Decision Modeling and Risk Analysis with Excel

Master Excel basics to organize, look up, and summarize data, then apply data analysis tools like Goal Seek, Solver, and Data Table; explore sensitivity analysis on a cafe scenario.

Organize your data and master Excel lookups by comparing vlookup, hlookup, and index&match for vertical and horizontal lookups. Use index&match for dynamic, exact-match lookups with flexible column references.

Learn how to organize data in Excel using vlookup to fill category and price, then apply index match for the same results, and use sumproduct to total sales.

Apply the sumif family of formulas to summarize and filter data in Excel, including sumifs, countifs, and averageifs. Build robust criteria-based totals and averages across multiple conditions.

Explore how to organize data with pivot tables in Excel, create calculated fields like profit margin, filter by location and date, and visualize results with charts.

Learn to apply Excel's data analysis toolpak goal seek to back-solve for a target value by setting set cell and adjusting changing cell, illustrated with a break-even coffee problem.

Learn how solver extends goal seek to optimize multiple decision variables under constraints, illustrated with a cafe model that minimizes cost and maximizes profit.

Learn how data tables support what-if analysis in Excel for decision modeling, using single and two-variable tables to see how fixed costs and units sold influence profit.

Learn how to perform sensitivity analysis in Excel using data tables and a no add-in tornado chart to visualize how price changes affect total profit across four coffees.

Learn to install and use the SensIt tornado chart add-in to perform single and two-factor sensitivity analyses, creating tornado and spider charts for profit scenarios.

Learn how to use Excel's solver sensitivity report to interpret variable and constraint tables, understand reduced costs, shadow prices, and allowable changes in a linear model.

Learn decision tree modeling in Excel to visualize uncertainty and compare decision scenarios using expected value. Explore value of information and utility under risk aversion through the FGC lawsuit example.

Install and activate a decision tree add-in in Excel, comparing treeplan and BYTreeplan, and master loading, managing, and troubleshooting add-ins for future decision tree work.

Learn how to construct and read a decision tree using treeplan or BYtree, including decision nodes, event nodes, and terminal payoffs, compute probabilities and expected value of a two-draw lottery.

Explore the value of information by comparing decision outcomes with and without knowledge of rivals' bids, using an Excel decision tree to compute expected values for perfect and imperfect information.

We compute the value of perfect information from a bidding scenario using excel tools like sumproduct to compare expected values with and without information and choose the optimal action.

Explore how imperfect information from market research maps to flipped probabilities using Bayes' theorem and tree flipping, and apply to decision trees under uncertainty.

Explore how Bayes' theorem informs conditional probabilities, then perform tree flipping to reconstruct decision trees and assess the value of imperfect information in a Rainbow in a Cup case study.

Explore utility theory and risk attitudes, comparing expected value with utility, and using certainty equivalents to illustrate risk-averse decision making.

Study exponential utility functions to model risk preferences, compute certainty equivalents, and estimate risk tolerance from gambles or wealth for use in decision trees.

Apply utility theory with the exponential utility function in an Excel decision tree to integrate risk tolerance, compute expected utility, and determine the certainty equivalent for Maria's gamble.

Explore Monte Carlo simulation in Excel to test decision scenarios with tens of thousands of random inputs, using probability distributions and confidence intervals to compare outcomes.

Learn how probability distribution functions power both discrete and continuous variables, with PMF and PDF, and how the cumulative distribution function sums areas to 1 for intervals.

Explore the normal distribution, its mean and standard deviation, and how to plot and interpret the bell curve using Excel to compute pdf, cdf, and a 95% confidence interval.

Learn how to standardize a normal distribution to a standard normal, compute z-scores, and derive 95 percent confidence intervals using Excel’s Norm.S.DIST and Norm.S.INV.

Explore stochastic dominance to compare portfolio distributions using pdf and cdf plots, identifying first-order and second-order dominance and how risk preferences guide decisions for discrete distributions.

Explore the central limit theorem through sample means, population parameters, and standard error, and apply XLrisk to simulate and verify the distribution of sample means.

Explore eight common probability distributions for discrete and continuous variables, learn their XLRisk formulas, and run simulations to compare PDFs, CDFs, and histograms.

Explore Monte Carlo simulation fundamentals using an aircraft overbooking example, modeling show-ups, revenue, and bump costs in Excel to maximize profit and apply the central limit theorem.

Explore Monte Carlo simulation in Excel without add-ins, using binomial distribution, rand, and binomial.inv to simulate show-ups and compare overbooking choices via distribution, mean, and confidence intervals.

Learn monte carlo simulation in excel with the xlrisk add-in for an overbooking example by defining a binomial random variable and forecast outputs, then compute confidence intervals.

Solve the hiring decision by simulating net income in XLRisk, using normal and beta-pert distributions, and estimate 95% confidence intervals and loss probability to find the optimal staffing level.

Apply solver to maximize expected net income by varying the number of employees, then evaluate risk with stochastic dominance and cdf plots to select a decision aligned with risk tolerance.

Apply utility theory in a simulation to select the optimal hiring decision using exponential utility, a risk tolerance, and certainty equivalents in XLRisk.

Explore what correlation means in simulation, learn to measure it with Pearson's R and Excel's CORREL, build correlation matrices, and apply these concepts to XLRisk modeling.

Apply correlation in XLRisk simulations using risk Corrmat to define a correlation matrix among random variables; observe how positive and negative correlations alter output risk in hiring decisions.