Ch. 0 - Intro to Statistics

Notes:

What is Statistics

Almost everyone around us− including scientists, engineers, tech
professionals, medical researchers, administrative employees,
banking, insurance and marketing professionals, and consumers−
deals with huge volume of data, or information.

These information or data could be in the form of numbers, texts,
images, or combinations of them.

Statistics is the science of designing studies or experiments,
collecting data, modeling and analyzing data for the purpose of
decision-making and scientific discovery when the available information is both limited and variable.

In a nutshell:

Statistics is the science of Learning from Data.
It’s is the tool to transform data into knowledge.
Helps to make informed decisions under uncertainty.

Examples: Engineering

Quality Control in Engineering

In manufacturing, defects must be minimized.
Control charts and statistical process control detect anomalies.
Example: Car companies use statistics to ensure safety and reliability.

Reliability in Civil Engineering

Bridges and structures face uncertain loads.
Probabilistic models ensure designs are safe and cost-efficient.
Example: Earthquake-resistant design uses statistical risk models.

Examples : Science

Medical Science: Clinical Trials

Testing new drugs requires careful statistical design.
Randomization prevents bias.
Statistical tests confirm if treatments are effective.

Physics and Data Analysis

Large Hadron Collider experiments generate petabytes of data.
Detecting rare events like the Higgs boson relies on statistical inference.
Without statistics, discoveries would remain hidden in noise.

Examples : Banking

Risk Management in Banking

Banks face uncertainty in lending decisions.
Statistical models estimate probability of default.
Example: Credit scoring models use regression and classification.
Case Study: During the 2008 financial crisis, poor risk modeling underestimated mortgage default probabilities, leading to global collapse.

Fraud Detection

Credit card fraud is a multi-billion dollar problem.
Statistical anomaly detection identifies unusual patterns.
Example: Banks use clustering and Bayesian methods to flag fraud.
Case Study: Visa and Mastercard use real-time statistical algorithms to stop fraud, saving billions annually.

Examples: Finance

Stock Market Analysis

Financial markets generate huge time series data.
Statistics helps detect patterns and volatility.
Example: GARCH models are used to forecast stock price fluctuations.
Case Study: JP Morgan’s “Value at Risk” (VaR) model became a standard tool for quantifying potential trading losses.

Portfolio Optimization

Investors seek maximum return for minimum risk.
Statistics quantifies uncertainty in asset returns.
Example: Markowitz’s Mean-Variance Optimization method uses covariance matrices.
Case Study: Modern hedge funds rely on advanced statistical simulations (Monte Carlo methods) to balance risk and return.

Examples : Modern Applications

Artificial Intelligence (AI), data Science, Machine Learning, and Big Data

AI and Data Science are built on statistical foundations.
Engineers use Machine Learning tools for predictive maintenance.
Scientists use big data to analyze climate change, genomics, and many more.

Why Learn Statistics

Change the World

Managing the impacts of climate change to making medicines more effective and reducing hunger and disease.

Satisfy Curiosity

Statistics is a science. It involves asking questions about the world and finding answers to them in a scientific way. If you are curious about how things work, statistics is a career that will keep your curiosity piqued and your brain angaged

Make Money

Demand for statisticians and data scientists is growing, and so are their salaries. The medias salary for statistician is $92,030, according to 2019

‘Statistician is the coolest job you’ve never heard of. Don’t take
our word for it; see for yourself all the great things you can do with a career in statistics.’

‘Regardless of the field that you pursue, you’ve got to know
statistics.’ - Deepak Kumar, LinkedIn Principal Data Scientist.

‘Statistics is for Adrenaline Junkies’ - Elizabeth J. Kelly,
Statistician at the Los Alamos National Laboratory, and elected
fellow of the American Statistical Association.

Takeaway Message:
Statistics is not just about numbers− it is the foundation of modern engineering, science, and finance, enabling innovation, safety, and discovery.