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Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
1. Time Series.
2. Classical statistics.
A. Basic concepts and common notation of classical statistics.
B. Chi squared distribution.
C. Student's t-distribution.
D. Classical estimation theory.
E. Pattern recognition.
3. Bayesian statistics.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Basic concepts and common notation of classical statistics.


collection MATH of independent and identically distributed (iid) random variables with distribution $f\left( x\right) $ is called a random sample from population $f\left( x\right) $ .

Let MATH be a real valued function whose domain includes the sample space of MATH . The random variable MATH is called "statistic".

Sample mean and variance are MATH , MATH

The following identity is useful MATH for any numbers $x_{1},...,x_{2}.$

For any sample MATH of population with mean $\mu$ and finite variance $\sigma^{2},$ the sample mean and variance have the properties MATH .





Notation. Index. Contents.


















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