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Linear models are a very common choice when modeling the relation between inputs and outputs because of their simplicity and interpretability. We will explore methods for parameter estimation in these models, with an eye toward understanding some of the more advanced techniques. We will start by reviewing the most commonly used estimator: the ordinary least squares (OLS) estimator. Then we will explore some limitations of the OLS estimator when the residuals are not i.i.d. and discuss how to overcome these limitations, first with with weighted least squares and then with generalized least squares. We'll close by discussing linear models in the context of genome-wide association studies (GWAS) as a lead-in to the talk.