M214:StatisticalErrorandPredictiveModelValidation

Statistical Error and Predictive Model Validation

How accurate is a model for predicting your shoe size based on your height? It's very rare to create a perfect predictive model — so how do you know whether to trust the ones you build? In M214 Statistical Error and Predictive Model Validation, you'll develop the tools to evaluate, improve, and compare predictive models with confidence.

In M214 Statistical Error and Predictive Model Validation, you will:


• Explain and quantify variation and its sources, and master multiple methods of model validation, uncertainty, and comparison
• Apply statistical techniques to improve predictive models, including F-tests, sensitivity analysis, ANOVA, residual plots, and cross-validation
• Understand the balance between bias and variance, and use that understanding to make better modeling decisions

Examples of careers that use M214 concepts: actuarial science, engineering, public health, and environmental sciences.

Learning Goals

Content & Practice Expectations

Each badge framework describes the mathematics skills and understandings that students will need to demonstrate to earn each badge through a set of Content & Practice Expectations (CPEs).

Assessments

Introduction to Badge Assessments

This document provides an overview of the different badging forms of assessment.

Portfolio Resources

A portfolio of evidence consists of student-produced artifacts that demonstrate proficiency against a badge’s CPEs. The portfolio development process involves students selecting and reflecting on artifacts that showcase their understanding of badge CPEs, followed by iterative teacher review and student-teacher conferencing until the work demonstrates proficiency.

Professional Learning Resources

Download a sample Professional Learning presentation for this badge that you may copy and customize to suit your needs. When available, samples of student work are provided for discussion.