QuantLab
Interactive tools for hands-on probability, statistics, and financial modeling.
Fundamental Tools
Interactive guide to mean, median, skewness, and kurtosis.
Discover how order emerges from chaos.
Understanding the range where a true value likely lies.
Calculate probabilities from Z-scores and vice-versa.
A framework for updating beliefs with new evidence.
Discrete Distributions
Modeling a single trial with two outcomes.
Modeling a series of success/fail trials.
Modeling the frequency of rare events.
Modeling trials until the first success.
Modeling sampling without replacement.
Modeling trials until a set number of successes.
Modeling where all outcomes are equally likely.
Generalizing the Binomial for multiple outcomes.
Continuous Distributions
The ubiquitous "bell curve."
Modeling waiting times and skewed data.
Modeling probabilities, percentages, and proportions.
Modeling the time between events in a Poisson process.
Modeling extreme events and 'fat-tailed' phenomena.
Modeling with a sharp peak and 'fat tails'.
Modeling time-to-failure and event durations.
A key distribution in machine learning and growth modeling.
Statistics Tools
A comprehensive guide to choosing the right statistical test.
Compares the means of two groups, assuming normal distribution.
Compares means of large samples (n>30) with known population variance.
Compares the averages of three or more groups.
Compares the variances (spread) of two or more groups.
Measures the linear relationship between two continuous variables.
Analyzes categorical data to find significant relationships.
Alternative to the T-Test when data is not normally distributed.
Alternative to ANOVA for comparing three or more groups.
Alternative to the paired T-Test for repeated measurements.
Measures the monotonic relationship between two ranked variables.
The non-parametric alternative to a repeated-measures ANOVA.
Tests if a sample is drawn from a specific distribution.
The detective work of data science.
Using random simulation to solve complex problems.
Breaking down a time series into its core components.
Measuring how a time series correlates with its past values.
Modeling the changing volatility of financial returns.
Finding the optimal portfolio for a given level of risk.
Dynamically estimating the state of a system from noisy data.
The calculus of random walks, essential for derivatives pricing.