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[Description] STEP 1: Multicollinearity and VIF Analysis - Introduction
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Multicollinearity and VIF Analysis Worksheet
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Dataset:decathlon.csv (5 Olympic athletes, 3 events)
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X1:100m dash time (seconds)
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X2:Long jump distance (meters)
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X3:400m run time (seconds)
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Y:Total points
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Learning Objectives:
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1. Understand multicollinearity and its effects on regression
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2. Calculate Variance Inflation Factor (VIF)
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3. Interpret correlation matrices for predictor relationships
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4. Identify problematic multicollinearity (VIF > 5 or 10)
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Key Formulas:
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Correlation:rᵢⱼ = Σ(xᵢ-x̄ᵢ)(xⱼ-x̄ⱼ) / √(SSᵢ × SSⱼ)
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R² (auxiliary):Rⱼ² = R² from regressing Xⱼ on other predictors
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VIF:VIFⱼ = 1 / (1 - Rⱼ²)
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Rule of thumb:VIF < 5: OK, VIF 5-10: Moderate, VIF > 10: Severe
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Color Guide:
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Given Data (Input)
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Intermediate Calculations
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Final Results
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Correct Answer (✓)
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Incorrect Answer (✗)
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