"""Create an educational video to explain the CFA Level 1 knowledge: Regression functional forms(with Linear-Linear;Log-linear;Linear-Log;Log-Log) 🎓 Content Requirements: Start with a clear, beginner-friendly definition of the concept Explain the core components and logic step by step Include simple numerical examples or visual analogies Add a short summary or key takeaways at the end Ensure the structure follows a logical teaching flow from concept to application 🎨 Visual and Layout Requirements: Full-screen visuals with centered, readable content Use smooth animations to transition between steps or sections Highlight important terms, formulas, and keywords with bright accent colors (e.g., yellow, red, blue) Avoid text crowding or overlap; leave clear visual spacing Use animated icons, graphs, or diagrams where appropriate (e.g., timelines, flowcharts, charts) Minimize blank space; keep each screen visually rich and balanced 🗣️ Tone and Style: Friendly, clear, and professional Focus on making the topic accessible for first-time learners Avoid excessive jargon; use plain language wherever possible Maintain alignment with CFA curriculum terminology and scope"""

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Welcome to regression functional forms for CFA Level 1. Regression functional forms describe different mathematical ways to model relationships between variables. They determine how we transform our variables before running regression analysis. Understanding these forms is crucial because the choice of functional form affects how we interpret coefficients and make predictions. Let's explore the four main types used in finance. The linear-linear form is the most basic regression model. The equation is Y equals b zero plus b one X plus epsilon. In this form, both variables are in their original units without any transformation. The slope coefficient b one represents the change in Y for a one-unit change in X. For example, in finance, if we regress stock returns against market returns with a slope of 1.2, this means a 1% increase in market return leads to a 1.2% increase in stock return. This relationship appears as a straight line on the graph. Now let's explore log-linear and linear-log forms. The log-linear form uses the natural log of Y as the dependent variable. Here, b one times 100 gives the percentage change in Y for a one-unit change in X. This is called semi-elasticity. The linear-log form uses the natural log of X as the independent variable. Here, b one represents the change in Y for a 1% change in X. These forms are particularly useful in finance for modeling growth rates and relationships with diminishing returns, such as the relationship between firm size and returns. The log-log form uses natural logs for both variables. This gives us true elasticity, where b one represents the percentage change in Y for a percentage change in X. For example, in a price-demand relationship, if the coefficient is negative 1.5, this means a 1% increase in price leads to a 1.5% decrease in demand. The log-log form is perfect for modeling power law relationships and situations where elasticity remains constant across different levels of the variables. This makes it very useful in economics and finance. Let's summarize the four regression functional forms for CFA Level 1. Linear-linear provides direct unit change interpretation. Log-linear gives semi-elasticity showing percentage change in Y. Linear-log shows change in Y per percentage change in X. Log-log provides true elasticity with percentage to percentage relationships. When choosing a functional form, consider economic theory, data characteristics, and interpretation needs. Understanding these forms is essential for proper regression analysis in finance and will help you interpret coefficients correctly in your CFA studies.