You rely on predictions every day: you might check the weather app before choosing your outfit or peek at the traffic before starting your commute. Perhaps you are responsible for setting your organization’s strategy in the future. Do you find yourself wondering how accurate predictions are, how you can see into the future, and why the weatherman always seems to be wrong? In our Error and Uncertainty course, you’ll make some predictions yourself, learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making. You will uncover patterns in Seattle crime data, predict students’ final grades, prevent Nashville traffic accidents, and determine whether a bakery’s menu needs to change. Join us! We’re certain you’ll enjoy learning about error and uncertainty.
Defining error, uncertainty, and riskFree
The first chapter presents common terminology, introduces methods for determining significant differences between groups, and outlines the kinds of error and uncertainty involved. We will specifically look at Seattle crime data and evaluate crime rate differences between precincts and neighborhoods. This chapter will equip learners to identify threats to the validity and accuracy of their conclusions.Defining error and uncertainty50 xpMeasures of central tendency100 xpCrime time100 xpIF functions50 xpExtracting UNIQUE() values100 xpBook 'em and count 'em100 xpAverages and IF conditions100 xpCounts with multiple criteria100 xpCorrelation50 xpRap sheet100 xpCorrelation preparation100 xpA (crimes) committed relationship100 xpStrong relationships50 xp
Making accurate predictions
The second chapter outlines both rudimentary (e.g., moving average, seasonal average, yearly average) and more complicated methods (e.g., linear regression) for making predictions and outlines the kinds of error and uncertainty involved. We will specifically look at anonymized student grades data and evaluate the accuracy of our predictions for given students. Throughout the chapter, we will identify threats to the validity and accuracy of our predictions.Making the grade50 xpWe all have our (central) tendencies100 xpVariable weights100 xpNow weight a minute100 xpLying in weights50 xpAdvanced prediction strategies50 xpWhat's in the FORECAST()?100 xpVariation in predictions100 xpSeems about right50 xpHow clear is your crystal ball?50 xpPrediction accuracy100 xpAbsolute deviation100 xpAverage absolute deviation100 xpStatistical significance50 xpSignificant differences100 xpSignificant differences of opinion50 xp
Poking holes in predictions
Chapter 3 encourages learners to test the assumptions of their predictions using data on car crashes. Specifically, they will determine how to allocate resources to reduce injuries and fatalities from auto accidents. Learners will discuss the impact of outliers in prediction accuracy, evaluate the importance of normally distributed data in making predictions, employ consequence-likelihood matrices in risk management, and adapt psychological heuristics to discussions of numerical uncertainty and risk.Outliers50 xpDown and outlier100 xpNo filter100 xpAddressing outliers100 xpSparklines50 xpCan't start a fire without a spark(line)100 xpThe max matters100 xpWhat's the worst that could happen?50 xpThere are consequences100 xpA likely story100 xpRisky business100 xpRisky business50 xpRandom numbers100 xpHow random100 xpBe fruitful and multiply100 xpRevisiting sparklines50 xp
Case study: Should you change your bakery's menu?
The final chapter integrates all the previous lessons into a constructed-world scenario. Learners are tasked with updating the menu at their small business: the Risky Business Bakery. They need to figure out whether to add or drop menu items based on whether there are significant differences in sales by baked good; whether their predicted sales figures from their accountant are accurate.A half-baked idea?50 xpHalf-baked ideas100 xpFalling on hearth times100 xpSummary statistics50 xpDo you know your muffins, man?50 xpChanging prices100 xpBread on the rise?100 xpPaying the price50 xpIs change on the menu?50 xpA recipe for change100 xpRain, rain, go away100 xpFed up50 xpReview: Are we certain now?50 xpAdding variation100 xpWin some, lose some100 xpJust t-testing100 xpWrap-up50 xp
In the following tracksIntermediate Spreadsheets
PrerequisitesIntroduction to Statistics in Spreadsheets
Evan KramerSee More
I help clients define and execute their data strategy. Before joining Thoughtworks, I worked in various leadership roles in the public sector, most recently leading the HR, operations, data, and IT functions for a 1800-person organization. I got my start working in wilderness education and wilderness therapy before becoming a teacher and administrator at various schools.