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821 lines (747 loc) Β· 30.6 KB
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Statistics Fundamentals Glossary</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=DM+Serif+Display:ital@0;1&family=Outfit:wght@300;400;500;600;700&family=JetBrains+Mono:wght@300;400;500;700&display=swap" rel="stylesheet">
<style>
:root {
--bg: #08090d;
--bg2: #0e1018;
--bg3: #14161f;
--bg4: #1c1f2e;
--lime: #c8ff00;
--lime2: #a8d600;
--lime-dim: rgba(200,255,0,0.08);
--lime-glow:rgba(200,255,0,0.18);
--white: #f0f2f8;
--grey: #8b90a8;
--grey2: #4a4f68;
--border: rgba(255,255,255,0.07);
--border2: rgba(255,255,255,0.12);
--disp: 'DM Serif Display', Georgia, serif;
--body: 'Outfit', sans-serif;
--mono: 'JetBrains Mono', monospace;
}
*, *::before, *::after { margin:0; padding:0; box-sizing:border-box; }
html { scroll-behavior: smooth; }
/* custom scrollbar */
::-webkit-scrollbar { width: 4px; height: 4px; }
::-webkit-scrollbar-track { background: var(--bg2); }
::-webkit-scrollbar-thumb { background: var(--grey2); border-radius: 4px; }
body {
font-family: var(--body);
background: var(--bg);
color: var(--white);
min-height: 100vh;
overflow-x: hidden;
}
/* ββ subtle grid bg ββ */
body::before {
content: '';
position: fixed; inset: 0;
background-image:
linear-gradient(rgba(200,255,0,0.025) 1px, transparent 1px),
linear-gradient(90deg, rgba(200,255,0,0.025) 1px, transparent 1px);
background-size: 60px 60px;
pointer-events: none;
z-index: 0;
}
/* ββ TOP BANNER ββ */
.banner {
position: fixed;
top: 0; left: 0; right: 0;
z-index: 200;
height: 52px;
background: rgba(8,9,13,0.92);
backdrop-filter: blur(20px);
border-bottom: 1px solid var(--border);
display: flex;
align-items: center;
padding: 0 40px;
gap: 24px;
}
.banner-logo {
font-family: var(--mono);
font-size: 0.72rem;
font-weight: 700;
letter-spacing: 0.18em;
text-transform: uppercase;
color: var(--lime);
white-space: nowrap;
flex-shrink: 0;
}
.banner-sep { width: 1px; height: 20px; background: var(--border2); flex-shrink:0; }
.search-wrap {
flex: 1;
max-width: 460px;
position: relative;
display: flex;
align-items: center;
}
.search-icon {
position: absolute;
left: 0;
color: var(--grey);
width: 16px; height: 16px;
pointer-events: none;
flex-shrink: 0;
}
#searchInput {
width: 100%;
background: transparent;
border: none;
border-bottom: 1px solid var(--grey2);
padding: 6px 0 6px 26px;
font-family: var(--mono);
font-size: 0.8rem;
color: var(--white);
outline: none;
transition: border-color .2s;
}
#searchInput::placeholder { color: var(--grey2); }
#searchInput:focus { border-bottom-color: var(--lime); }
.banner-actions {
display: flex;
align-items: center;
gap: 10px;
margin-left: auto;
flex-shrink: 0;
}
.banner-stat {
font-family: var(--mono);
font-size: 0.68rem;
color: var(--grey);
display: flex;
align-items: center;
gap: 5px;
}
.banner-stat strong {
font-weight: 700;
color: var(--lime);
}
.reset-btn {
font-family: var(--mono);
font-size: 0.65rem;
font-weight: 500;
letter-spacing: 0.1em;
text-transform: uppercase;
padding: 6px 14px;
background: transparent;
border: 1px solid var(--grey2);
border-radius: 4px;
color: var(--grey);
cursor: pointer;
transition: all .15s;
}
.reset-btn:hover { border-color: var(--lime); color: var(--lime); }
/* ββ PAGE LAYOUT ββ */
.page {
position: relative;
z-index: 1;
display: flex;
min-height: 100vh;
padding-top: 52px;
}
/* ββ LEFT SIDEBAR: LETTER NAV ββ */
.letter-nav {
position: fixed;
top: 52px;
left: 0;
width: 64px;
height: calc(100vh - 52px);
background: var(--bg2);
border-right: 1px solid var(--border);
display: flex;
flex-direction: column;
align-items: center;
padding: 20px 0;
gap: 2px;
overflow-y: auto;
z-index: 100;
scrollbar-width: none;
}
.letter-nav::-webkit-scrollbar { display:none; }
.lnav-btn {
width: 40px; height: 36px;
display: flex; align-items: center; justify-content: center;
font-family: var(--mono);
font-size: 0.72rem;
font-weight: 700;
color: var(--grey2);
background: transparent;
border: none;
border-radius: 6px;
cursor: pointer;
transition: all .14s;
letter-spacing: 0.04em;
}
.lnav-btn:hover:not(.no-terms) {
background: var(--lime-dim);
color: var(--lime);
}
.lnav-btn.active {
background: var(--lime);
color: var(--bg);
box-shadow: 0 0 12px var(--lime-glow);
}
.lnav-btn.no-terms { opacity: 0.2; cursor: default; }
.lnav-all {
font-size: 0.58rem;
letter-spacing: 0.08em;
width: 40px; height: 28px;
margin-bottom: 10px;
}
.lnav-sep {
width: 24px; height: 1px;
background: var(--border);
margin: 6px 0;
flex-shrink: 0;
}
/* ββ MAIN CONTENT ββ */
.main {
margin-left: 64px;
flex: 1;
max-width: 100%;
}
/* ββ HERO ββ */
.hero {
padding: 72px 64px 60px;
border-bottom: 1px solid var(--border);
position: relative;
overflow: hidden;
}
.hero-bg-text {
position: absolute;
bottom: -30px;
right: -20px;
font-family: var(--disp);
font-size: clamp(120px, 18vw, 220px);
font-weight: 400;
line-height: 1;
color: transparent;
-webkit-text-stroke: 1px rgba(200,255,0,0.06);
pointer-events: none;
user-select: none;
letter-spacing: -0.05em;
}
.hero-kicker {
font-family: var(--mono);
font-size: 0.68rem;
font-weight: 500;
letter-spacing: 0.22em;
text-transform: uppercase;
color: var(--lime);
margin-bottom: 20px;
display: flex;
align-items: center;
gap: 12px;
}
.hero-kicker::before {
content: '';
display: block;
width: 32px; height: 1px;
background: var(--lime);
}
.hero-title {
font-family: var(--disp);
font-size: clamp(3rem, 6vw, 5.5rem);
font-weight: 400;
line-height: 1.0;
letter-spacing: -0.02em;
color: var(--white);
margin-bottom: 8px;
}
.hero-title em {
font-style: italic;
color: var(--lime);
}
.hero-subtitle {
font-size: 1rem;
color: var(--grey);
font-weight: 300;
margin-bottom: 36px;
max-width: 560px;
line-height: 1.6;
}
.hero-chips {
display: flex;
gap: 10px;
flex-wrap: wrap;
}
.chip {
display: flex;
align-items: center;
gap: 8px;
padding: 8px 16px;
border: 1px solid var(--border2);
border-radius: 100px;
font-family: var(--mono);
font-size: 0.68rem;
color: var(--grey);
background: var(--bg2);
}
.chip-dot {
width: 6px; height: 6px;
border-radius: 50%;
background: var(--lime);
box-shadow: 0 0 6px var(--lime);
flex-shrink: 0;
}
.chip strong { color: var(--white); font-weight: 600; }
/* ββ LETTER SECTION GROUP ββ */
.terms-area {
padding: 0 64px 80px;
}
.letter-group {
margin-top: 56px;
animation: groupIn .35s ease both;
}
@keyframes groupIn {
from { opacity:0; transform: translateY(16px); }
to { opacity:1; transform: translateY(0); }
}
.group-header {
display: flex;
align-items: flex-end;
gap: 20px;
margin-bottom: 20px;
padding-bottom: 16px;
border-bottom: 1px solid var(--border);
position: relative;
}
.group-letter {
font-family: var(--disp);
font-size: 4.5rem;
font-weight: 400;
line-height: 1;
color: var(--lime);
letter-spacing: -0.03em;
flex-shrink: 0;
}
.group-meta {
padding-bottom: 8px;
}
.group-label {
font-family: var(--mono);
font-size: 0.62rem;
letter-spacing: 0.16em;
text-transform: uppercase;
color: var(--grey2);
margin-bottom: 4px;
}
.group-count {
font-family: var(--body);
font-size: 0.88rem;
color: var(--grey);
font-weight: 300;
}
/* ββ TERM CARDS ββ */
.terms-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(420px, 1fr));
gap: 12px;
}
.term-card {
background: var(--bg3);
border: 1px solid var(--border);
border-radius: 10px;
padding: 22px 24px;
position: relative;
overflow: hidden;
transition: border-color .2s, background .2s, transform .2s, box-shadow .2s;
animation: cardIn .3s ease both;
}
@keyframes cardIn {
from { opacity:0; transform: translateY(8px); }
to { opacity:1; transform: translateY(0); }
}
.term-card::before {
content: '';
position: absolute;
left: 0; top: 0; bottom: 0;
width: 3px;
background: var(--lime);
opacity: 0;
transition: opacity .2s;
}
.term-card:hover {
border-color: rgba(200,255,0,0.25);
background: var(--bg4);
transform: translateY(-2px);
box-shadow: 0 8px 32px rgba(0,0,0,.4), 0 0 0 1px rgba(200,255,0,0.1);
}
.term-card:hover::before { opacity: 1; }
.tc-watermark {
position: absolute;
right: 14px;
top: 50%;
transform: translateY(-50%);
font-family: var(--disp);
font-size: 5rem;
font-weight: 400;
line-height: 1;
color: rgba(200,255,0,0.04);
pointer-events: none;
user-select: none;
transition: color .2s;
}
.term-card:hover .tc-watermark { color: rgba(200,255,0,0.07); }
.tc-term {
font-family: var(--disp);
font-size: 1.25rem;
font-weight: 400;
color: var(--white);
margin-bottom: 10px;
line-height: 1.2;
position: relative;
}
.tc-def {
font-family: var(--body);
font-size: 0.88rem;
color: var(--grey);
line-height: 1.75;
font-weight: 300;
position: relative;
}
/* search highlight */
mark.hl {
background: rgba(200,255,0,0.22);
color: var(--lime);
border-radius: 2px;
padding: 0 2px;
}
/* ββ EMPTY STATE ββ */
.empty {
text-align: center;
padding: 100px 40px;
color: var(--grey2);
}
.empty-char {
font-family: var(--disp);
font-size: 8rem;
font-weight: 400;
color: transparent;
-webkit-text-stroke: 1px var(--border2);
display: block;
margin-bottom: 20px;
line-height: 1;
}
.empty h3 {
font-family: var(--disp);
font-size: 1.5rem;
color: var(--grey);
margin-bottom: 8px;
}
.empty p {
font-size: 0.88rem;
font-weight: 300;
color: var(--grey2);
}
/* ββ FOOTER ββ */
footer {
margin-left: 64px;
border-top: 1px solid var(--border);
padding: 32px 64px;
display: flex;
align-items: center;
justify-content: space-between;
flex-wrap: wrap;
gap: 16px;
}
.footer-brand {
font-family: var(--disp);
font-size: 1.2rem;
color: var(--white);
}
.footer-brand em { font-style: italic; color: var(--lime); }
.footer-info {
font-family: var(--mono);
font-size: 0.68rem;
color: var(--grey2);
letter-spacing: 0.08em;
text-align: right;
line-height: 1.8;
}
/* ββ RESPONSIVE ββ */
@media (max-width: 860px) {
.letter-nav { display: none; }
.main, footer { margin-left: 0; }
.hero { padding: 48px 24px 40px; }
.terms-area { padding: 0 24px 60px; }
.terms-grid { grid-template-columns: 1fr; }
.banner { padding: 0 20px; }
.banner-stat { display: none; }
footer { padding: 24px; }
footer { margin-left: 0; }
}
@media (max-width: 500px) {
.hero-title { font-size: 2.4rem; }
}
</style>
</head>
<body>
<!-- TOP BANNER -->
<div class="banner">
<div class="banner-logo">DSA Β· Glossary</div>
<div class="banner-sep"></div>
<div class="search-wrap">
<svg class="search-icon" viewBox="0 0 20 20" fill="none" stroke="currentColor" stroke-width="2">
<circle cx="8.5" cy="8.5" r="5.5"/><path d="M15 15l-3-3"/>
</svg>
<input id="searchInput" type="text" placeholder="Search terms or definitionsβ¦" autocomplete="off">
</div>
<div class="banner-actions">
<div class="banner-stat">Showing <strong id="bCount">64</strong> of <strong id="bTotal">64</strong></div>
<button class="reset-btn" id="resetBtn">βΊ Reset</button>
</div>
</div>
<div class="page">
<!-- LEFT LETTER NAV -->
<nav class="letter-nav" id="letterNav">
<button class="lnav-btn lnav-all active" data-l="all">ALL</button>
<div class="lnav-sep"></div>
<!-- injected A-Z -->
</nav>
<!-- MAIN -->
<div class="main">
<!-- HERO -->
<section class="hero">
<div class="hero-bg-text">Ξ£</div>
<div class="hero-kicker">Data Science Academy</div>
<h1 class="hero-title">Statistics<br><em>Fundamentals</em></h1>
<p class="hero-subtitle">An alphabetised reference of essential statistics terms for data professionals, analysts, and students.</p>
<div class="hero-chips">
<div class="chip"><div class="chip-dot"></div><strong id="chipCount">64</strong> terms defined</div>
<div class="chip"><div class="chip-dot"></div>β± ~7 min read</div>
<div class="chip"><div class="chip-dot"></div>Author: <strong>Willie Conway</strong></div>
</div>
</section>
<!-- TERMS -->
<div class="terms-area" id="termsArea">
<!-- injected dynamically -->
</div>
</div>
</div>
<!-- FOOTER -->
<footer>
<div class="footer-brand">Data<em>Science</em> Academy</div>
<div class="footer-info">
Statistics Fundamentals using Excel<br>
Author: Willie Conway
</div>
</footer>
<script>
// βββββββββββββββββββββββββββββββββββββββββββ
// DATA
// βββββββββββββββββββββββββββββββββββββββββββ
const TERMS = [
{ term:"Alpha value", def:"A threshold of statistical significance used in hypothesis testing β commonly set to 0.05, meaning a 5% risk of concluding a difference exists when it does not." },
{ term:"Average", def:"The sum of all values divided by the count of values. Also called the arithmetic mean. Sensitive to outliers." },
{ term:"Bar chart", def:"A pictorial representation using rectangular bars whose length is proportional to the value being measured. Bars can be vertical or horizontal and are ideal for comparing discrete categories." },
{ term:"Big data", def:"Extremely large and diverse datasets characterised by high volume, velocity, and variety that exceed the processing capacity of conventional tools." },
{ term:"Binary variable", def:"A variable with exactly two possible values, such as 0/1, yes/no, or true/false. A special case of both categorical and dichotomous variables." },
{ term:"Boxplot", def:"A graphical summary showing the median, quartiles, and outliers of a dataset. The box spans the IQR (Q3 β Q1); whiskers extend to non-outlier extremes." },
{ term:"Bubble chart", def:"A scatter plot extended with a third variable encoded as the size of each circle, enabling three-dimensional relationships to be visualised in two dimensions." },
{ term:"Business analyst", def:"A data professional who bridges business objectives and technical solutions β gathering requirements, conducting feasibility studies, and recommending process improvements." },
{ term:"Categorical variable", def:"A variable that assigns each observation to a named group based on a qualitative property. Subcategories include nominal (unordered) and ordinal (ordered) variables." },
{ term:"Counterplot", def:"A plot counting the number of times each category appears in a categorical variable, providing a visual frequency distribution for qualitative data." },
{ term:"Cross-sectional data", def:"Observations collected from multiple subjects at a single point in time. Contrasts with time-series data, which tracks the same subject over time." },
{ term:"Cumulative distribution function", def:"A function F(x) giving the probability that a random variable X takes a value β€ x. It rises from 0 to 1 as x increases and fully describes the distribution." },
{ term:"Data science", def:"An interdisciplinary field combining statistics, computer science, and domain expertise to extract meaningful insights and predictive models from data." },
{ term:"Delphi method", def:"A structured forecasting technique that iteratively collects and refines expert opinions through anonymous rounds until consensus is reached." },
{ term:"Dependent variable", def:"The outcome variable in a study β the one that may change in response to manipulation of the independent variable. Often denoted Y in regression models." },
{ term:"Descriptive statistics", def:"Methods for summarising and describing a dataset through numerical measures (mean, variance) or visual displays (histograms, boxplots) without inferring about a larger population." },
{ term:"Dichotomous variable", def:"A variable that takes exactly two mutually exclusive values, such as pass/fail or alive/dead." },
{ term:"Dispersion", def:"The degree to which data values spread around a central value. Common measures include range, variance, standard deviation, and IQR." },
{ term:"Endogeneity", def:"A problem in regression where an independent variable is correlated with the error term, leading to biased and inconsistent coefficient estimates." },
{ term:"F-statistic", def:"A ratio of two variances used in ANOVA and regression F-tests to assess whether group means differ or whether the overall model is statistically significant." },
{ term:"Histogram", def:"A bar chart for continuous data where values are grouped into equal-width bins. Bar height represents frequency or density, revealing the shape of a distribution." },
{ term:"Homoscedasticity", def:"A regression assumption where the variance of residuals is constant across all levels of the predictor. Its violation is called heteroscedasticity." },
{ term:"Hypothesis", def:"A testable, falsifiable statement about a population parameter or the relationship between variables, formulated before data collection." },
{ term:"Hypothesis test", def:"A formal procedure for deciding whether sample data provide sufficient evidence to reject a null hypothesis in favour of an alternative." },
{ term:"Independent variable", def:"A predictor or explanatory variable whose variation may explain or cause changes in the dependent variable. Denoted X in regression models." },
{ term:"Linear regression", def:"A model estimating the linear relationship between a continuous dependent variable and one or more independent variables by minimising the sum of squared residuals." },
{ term:"Linearity", def:"A relationship where the change in the dependent variable is proportional to the change in the independent variable β the core assumption of linear regression." },
{ term:"Logistic regression", def:"A classification model estimating the probability of a binary outcome using a logistic function applied to a linear combination of predictors." },
{ term:"Mean absolute error (MAE)", def:"The average of the absolute differences between predicted and actual values. Less sensitive to outliers than MSE and expressed in the same units as the outcome." },
{ term:"Median", def:"The middle value when observations are sorted. Robust to outliers; preferred over the mean for skewed distributions." },
{ term:"Mode", def:"The most frequently occurring value in a dataset. A distribution can be unimodal, bimodal, or multimodal. The only average applicable to nominal data." },
{ term:"Multicollinearity", def:"High correlation among independent variables in a regression model, inflating standard errors and making coefficient estimates unstable." },
{ term:"Multiple linear regression", def:"An extension of simple linear regression modelling the relationship between a dependent variable and two or more independent variables simultaneously." },
{ term:"Multivariate dataset", def:"A dataset containing two or more variables per observation, enabling analysis of relationships and interactions among multiple features." },
{ term:"Nominal variable", def:"A categorical variable whose categories have no inherent order β for example, country, colour, or blood type." },
{ term:"Normal distribution", def:"A symmetric, bell-shaped probability distribution defined by mean ΞΌ and standard deviation Ο. About 68% of values fall within Β±1Ο of the mean." },
{ term:"Normal distribution curve", def:"The bell-shaped probability density curve of the normal distribution, symmetric about the mean, where most observations cluster near the centre." },
{ term:"Null hypothesis", def:"The default assumption in a hypothesis test β usually no effect, no difference, or no relationship. Denoted Hβ. It is either rejected or not rejected." },
{ term:"Overfitting", def:"A modelling error where a model learns training data too precisely (including noise), giving excellent in-sample fit but poor generalisation to new data." },
{ term:"Pie chart", def:"A circular graphic divided into slices proportional to each category's share. Best for part-to-whole relationships with a small number of categories." },
{ term:"Polynomial regression", def:"A regression variant where the relationship between Y and X is modelled as an nth-degree polynomial, enabling curved (nonlinear) fits." },
{ term:"Probability", def:"A number between 0 and 1 expressing the likelihood of an event. P = 0 means impossible; P = 1 means certain. Governed by the axioms of probability theory." },
{ term:"P-value", def:"The probability of obtaining results at least as extreme as those observed, assuming Hβ is true. A small p-value (e.g. < 0.05) is evidence against Hβ." },
{ term:"Qualitative forecasting", def:"Prediction methods relying on expert judgment and structured opinion β such as the Delphi method β rather than historical numerical data." },
{ term:"Quantitative forecasting", def:"Prediction methods using historical numerical data and statistical models β such as time series or regression β to project future values." },
{ term:"Quantitative relationship", def:"A relationship between variables expressed in measurable numerical terms, enabling equations and statistical models to describe it precisely." },
{ term:"Regression analysis", def:"A set of statistical methods for estimating the relationship between a dependent variable and one or more independent variables and using it for prediction." },
{ term:"Regression model", def:"The mathematical equation β usually Y = Ξ²β + Ξ²βX + Ξ΅ β representing the estimated relationship between dependent and independent variables." },
{ term:"R-squared", def:"The proportion of variance in the dependent variable explained by the independent variable(s), ranging from 0 to 1. Also called the coefficient of determination." },
{ term:"Scatter plot", def:"A two-dimensional graph plotting paired (x, y) observations to reveal the direction, strength, and form of the relationship between two continuous variables." },
{ term:"Simple linear regression", def:"A regression model with exactly one independent variable, producing the best-fit line Y = Ξ²β + Ξ²βX + Ξ΅ through the data." },
{ term:"Skewness", def:"A measure of asymmetry in a distribution. Positive skew means the right tail is longer; negative skew means the left tail is longer." },
{ term:"Standard deviation", def:"The square root of variance β a measure of the average distance of observations from the mean, expressed in the same units as the data." },
{ term:"Standard error", def:"The standard deviation of a sampling distribution, typically of the sample mean. Measures how much a statistic varies from sample to sample." },
{ term:"Statistical analysis", def:"The process of collecting, cleaning, exploring, modelling, and interpreting data to uncover patterns, test hypotheses, and support decisions." },
{ term:"Statistical parameter", def:"A numerical characteristic of a population β such as the mean (ΞΌ) or standard deviation (Ο) β as opposed to a sample statistic estimated from data." },
{ term:"Statistical tools", def:"Software and libraries β such as Excel, R, Python (pandas, scipy), SAS, and SPSS β used to apply statistical methods." },
{ term:"Statistics", def:"The science of collecting, analysing, interpreting, and presenting data to understand variability, draw inferences, and support decisions." },
{ term:"Symmetrical distribution", def:"A distribution where the left and right halves are mirror images, causing the mean, median, and mode to coincide." },
{ term:"T distribution", def:"A bell-shaped probability distribution used when the population standard deviation is unknown and sample sizes are small. Has wider tails than the normal distribution." },
{ term:"Underfitting", def:"A modelling error where a model is too simple to capture underlying structure, resulting in high error on both training and test data." },
{ term:"Univariate dataset", def:"A dataset containing measurements on a single variable per observation. Analysed with frequency distributions, histograms, and summary statistics." },
{ term:"Variance inflation factors (VIF)", def:"A diagnostic quantifying how much a regression coefficient's variance is inflated due to multicollinearity. VIF > 10 typically signals a problem." },
{ term:"Z score", def:"A standardised measure expressing how many standard deviations an observation is from the mean: z = (x β ΞΌ) / Ο. Enables comparison across scales." }
];
TERMS.sort((a,b) => a.term.localeCompare(b.term));
// βββββββββββββββββββββββββββββββββββββββββββ
// STATE
// βββββββββββββββββββββββββββββββββββββββββββ
const S = { q:'', letter:'all' };
// βββββββββββββββββββββββββββββββββββββββββββ
// UTILS
// βββββββββββββββββββββββββββββββββββββββββββ
function esc(s){
return String(s)
.replace(/&/g,'&')
.replace(/</g,'<')
.replace(/>/g,'>');
}
function highlight(text, q){
if(!q) return esc(text);
const re = new RegExp(`(${q.replace(/[.*+?^${}()|[\]\\]/g,'\\$&')})`, 'gi');
return esc(text).replace(re, '<mark class="hl">$1</mark>');
}
// βββββββββββββββββββββββββββββββββββββββββββ
// LETTER NAV
// βββββββββββββββββββββββββββββββββββββββββββ
function buildLetterNav(){
const nav = document.getElementById('letterNav');
const present = new Set(TERMS.map(t => t.term[0].toUpperCase()));
// All btn already in HTML
nav.querySelector('[data-l="all"]').addEventListener('click', () => setLetter('all'));
'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.split('').forEach(l => {
const btn = document.createElement('button');
btn.className = 'lnav-btn' + (!present.has(l) ? ' no-terms' : '');
btn.dataset.l = l;
btn.textContent = l;
if(present.has(l)) btn.addEventListener('click', () => setLetter(l));
nav.appendChild(btn);
});
}
function setLetter(l){
S.letter = l;
S.q = '';
document.getElementById('searchInput').value = '';
updateNavActive();
render();
}
function updateNavActive(){
document.querySelectorAll('.lnav-btn').forEach(b => {
b.classList.toggle('active', b.dataset.l === S.letter);
});
}
// βββββββββββββββββββββββββββββββββββββββββββ
// FILTER
// βββββββββββββββββββββββββββββββββββββββββββ
function getFiltered(){
const q = S.q.toLowerCase();
return TERMS.filter(t => {
const ml = S.letter === 'all' || t.term[0].toUpperCase() === S.letter;
const mq = !q || t.term.toLowerCase().includes(q) || t.def.toLowerCase().includes(q);
return ml && mq;
});
}
// βββββββββββββββββββββββββββββββββββββββββββ
// RENDER
// βββββββββββββββββββββββββββββββββββββββββββ
function render(){
const items = getFiltered();
const area = document.getElementById('termsArea');
const showGroups = S.letter === 'all' && !S.q;
// update counts
document.getElementById('bCount').textContent = items.length;
document.getElementById('bTotal').textContent = TERMS.length;
document.getElementById('chipCount').textContent = TERMS.length;
if(!items.length){
area.innerHTML = `<div class="empty">
<span class="empty-char">β
</span>
<h3>No matching terms</h3>
<p>Try different keywords or browse by letter.</p>
</div>`;
return;
}
// group by letter
const groups = {};
items.forEach(t => {
const l = t.term[0].toUpperCase();
(groups[l] = groups[l]||[]).push(t);
});
let html = '';
Object.keys(groups).sort().forEach(l => {
html += `<div class="letter-group" id="grp-${l}">`;
if(showGroups){
html += `<div class="group-header">
<div class="group-letter">${l}</div>
<div class="group-meta">
<div class="group-label">Letter group</div>
<div class="group-count">${groups[l].length} term${groups[l].length !== 1 ? 's' : ''}</div>
</div>
</div>`;
}
html += `<div class="terms-grid">`;
groups[l].forEach((t, i) => {
html += `<div class="term-card" style="animation-delay:${i * 0.03}s">
<div class="tc-watermark">${l}</div>
<div class="tc-term">${highlight(t.term, S.q)}</div>
<div class="tc-def">${highlight(t.def, S.q)}</div>
</div>`;
});
html += `</div></div>`;
});
area.innerHTML = html;
}
// βββββββββββββββββββββββββββββββββββββββββββ
// EVENTS
// βββββββββββββββββββββββββββββββββββββββββββ
document.getElementById('searchInput').addEventListener('input', e => {
S.q = e.target.value.trim();
S.letter = 'all';
updateNavActive();
render();
});
document.getElementById('resetBtn').addEventListener('click', () => {
S.q = ''; S.letter = 'all';
document.getElementById('searchInput').value = '';
updateNavActive();
render();
});
document.addEventListener('keydown', e => {
if((e.ctrlKey||e.metaKey) && e.key === 'f'){
e.preventDefault();
document.getElementById('searchInput').focus();
}
if(e.key === 'Escape'){
S.q = '';
document.getElementById('searchInput').value = '';
render();
}
});
// βββββββββββββββββββββββββββββββββββββββββββ
// BOOT
// βββββββββββββββββββββββββββββββββββββββββββ
buildLetterNav();
render();
</script>
</body>
</html>