Unit 4 • Lesson 30.8h

Outliers and Data Quality

To teach students how to identify and handle outliers, and how to use the Analysis ToolPak to compute descriptive statistics.

What You'll Learn
  • Identify outliers using z-score analysis and business context
  • Evaluate data quality and defend cleaning decisions
  • Explain why outliers matter to planning and forecasting
Key Concepts
Z-scores: measuring how unusual a data point is
Outlier detection: the ±2 standard deviation rule
Data quality decisions: keep, flag, or remove
Lesson Phases

This lesson follows a structured 6-phase learning model designed for authentic project-based learning.

Hook

Review previous learning and connect to today's application activities

Start Phase

Introduction

Introduce Descriptive statistics: mean, median, standard deviation, and z-scores and connect to business applications

Start Phase

Guided Practice

Work through structured examples applying Descriptive statistics: mean, median, standard deviation, and z-scores with teacher support

Start Phase

Independent Practice

Apply Descriptive statistics: mean, median, standard deviation, and z-scores independently to solve authentic business problems

Start Phase

Assessment

Demonstrate understanding through formative assessment and peer evaluation

Start Phase

Closing

Consolidate learning and connect to broader unit goals and real-world applications

Start Phase
How You'll Learn
Identify outliers and compute key statistics using Analysis ToolPak