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.

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 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 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.

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 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.

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.

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.