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