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Currently Playing: Simple Linear Regression – Module 10 – Quantitative Methods – CFA® Level I 2026

Get our FREE CFA Level 1 summaries: https://www.finquiz.com/cfa/level-1/summary 📉 Quant Methods Got You Spiraling? FinQuiz = Your CFA Lifeline Quant isn’t just plug-and-chug. It’s logic, timing, and not getting trapped on exam day. Whether you're battling z-scores or trying to remember if it's n or n–1, we’ve got your back. 📎 Battle-Ready Summaries – No fluff, no chaos. Just the core Quant ideas, explained clearly 👉 https://www.finquiz.com/cfa/level-1/summary/ 🧷 Stanley Notes – Clean breakdowns of complex concepts (yes, even heteroskedasticity) 👉 https://www.finquiz.com/cfa/level-1/notes/ 📌 Formula Sheet – All the essentials on one page. Screenshot it. Tattoo it. Just don’t forget it. 👉 https://www.finquiz.com/cfa/level-1/formula-sheet/ 🎮 Question Bank – Practice like you mean it. Real CFA-style traps, logic puzzles, and curveballs 👉 https://www.finquiz.com/cfa/level-1/question-bank/ ⏱ Mock Exams – Time pressure. Real feel. Actual anxiety simulator (but also confidence booster) 👉 https://www.finquiz.com/cfa/level-1/mock-exam/ 🧃 Explore All CFA Level 1 Resources 👉 https://www.finquiz.com/cfa/level-1/ 💸 Want the full upgrade? Go Premium = Everything unlocked + guidance to crush Level 1 👉 https://www.finquiz.com/cfa-level-1-study-packages/ 0:00 Introduction: Simple Linear Regression (CFA Level 1) Overview of regression analysis in finance Using one independent variable (SLR) to predict a dependent variable 0:39 Key Variables in Regression Dependent (explained) variable Independent (explanatory) variable The linear relationship 1:18 Basic Assumptions of SLR Linearity (linear relationship between X and Y) Homoskedasticity (constant variance of errors) Independence (no autocorrelation) Normality of error terms 2:02 Estimating Coefficients & Minimizing Errors Ordinary Least Squares (OLS) method 2:55 Data Types in Regression Time series (observations over time) Cross-sectional (data at one point in time) Panel data (combination of both) 3:22 Measuring Goodness of Fit Coefficient of Determination F-statistic (overall significance of the model) Standard Error of Estimate (accuracy of predictions) 4:20 Hypothesis Testing in Regression F-test: are any coefficients non-zero? t-test: testing a specific coefficient (e.g., slope) Use of ANOVA (Analysis of Variance) framework 5:04 Transformations: Log-Lin, Lin-Log, Log-Log When relationships are not strictly linear Log-Lin: Dependent variable in logs (percentage change in Y for unit change in X) Lin-Log: Independent variable in logs (diminishing effect of X on Y) Log-Log: Both in logs (elasticity interpretation) 7:12 Practical Example & Model Selection Checking residuals and patterns Choosing transformations based on improved fit and reduced errors Statistical software and visual diagnostics 8:10 Conclusion & CFA Exam Tips Recap of key SLR concepts (coefficients, hypothesis tests, transformations) Importance of practicing with real data & CFA curriculum problems Encouragement for mastering regression for both exam and professional use