Excel Analytics: Linear Regression Analysis in MS Excel — Lesson 1: Regression Foundations, Business Questions and Data Preparation

Learning Objectives

• Explain dependent and independent variables, correlation, regression, coefficients, assumptions, residuals, and business use cases
• Define the analytical question and prepare clean numeric data, labels, missing values, outliers, and appropriate samples
• Validate regression foundations, business questions and data preparation decisions with documented requirements, evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Explain dependent and independent variables: Explain dependent and independent variables, correlation, regression, coefficients, assumptions, residuals, and business use cases
• Define the analytical question and prepare clean numeric data: Define the analytical question and prepare clean numeric data, labels, missing values, outliers, and appropriate samples
• Validate regression foundations: Validate regression foundations, business questions and data preparation decisions with documented requirements, evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, administration, testing, or operational workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Create a requirements and readiness checklist, then design or configure the first working foundation.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Regression Foundations, Business Questions and Data Preparation in a realistic scenario and validate the result with documented evidence.