# kaal:claim:2389423-019

**Claim.** Least squares linear regression is non-robust to outliers: in the presence of outliers its predictions can be dragged toward the outliers and the variance of the estimates can be artificially inflated.

**Type.** failure  **Support.** argued

**Holds when.**

- least squares linear regression applied to samples containing outliers

**Source quote.**

> In the presence of outliers, LSLR predictions can be dragged towards the outliers and the variance of the LSLR estimates can be artificially inflated.

**From.** Wulf A. Kaal, *The Impact of Dodd-Frank Act Compliance Cost on the Hedge Fund Industry* (2014), Methodology, page 10

**Cite as.** Wulf A. Kaal, The Impact of Dodd-Frank Act Compliance Cost on the Hedge Fund Industry (2014). SSRN: https://ssrn.com/abstract=2389423

**Verify.** sha256 of source PDF `6b95323abbaffd00a012589531859f2938ab8b372d0243171059ff82e55a0838` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202014%20-%20The%20Impact%20of%20Dodd-Frank%20Act%20Compliance%20Cost%20on%20the%20Hedge%20Fund%20Industry.pdf

**Failure mode.** outlier sensitivity of least squares regression  (family: research-design-limitation)

**Topics.** research-methods

**Keywords.** least-squares-regression, outliers, robustness, methodology, estimation-bias

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