On successful completion of the module, students should be able to:
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Describe the key components of a statistical model.
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Explain the criteria used to evaluate the performance of an estimator.
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Discuss the motivation behind the least squares estimator.
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Estimate a linear regression model using ordinary least squares.
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Interpret the results of a linear regression model.
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Outline the conditions required for the Gauss-Markov theorem to hold.
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Conduct hypothesis tests on the linear regression model
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Examine the linear regression model when the assumptions needed for the Gauss-Markov theorem fail.