Basic Statistical Concepts

Statistics provides the foundational language and tools for understanding, modeling, and reasoning about the topics covered in this page: machine learning, reinforcement learning, and experiment design. This section serves as a concise, accessible introduction to fundamental statistical concepts. Each page explores a specific topic, providing clear definitions, examples, and connections to practical applications in ML, RL, and experimentation.

Topics Covered

📄️ 1. Random Variable

A random variable is a variable whose values depend on outcomes of a random process. It is a mathematical function that assigns a numerical value to each outcome in a sample space of a random experiment.

📄️ 2. Probability Distributions

A probability distribution describes how the values of a random variable are distributed. It assigns probabilities to the possible values of the random variable, indicating the likelihood of each value occurring.

📄️ 3. Mean, Variance, and Expected Values

These concepts describe the central tendency and variability of a probability distribution.

📄️ 4. Sampling Distributions

Sampling involves selecting a subset of data from a population to estimate characteristics of the whole population.

References

1. Joe Blitztein and Jessica Hwang (2019) Introduction to Probabilit.y 2nd edition, CRC Press.

2. Sheldon Ross (2023) A First Course in Probability 10th edition, Pearson.

3. Douglas C. Montgomery (2019) Design and Analysis of Experiments. 10th edition, Wiley.