This multipart problem evaluates competency in probability and mathematical statistics, covering order statistics and covariance, the rationale for degrees of freedom in sample variance, z-test sample size calculation, and a one-sided concentration inequality, categorized under Statistics & Math for a Data Scientist role and combining conceptual derivations with practical formula application. Such questions are commonly asked to verify mathematical rigor in deriving expectations and covariances, understanding sampling distributions and unbiased estimation, applying hypothesis testing power formulas, and proving probabilistic inequalities that underpin statistical reasoning.
Answer the following four questions.
Let and be independent . Define:
Compute .
Explain why the usual sample variance
uses degrees of freedom instead of .
How do you estimate the required sample size for a z-test to achieve significance level and power for detecting a difference ? State the formula and assumptions.
Prove the one-sided Chebyshev (Cantelli) inequality: If has mean and variance , then for any ,
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