This Vue.js application serves as a binomial probability calculator, allowing users to input parameters like number of trials (n), probability (p), and successes (x) to compute and display probability distributions in a dynamic table. While the math behind binomial distributions is fascinating, the real focus here is Vue’s power in creating simple, single-purpose tools and widgets. Vue’s reactive data binding and declarative templates make it effortless to build interactive utilities without complex setups, enabling quick prototyping of calculators, converters, or data visualizers.
This interactive tool combines Vue.js for user inputs and probability calculations with Desmos graphing to visualize the normal distribution bell curve. Users enter a Z-score, and the app computes the cumulative probability using a custom Gaussian distribution library. Clicking “Update Graph” dynamically shades the area under the curve in Desmos, illustrating concepts like one-sided probabilities. The implementation focuses on JavaScript integration for real-time updates and Desmos’ API for embedding interactive math visualizations.
The Elo rating system is a method for calculating the relative skill levels of players in competitive games, particularly chess. It adjusts ratings based on game outcomes, rewarding wins against stronger opponents and penalizing losses to weaker ones. This calculator demonstrates Elo calculations, allowing users to input current ratings and game results to see rating changes. Elo is a form of pairwise comparison, evaluating players in head-to-head matchups. Beyond chess, it’s used in esports, online gaming platforms, and even non-gaming contexts like ranking algorithms. The widget’s simplicity highlights how binding frameworks like Angular or Vue enable quick development of interactive tools with minimal code.
I built an interactive bell curve visualization using Google Charts API to display a normal distribution with customizable shaded regions. It leverages the jStat library for probability density calculations, allowing users to adjust mean, standard deviation, and shading bounds dynamically. The chart renders as an area chart with opacity styling for shading, providing a clear visual representation of statistical concepts like confidence intervals or hypothesis testing areas.