Chapter 10: Simple Linear Regression

In this section

• The Least Squares Criterion and Estimating Regression Coefficients
  Explore the fundamental concepts and practical techniques of simple linear regression, focusing on least squares estimation, slope and intercept interpretation, and application within a financial context.
• Assumptions of the Simple Linear Regression Model
  Explore the key assumptions underlying simple linear regression, including linearity in parameters, random sampling, homoscedasticity, and more. Understand why these assumptions are crucial for accurate parameter estimates and reliable inference, and learn how to check for common violations using diagnostic tests and residual analysis.
• Residual Analysis and Model Diagnostics
  Discover how to analyze regression residuals, identify potential issues like outliers and heteroskedasticity, and apply diagnostic tests to ensure a robust simple linear regression model.
• Measures of Fit (R², Standard Error of Estimate, ANOVA)
  Explore R², Standard Error of Estimate, and ANOVA in simple linear regression, including real-world applications, examples, best practices, and key interpretations for CFA® 2025 Level I candidates.
• Prediction Intervals and Functional Forms of Simple Linear Regressions
  Learn how to construct prediction intervals and apply different functional forms in simple linear regressions for robust financial analysis and forecasting.
• Hypothesis Testing on Slope and Intercept
  Learn how to test the significance of slope and intercept in simple linear regression using t-statistics, p-values, and confidence intervals. Explore both theoretical foundations and practical finance applications.