project cancellation studies."]
[]
[null,"Prior","w/Data",null,"Observations"]
["alpha",1.5,{"$f":"B5+F5","v":3.5},null,"\"Hits\" (Cancellations)",2]
["beta",90,{"$f":"B6+F6","v":522},null,"\"Misses\"",{"$f":"F7-F5","v":432}]
[null,null,null,null,"Total Observations of Project-Months",434]
["Monthly Rate"]
["P5",{"$f":"_xlfn.BETA.INV(0.05,B5,B6)","v":1.9474017685780299E-3},{"$f":"_xlfn.BETA.INV(0.05,C5,C6)","v":2.0689075123389643E-3}]
["Mode",{"$f":"_xlfn.BETA.INV(0.5,B5,B6)","v":1.3022476189877263E-2},{"$f":"_xlfn.BETA.INV(0.5,C5,C6)","v":6.0455121254358261E-3}]
["Mean",{"$f":"B5/(B5+B6)","v":1.6393442622950821E-2},{"$f":"C5/(C5+C6)","v":6.6603235014272124E-3}]
["P95",{"$f":"_xlfn.BETA.INV(0.95,B5,B6)","v":4.2371727626918876E-2},{"$f":"_xlfn.BETA.INV(0.95,C5,C6)","v":1.3352200406777093E-2}]
["Annualized"]
["P5",{"$f":"1-(1-B9)^12","v":2.3120142225677265E-2},{"$f":"1-(1-C9)^12","v":2.4546324396859776E-2}]
["Mode",{"$f":"1-(1-B10)^12","v":0.14554901956280863},7.018191792759354E-2]
["Mean",0.1799185574021831,7.706016354777645E-2]
["P95",0.40521019912946632,0.14896813811470222]
[]
["Chart Distribution Calculations"]
["Population Proportion","Prior","w/Data",null,"Calcs for Chart Boundaries"]
[1E-3,{"$f":"_xlfn.BETA.DIST(A21+A$21/2,B$5,B$6,1)-_xlfn.BETA.DIST(A21-A$21/2,B$5,B$6,1)","v":2.7582882757161954E-2},{"$f":"_xlfn.BETA.DIST(A21+A$21/2,C$5,C$6,1)-_xlfn.BETA.DIST(A21-A$21/2,C$5,C$6,1)","v":1.960413395533173E-2},null,{"$f":"E22","v":1.9474017685780299E-3},0]
[{"$f":"A21+A$21","v":2E-3},3.6052657445403362E-2,6.1920674263225214E-2,null,{"$f":"B9","v":1.9474017685780299E-3},{"$f":"VLOOKUP(E22,A$21:B$1019,2)","v":2.7582882757161954E-2}]
[{"$f":"A22+A$21","v":3.0000000000000001E-3},4.0471359394844614E-2,0.10083511220259009,null,{"$f":"E24","v":4.2371727626918876E-2},0]
[4.0000000000000001E-3,4.2774684737044336E-2,0.12289104199185896,null,{"$f":"B12","v":4.2371727626918876E-2},{"$f":"VLOOKUP(E24,A$21:B$1019,2)","v":4.35400061907254E-3}]
[5.0000000000000001E-3,4.3752057628526936E-2,0.12745044584793846,null,{"$f":"E26","v":2.0689075123389643E-3},0]
[6.0000000000000001E-3,4.383618918991522E-2,0.11926880662577166,null,{"$f":"C9","v":2.0689075123389643E-3},{"$f":"VLOOKUP(E26,A$21:C$1019,3)","v":6.1920674263225214E-2}]
[7.0000000000000001E-3,4.3298540283022685E-2,0.10393895470971781,null,{"$f":"E28","v":1.3352200406777093E-2},0]
[8.0000000000000002E-3,4.2322929678597521E-2,8.5962353694449045E-2,null,{"$f":"C12","v":1.3352200406777093E-2},{"$f":"VLOOKUP(E28,A$21:C$1019,3)","v":2.0881173369039052E-2}]
[9.0000000000000011E-3,4.1039868834711668E-2,6.8300631791179289E-2]
[1.0000000000000002E-2,3.9544911956031215E-2,5.2572124528210584E-2]
[1.1000000000000003E-2,3.7909442383897374E-2,3.9435703073807615E-2]
[1.2000000000000004E-2,3.618747441521547E-2,2.895601243842616E-2]
[1.3000000000000005E-2,3.4420178810997237E-2,2.0881173369039052E-2]
[1.4000000000000005E-2,3.2639022575707499E-2,1.4827449381107805E-2]
[1.5000000000000006E-2,3.0868020911137273E-2,1.0388857241593552E-2]
[1.6000000000000007E-2,2.9125396015206251E-2,7.1941536271367124E-3]
[1.7000000000000008E-2,2.7424825572306188E-2,4.9305078959798188E-3]
[1.8000000000000009E-2,2.5776399024575625E-2,3.3480484587105064E-3]
[1.900000000000001E-2,2.418736054559445E-2,2.2546994913286156E-3]
[2.0000000000000011E-2,2.2662693042513005E-2,1.5070515789161743E-3]
[2.1000000000000012E-2,2.1205581554831099E-2,1.0004721516010395E-3]
[2.2000000000000013E-2,1.9817783762499208E-2,6.6004411702591526E-4]
[2.3000000000000013E-2,1.8499928017426948E-2,4.3296390607761026E-4]
[2.4000000000000014E-2,1.7251754198552516E-2,2.8250929012574311E-4]
[2.5000000000000015E-2,1.6072309033881016E-2,1.8343579285473588E-4]
[2.6000000000000016E-2,1.4960104870005653E-2,1.1856428264334085E-4]
[2.7000000000000017E-2,1.3913248898365871E-2,7.6308587808826545E-5]
[2.8000000000000018E-2,1.2929548366442667E-2,4.8916974881185737E-5]
[2.9000000000000019E-2,1.2006596174380268E-2,3.1240424360978736E-5]
[3.000000000000002E-2,1.1141840388397384E-2,1.9881046545378034E-5]
[3.1000000000000021E-2,1.0332640525189563E-2,1.2609883679792766E-5]
[3.2000000000000021E-2,9.5763129286694504E-3,7.9727632225079503E-6]
[3.3000000000000022E-2,8.8701671373720981E-3,5.0257619801374886E-6]
[3.4000000000000023E-2,8.2115348022904211E-3,3.159026626287087E-6]
[3.5000000000000024E-2,7.5977924419969867E-3,1.9802521677991791E-6]
[3.6000000000000025E-2,7.026379100466551E-3,1.2381012464146579E-6]
[3.7000000000000026E-2,6.4948097922840065E-3,7.721621615441876E-7]
[3.8000000000000027E-2,6.0006854716555535E-3,4.8042090572408824E-7]
[3.9000000000000028E-2,5.5417001394185705E-3,2.982202573775794E-7]
[4.0000000000000029E-2,5.1156456010874596E-3,1.847106498864548E-7]
[4.1000000000000029E-2,4.7204143049374148E-3,1.1416188738788691E-7]
[4.200000000000003E-2,4.35400061907254E-3,7.0413681219072544E-8]
[4.3000000000000031E-2,4.0145008478960076E-3,4.3344065003836363E-8]
[4.4000000000000032E-2,3.7001122393361952E-3,2.6629731308958071E-8]
[4.5000000000000033E-2,3.4091311930063428E-3,1.6330309993151104E-8]
[4.6000000000000034E-2,3.1399508448304303E-3,9.9962425004207489E-9]
[4.7000000000000035E-2,2.8910581744995278E-3,6.1082450208971295E-9]
[4.8000000000000036E-2,2.6610307575197778E-3,3.7261007612698904E-9]
[4.9000000000000037E-2,2.4485332628599865E-3,2.2691906131910855E-9]
[5.0000000000000037E-2,2.2523137796778281E-3,1.3796985776082238E-9]
[5.1000000000000038E-2,2.0712000418177112E-3,8.3755169466570578E-10]
[5.2000000000000039E-2,1.9040956062732484E-3,5.0765625037030304E-10]
[5.300000000000004E-2,1.7499760312739143E-3,3.0723734667503777E-10]
[5.4000000000000041E-2,1.6078850907691455E-3,1.8566870263470037E-10]
[5.5000000000000042E-2,1.4769310545988956E-3,1.1204115413221416E-10]
[5.6000000000000043E-2,1.3562830573496942E-3,6.7515548707319795E-11]
[5.7000000000000044E-2,1.2451675736199208E-3,4.0628389541552679E-11]
[5.8000000000000045E-2,1.1428650129962081E-3,2.4415358623741668E-11]
[5.9000000000000045E-2,1.0487064443601701E-3,1.4652945523607741E-11]
[6.0000000000000046E-2,9.6207045608465158E-4,8.7825302585997633E-12]
[6.1000000000000047E-2,8.8238015613728571E-4,5.2570170439025787E-12]
[6.2000000000000048E-2,8.091003140358044E-4,3.1429303604113557E-12]
[6.3000000000000042E-2,7.4173464488413998E-4,1.8766099785239021E-12]
[6.4000000000000043E-2,6.7982323434878644E-4,1.1191048088221578E-12]
[6.5000000000000044E-2,{"$f":"_xlfn.BETA.DIST(A85+A$21/2,B$5,B$6,1)-_xlfn.BETA.DIST(A85-A$21/2,B$5,B$6,1)","v":6.2294010232477603E-4},{"$f":"_xlfn.BETA.DIST(A85+A$21/2,C$5,C$6,1)-_xlfn.BETA.DIST(A85-A$21/2,C$5,C$6,1)","v":6.666889262874065E-13}]
[6.6000000000000045E-2,5.706909021645501E-4,3.9657166439610592E-13]
[{"$f":"A86+A$21","v":6.7000000000000046E-2},5.2271075166587799E-4,2.3558932582545822E-13]
[6.8000000000000047E-2,4.7866219150127787E-4,1.3988810110276972E-13]
[6.9000000000000047E-2,4.3823326638625648E-4,8.3044682241961709E-14]
[7.0000000000000048E-2,4.0113572402877828E-4,4.9071857688431919E-14]
[7.1000000000000049E-2,3.6710332673084434E-4,2.9087843245179101E-14]
[7.200000000000005E-2,3.3589027042080222E-4,1.7097434579227411E-14]
[7.3000000000000051E-2,3.0726970588013103E-4,1.021405182655144E-14]
[7.4000000000000052E-2,2.8103235694876272E-4,5.8841820305133297E-15]
[7.5000000000000053E-2,2.5698523056549849E-4,3.5527136788005009E-15]
[7.6000000000000054E-2,2.3495041361543123E-4,2.1094237467877974E-15]
[7.7000000000000055E-2,2.1476395167341344E-4,1.1102230246251565E-15]
[7.8000000000000055E-2,1.9627480489570193E-4,0]
[7.9000000000000056E-2,1.7934387648022021E-4,0]
[8.0000000000000057E-2,1.6384310928652113E-4,0]
[8.1000000000000058E-2,1.4965464640037673E-4,0]
[8.2000000000000059E-2,1.3667005161632773E-4,0]
[8.300000000000006E-2,1.2478958600070644E-4,0]
[8.4000000000000061E-2,1.1392153689104845E-4,0]
[8.5000000000000062E-2,1.0398159587976519E-4,0]
[8.6000000000000063E-2,9.4892282510139481E-5,0]
[8.7000000000000063E-2,8.6582410601554471E-5,0]
[8.8000000000000064E-2,7.8986594289176537E-5,0]
[8.9000000000000065E-2,7.2044791043168743E-5,0]
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[0.94000000000000072,0,0]
[0.94100000000000072,0,0]
[0.94200000000000073,0,0]
[0.94300000000000073,0,0]
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[0.94500000000000073,0,0]
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[0.94700000000000073,0,0]
[0.94800000000000073,0,0]
[0.94900000000000073,0,0]
[0.95000000000000073,0,0]
[0.95100000000000073,0,0]
[0.95200000000000073,0,0]
[0.95300000000000074,0,0]
[0.95400000000000074,0,0]
[0.95500000000000074,0,0]
[0.95600000000000074,0,0]
[0.95700000000000074,0,0]
[0.95800000000000074,0,0]
[0.95900000000000074,0,0]
[0.96000000000000074,0,0]
[0.96100000000000074,{"$f":"_xlfn.BETA.DIST(A981+A$21/2,B$5,B$6,1)-_xlfn.BETA.DIST(A981-A$21/2,B$5,B$6,1)","v":0},{"$f":"_xlfn.BETA.DIST(A981+A$21/2,C$5,C$6,1)-_xlfn.BETA.DIST(A981-A$21/2,C$5,C$6,1)","v":0}]
[0.96200000000000074,0,0]
[{"$f":"A982+A$21","v":0.96300000000000074},0,0]
[0.96400000000000075,0,0]
[0.96500000000000075,0,0]
[0.96600000000000075,0,0]
[0.96700000000000075,0,0]
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[0.96900000000000075,0,0]
[0.97000000000000075,0,0]
[0.97100000000000075,0,0]
[0.97200000000000075,0,0]
[0.97300000000000075,0,0]
[0.97400000000000075,0,0]
[0.97500000000000075,0,0]
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[0.97800000000000076,0,0]
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[0.98000000000000076,0,0]
[0.98100000000000076,0,0]
[0.98200000000000076,0,0]
[0.98300000000000076,0,0]
[0.98400000000000076,0,0]
[0.98500000000000076,0,0]
[0.98600000000000076,0,0]
[0.98700000000000077,0,0]
[0.98800000000000077,0,0]
[0.98900000000000077,0,0]
[0.99000000000000077,0,0]
[0.99100000000000077,0,0]
[0.99200000000000077,0,0]
[0.99300000000000077,0,0]
[0.99400000000000077,0,0]
[{"$f":"A1014+A$21","v":0.99500000000000077},0,0]
[{"$f":"A1015+A$21","v":0.99600000000000077},0,0]
[{"$f":"A1016+A$21","v":0.99700000000000077},0,0]
[{"$f":"A1017+A$21","v":0.99800000000000078},0,0]
[{"$f":"A1018+A$21","v":0.99900000000000078},0,0]

{"sheet":"PopMean Ws","index":7,"n_rows":620,"n_cols":15,"rows_kept":620,"cut":false,"hidden":false,"merged":[[15,3,15,4],[15,10,15,11],[6,1,6,2],[7,1,7,2],[11,1,11,2],[12,1,12,2],[13,1,13,2],[15,1,15,2],[5,1,5,2],[0,1,0,15],[1,1,1,3],[2,1,2,3],[3,1,3,2],[4,1,4,2]]}
[null,"Worksheet: Computing 90% Confidence Intervals with Small Samples"]
[null," The \"t-stat\" is used for small samples.  Note that two excel formulas are introduced here that give the same answers as the steps in the book: VAR() and TINV().  This will slightly simplify the calculation.  You can use this spreadsheet to compute ranges for larger samples, too.  Just add however many rows you need and don't forget to change the references in the cells to capture all the new rows.  See how close the answers would be if you just used the \"Mathless\" table.\n\nThis sheet will also calculate an estimate of the underlying distribution using a t-distribution. One will notice from the previous example that this may or ay not be a good assumption."]
[null,"90% Confidence Interval Calculations (by steps described in book)"]
[null,"Average of Sample Values (step 1.a in book)",null,{"$f":"AVERAGE(C18:C117)","v":27.103569366319892}]
[null,"Population Variance",null,{"$f":"VAR(C18:C117)","v":57.071368573250624}]
[null,"SD of the estimate of the mean",null,{"$f":"SQRT(D5/COUNT(C18:C117))","v":1.6892508483533555}]
[null,"t-stat using Excel's \"Tinv()\" formula (same answer as step 3 in book)",null,{"$f":"TINV(0.1,COUNT(C18:C117)-1)","v":1.7291328115213698}]
[null,"Sample error (step 4 in book)",null,{"$f":"D7*D6","v":2.9209390687780967}]
[null,"SD",null,{"$f":"_xlfn.STDEV.S(C18:C117)","v":7.5545594559345828}]
[null,"Sample size",null,{"$f":"COUNT(C18:C1001)","v":20}]
[]
[null,"Computing bounds for 90% CI (step 5 in book)"]
[null,"Upper bound",null,{"$f":"D4+D8","v":30.024508435097989}]
[null,"Lower bound",null,{"$f":"D4-D8","v":24.182630297541795}]
[]
[null,"Data ",null,"Chart Data",null,"Std Error for Estimate of Mean (Normalized)","cpf for Sample","Incremental P ","Sample","Error of the mean","Confidence Interval Bars",null," Estimate of Distribution"," Estimate of Distribution","Error of the Mean"]
[null,"Data Point","Sample Value","Min",{"$f":"MIN(C18:C117)","v":14},-4.2,{"$f":"NORMDIST(F17+0.05,0,1,1)","v":1.6623763729652213E-5},null,null,null,{"$f":"D14","v":24.182630297541795},0,{"$f":"E17-(E19*100)","v":-3.1875}]
[0,1,19.60341451227848,"Max",{"$f":"MAX(C18:C117)","v":41.5},-4.0999999999999996,{"$f":"NORMDIST(F18+0.05,0,1,1)","v":2.5608816474041486E-5},{"$f":"(G18-G17)","v":8.9850527443892729E-6},{"$f":"D$4+F18*D$6","v":20.177640888071135},null,{"$f":"D14","v":24.182630297541795},{"$f":"_xlfn.T.DIST(((K18+2*E19)-D$4)/D$6,D$10-1,1)-_xlfn.T.DIST((K18-D$4)/D$6,D$10-1,1)","v":2.1786871236267859E-2},{"$f":"M17+$E$19","v":-3.015625},{"$f":"_xlfn.T.DIST((M19-D$4)/D$9,D$10-1,1)-_xlfn.T.DIST((M17-D$4)/D$9,D$10-1,1)","v":4.1048154149605858E-5},{"$f":"_xlfn.T.DIST((M19-D$4)/D$6,D$10-1,1)-_xlfn.T.DIST((M17-D$4)/D$6,D$10-1,1)","v":2.6126249288936221E-14}]
[0,2,14,"Increment",{"$f":"(E18-E17)/160","v":0.171875},-4,{"$f":"NORMDIST(F19+0.05,0,1,1)","v":3.9075596597787456E-5},{"$f":"(G19-G18)","v":1.346678012374597E-5},20.346565972906468,{"$f":"_xlfn.T.DIST((I19-D$4)/D$6,D$10-1,1)-_xlfn.T.DIST((I18-D$4)/D$6,D$10-1,1)","v":7.822297032338601E-5},{"$f":"D13","v":30.024508435097989},0,-2.84375,{"$f":"_xlfn.T.DIST((M20-D$4)/D$9,D$10-1,1)-_xlfn.T.DIST((M18-D$4)/D$9,D$10-1,1)","v":4.3237982524760288E-5},{"$f":"_xlfn.T.DIST((M20-D$4)/D$6,D$10-1,1)-_xlfn.T.DIST((M18-D$4)/D$6,D$10-1,1)","v":2.9105525132326058E-14}]
[0,3,29.777969943565893,null,null,-3.9,{"$f":"NORMDIST(F20+0.05,0,1,1)","v":5.9058912418922381E-5},{"$f":"(G20-G19)","v":1.9983315821134925E-5},20.515491057741805,9.8308714610202143E-5,{"$f":"D13","v":30.024508435097989},{"$f":"L18","v":2.1786871236267859E-2},-2.671875,4.554334619021695E-5,3.2442400224815572E-14]
[0,4,19,null,null,-3.8,8.841728520080376E-5,{"$f":"(G21-G20)","v":2.9358372781881379E-5},20.684416142577142,1.2348137015670525E-4,null,null,-2.5,4.7970182419311471E-5,3.6181948605145834E-14]
[0,5,22.472662067192626,null,null,-3.7,1.3112015442048446E-4,{"$f":"(G22-G21)","v":4.2702869219680701E-5},20.853341227412479,{"$f":"_xlfn.T.DIST((I22-D$4)/D$6,D$10-1,1)-_xlfn.T.DIST((I21-D$4)/D$6,D$10-1,1)","v":1.5498574566802533E-4},null,null,-2.328125,5.0524718820917373E-5,4.0375197884841549E-14]
[0,6,36,null,null,-3.6,{"$f":"NORMDIST(F23+0.05,0,1,1)","v":1.9261557563563276E-4},6.1495421215148298E-5,21.022266312247812,1.9435143169228234E-4,null,null,-2.15625,5.3213486296102542E-5,4.5079951683410054E-14]
[0,7,26.248730784373315,null,null,-3.5,2.8029327681617738E-4,8.7677701180544624E-5,{"$f":"D$4+F24*D$6","v":21.191191397083148},2.4344991409986998E-4,null,null,-1.984375,5.6043332473480799E-5,5.036173067174571E-14]
[0,8,32.496312612666316,null,null,-3.4,{"$f":"NORMDIST(F25+0.05,0,1,1)","v":4.0405780186402075E-4},1.2376452504784337E-4,21.360116481918482,3.0456006189852943E-4,null,null,-1.8125,5.9021435632935137E-5,5.6294850051158251E-14]
[0,9,17,null,null,-3.3,5.7702504239076603E-4,1.7296724052674528E-4,21.529041566753818,3.8044203151619345E-4,null,null,-1.640625,6.2155319126924692E-5,6.2963654083856472E-14]
[0,10,35,null,null,-3.19999999999999,8.1635231282858845E-4,2.3932727043782243E-4,21.697966651589169,4.7441907587551031E-4,null,null,-1.46875,6.5452866307937193E-5,7.046393153218045E-14]
[0,11,26.446772517453628,null,null,-3.0999999999999899,1.1442068310227363E-3,3.2785451819414789E-4,21.866891736424506,5.9046595981379537E-4,null,null,-1.296875,6.8922335969976356E-5,7.8904539635020047E-14]
[0,12,39,null,null,-2.9999999999999898,1.5888696473649174E-3,{"$f":"(G29-G28)","v":4.446628163421811E-4},22.035816821259843,7.3330161798896903E-4,null,null,-1.125,7.2572378311258428E-5,8.8409268640295985E-14]
[0,13,41.5,null,null,-2.8999999999999901,2.1859614549133047E-3,5.9709180754838722E-4,22.20474190609518,9.0848230830345086E-4,null,null,-0.953125,7.6412051424312602E-5,9.9118984027733792E-14]
[0,14,22.851439941356947,null,null,-2.7999999999999901,2.9797632350546427E-3,7.9380178014133804E-4,22.373666990930513,1.1224897898124914E-3,null,null,-0.78125,8.0450838318749411E-5,1.1119408952072801E-13]
[0,15,30.269414387144479,null,null,-2.69999999999999,4.0245885427584224E-3,1.0448253077037797E-3,22.54259207576585,1.3828069940034476E-3,null,null,-0.609375,8.4698664480914215E-5,1.2481736094576051E-13]
[0,16,34,null,null,-2.5999999999999899,5.386145954066837E-3,1.3615574113084146E-3,22.711517160601183,1.6979713110588874E-3,null,null,-0.4375,8.9165915973287119E-5,1.4019720912541527E-13]
[0,17,23.508369792067629,null,null,-2.4999999999999898,7.1428107352716139E-3,1.7566647812047769E-3,22.880442245436519,2.0775930890862618E-3,null,null,-0.265625,9.3863458075263504E-5,1.5757143948943291E-13]
[0,18,22.527255652526545,null,null,-2.3999999999999901,9.3867055348388212E-3,2.2438947995672073E-3,23.049367330271856,2.5323244361108062E-3,null,null,-9.375E-2,9.8802654465429056E-5,1.7721158719733209E-13]
[0,19,21.820836119113643,null,null,-2.2999999999999901,1.2224472655045003E-2,2.8377671202061815E-3,23.218292415107193,3.0737612113880576E-3,null,null,7.8125E-2,1.0399538694374621E-4,1.9942791956965208E-13]
[0,20,28.548208996658332,null,null,-2.19999999999999,1.5777607391090891E-2,3.5531347360458886E-3,23.387217499942526,3.7142595913896362E-3,null,null,0.25,1.0945407569036623E-4,2.2457521285753085E-13]
[0,21,null,null,null,-2.0999999999999899,2.0182215405704869E-2,4.4046080146139775E-3,23.556142584777863,4.466648298549751E-3,null,null,0.421875,1.1519170005581448E-4,2.5305942823868069E-13]
[0,22,null,null,null,-1.99999999999999,2.5588059521639211E-2,5.4058441159343419E-3,23.725067669613196,5.3438190714124728E-3,null,null,0.59375,1.2122181987514512E-4,2.8534543288155335E-13]
[0,23,null,null,null,-1.8999999999999899,3.2156774795614428E-2,6.5687152739752168E-3,23.893992754448533,6.3581818494570196E-3,null,null,0.765625,1.27558597296391E-4,3.2196593652479522E-13]
[0,24,null,null,null,-1.7999999999999901,4.0059156863817939E-2,7.9023820682035117E-3,24.06291783928387,7.5209780110961494E-3,null,null,0.9375,1.3421681911120415E-4,3.6353184293633036E-13]
[0,25,null,null,null,-1.69999999999999,4.9471468033649123E-2,9.4123111698311834E-3,24.231842924119206,8.84145524041164E-3,null,null,1.109375,1.4121191957282178E-4,4.1074424962890383E-13]
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[0,27,null,null,null,-1.49999999999999,7.3529259609649775E-2,1.2958501607589594E-2,24.569693093789876,1.1976710777555966E-2,null,null,1.453125,1.562778709318772E-4,5.2544969322697666E-13]
[0,28,null,null,null,-1.3999999999999899,8.8507991437403635E-2,1.4978731827753861E-2,24.73861817862521,1.3791117793889807E-2,null,null,1.625,1.643830394525217E-4,5.9493277391229804E-13]
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[0,48,null,null,null,0.60000000000002995,0.74215388919414504,3.3313575982481081E-2,28.117119875331955,3.3610567667198943E-2,null,null,5.0625,4.4223861712597377E-4,8.3656962129073291E-12]
[0,49,null,null,null,0.70000000000002904,0.77337264762314051,3.1218758428995463E-2,28.286044960167288,3.1592633863551267E-2,null,null,5.234375,4.6406662168683565E-4,9.6303719711333594E-12]
[0,50,null,null,null,0.80000000000003002,0.80233745687731606,2.896480925417555E-2,28.454970045002629,2.9405106793333857E-2,null,null,5.40625,4.8690057652825153E-4,1.1096042051108552E-11]
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[null,"Finish01","Finish02","Finish03","Finish04","Finish05","Finish06","Finish07","Finish08","Finish09","Finish10",{"$f":"IF(SIPmath,\"Finish - \",\"\")","v":"Finish - "}]
["Expected Value",{"$f":"ROUND( AVERAGE( Finish01 ), $B$9 )","v":4},{"$f":"ROUND( AVERAGE( Finish02 ), $C$9 )","v":9},{"$f":"ROUND( AVERAGE( Finish03 ), $D$9 )","v":9},{"$f":"ROUND( AVERAGE( Finish04 ), $E$9 )","v":10},{"$f":"ROUND( AVERAGE( Finish05 ), $F$9 )","v":13},{"$f":"ROUND( AVERAGE( Finish06 ), $G$9 )","v":15},{"$f":"ROUND( AVERAGE( Finish07 ), $H$9 )","v":15},{"$f":"ROUND( AVERAGE( Finish08 ), $I$9 )","v":14},{"$f":"ROUND( AVERAGE( Finish09 ), $J$9 )","v":16},{"$f":"ROUND( AVERAGE( Finish10 ), $K$9 )","v":19},{"$f":"ROUND( AVERAGE( FinishProject ), $L$9 )","v":19}]
["Series Name",{"$f":"$B$1 & \" EV \" & $B$2","v":"Finish01 EV 4"},{"$f":"$C$1 & \" EV \" & $C$2","v":"Finish02 EV 9"},{"$f":"$D$1 & \" EV \" & $D$2","v":"Finish03 EV 9"},{"$f":"$E$1 & \" EV \" & $E$2","v":"Finish04 EV 10"},{"$f":"$F$1 & \" EV \" & $F$2","v":"Finish05 EV 13"},{"$f":"$G$1 & \" EV \" & $G$2","v":"Finish06 EV 15"},{"$f":"$H$1 & \" EV \" & $H$2","v":"Finish07 EV 15"},{"$f":"$I$1 & \" EV \" & $I$2","v":"Finish08 EV 14"},{"$f":"$J$1 & \" EV \" & $J$2","v":"Finish09 EV 16"},{"$f":"$K$1 & \" EV \" & $K$2","v":"Finish10 EV 19"},{"$f":"IF(SIPmath,$L$1 & \" Average=\" & $L$2,\"\")","v":"Finish -  Average=19"}]
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["Loss Exceedance",false,false,false,false,false,false,false,false,false,false,false]
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["Use Axis Title?",false,false,false,false,false,false,false,false,false,false,false]
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[null,{"$f":"IF($B$4, $B110, $B109 / $B$10 & REPT( \" \", $B$7 ))","v":96},{"$f":"IF($C$4, $C110, $C109 / $C$10 & REPT( \" \", $C$7 ))","v":100},{"$f":"IF($D$4, $D110, $D109 / $D$10 & REPT( \" \", $D$7 ))","v":101},{"$f":"IF($E$4, $E110, $E109 / $E$10 & REPT( \" \", $E$7 ))","v":101},{"$f":"IF($F$4, $F110, $F109 / $F$10 & REPT( \" \", $F$7 ))","v":103},{"$f":"IF($G$4, $G110, $G109 / $G$10 & REPT( \" \", $G$7 ))","v":104},{"$f":"IF($H$4, $H110, $H109 / $H$10 & REPT( \" \", $H$7 ))","v":103},{"$f":"IF($I$4, $I110, $I109 / $I$10 & REPT( \" \", $I$7 ))","v":103},{"$f":"IF($J$4, $J110, $J109 / $J$10 & REPT( \" \", $J$7 ))","v":105},{"$f":"IF($K$4, $K110, $K109 / $K$10 & REPT( \" \", $K$7 ))","v":107},{"$f":"IF($L$4, $L110, $L109 / $L$10 & REPT( \" \", $L$7 ))","v":"107          "}]
[null,97,101,102,102,104,105,104,104,106,108,"108          "]
[null,98,102,103,103,105,106,105,105,107,109,"109          "]
[null,99,103,104,104,106,107,106,106,108,110,"110          "]
[null,100,104,105,105,107,108,107,107,109,111,"111          "]
[null,101,105,106,106,108,109,108,108,110,112,"112          "]
[]
[1E-3]
[4.0000000000000001E-3]
[7.0000000000000001E-3]
[0.01]
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[0.03]
[0.04]
[0.05]
[0.06]
[7.0000000000000007E-2]
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[0.14000000000000001]
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