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Introduction to Data Science (DS 215)
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Module 4
Foundations of Statistical Inference
1
Parameters vs. Statistics
Distinguish between population parameters and sample statistics.
2
Properties of Estimators: Bias, Variance, and Consistency
Define and mathematically formulate the properties of an estimator: bias, variance, and consistency.
3
Key Estimation Methods Explained
Explain the purpose of key estimation methods: Method of Moments, Maximum Likelihood (MLE), and Bayesian Estimation.
4
Method of Moments Estimation for Gamma Distribution Parameters
Derive the Method of Moments estimators for the parameters of a Gamma distribution.
5
Sufficient Statistics and the Rao-Blackwell Theorem
Explain the concept of a sufficient statistic and its role in estimation via the Rao-Blackwell theorem.
Previous module
Asymptotic Theory and Simulation
Next module
Maximum Likelihood and Least Squares Estimation