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Error and Uncertainty in Google Sheets

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

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4 Hours16 Videos62 Exercises
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Course Description

In a world where predictions shape our daily decisions, from choosing outfits based on weather forecasts to planning commutes with a glance at traffic conditions, understanding the accuracy and intricacies of predictions becomes paramount. Whether you're an individual making personal choices or someone steering the strategy of an entire organization into the future, questions about the reliability of predictions, the ability to foresee events, and the occasional inaccuracies in forecasts may have crossed your mind. If you've ever wondered why the weatherman seems to miss the mark, our illuminating online course on Error and Uncertainty is designed for you.

Unraveling the Fabric of Predictions

Dive into the fascinating realm of predictions in our Error and Uncertainty course, where you'll not only explore the accuracy of forecasts but also actively engage in making predictions yourself. Gain the skills to differentiate genuine patterns from random noise, equipping you with the tools to make informed decisions in the face of uncertainty. This course goes beyond the surface, delving into the psychological crutches that often cloud our rational decision-making processes. Whether you're examining patterns in Seattle crime data, predicting students' final grades, preventing traffic accidents in Nashville, or assessing the need for changes in a bakery's menu, you'll emerge from this course with a heightened ability to navigate the complexities of error and uncertainty.

Hands-On Learning for Practical Insight

Join us on a captivating learning journey as we guide you through real-world applications of error and uncertainty analysis. Through engaging exercises, you'll apply your newfound knowledge to predict outcomes, identify potential pitfalls, and enhance your decision-making capabilities. From decoding crime trends to forecasting academic performance and mitigating traffic risks, our course provides a dynamic learning experience that promises not only insight but also practical skills applicable in a variety of scenarios. Embrace the challenge of understanding error and uncertainty, and join a community of learners who are certain to find joy in unraveling the mysteries of predictions.

  1. 1

    Defining error, uncertainty, and risk

    Free

    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.

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    Defining error and uncertainty
    50 xp
    Measures of central tendency
    100 xp
    Crime time
    100 xp
    IF functions
    50 xp
    Extracting UNIQUE() values
    100 xp
    Book 'em and count 'em
    100 xp
    Averages and IF conditions
    100 xp
    Counts with multiple criteria
    100 xp
    Correlation
    50 xp
    Rap sheet
    100 xp
    Correlation preparation
    100 xp
    A (crimes) committed relationship
    100 xp
    Strong relationships
    50 xp
  2. 2

    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.

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  3. 3

    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.

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  4. 4

    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.

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In the following tracks

Intermediate Google Sheets

Collaborators

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Chester Ismay
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Becca Robins
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Ruanne Van Der Walt
Evan Kramer HeadshotEvan Kramer

Data Scientist

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