What does the Shannon-Hartley theorem convey about the maximum data rate for a noisy channel in communication systems?

Shannon's channel capacity: C = B log₂(1 + S/N) bits per second

Shannon's formula: C = B log₂(1 + S/N) defines channel capacity in bits/s

How does the concept of information entropy, as described by Claude Shannon, contribute to understanding the complexity and unpredictability of communication systems?

Information entropy quantifies uncertainty and complexity in communication systems, aiding in efficient data encoding and transmission

What the information ratio measures — excess return per unit of tracking error vs a benchmark

Information ratio measures excess return per unit of tracking error relative to a benchmark

What the Coase theorem says — with zero transaction costs, parties negotiate efficient outcomes

Coase theorem: Zero transaction costs lead to efficient resource allocation through bargaining

What the Nyquist theorem says: sample at ≥ 2× the highest frequency to avoid aliasing

Nyquist theorem: Sample rate ≥ 2*highest frequency to prevent frequency aliasing

What the Sharpe ratio measures — excess return per unit of risk: (R - Rf) / σ

Sharpe ratio: Excess return per standard deviation of portfolio returns