als-statistics/docs/01-first-steps/02-quick-starts.md

Modular JS statistics toolkit for Node.js and the browser: descriptive stats, correlations (Pearson/Spearman/Kendall), t-tests & ANOVA (Student/Welch), reliability (Cronbach’s alpha), regression (linear/logistic), clustering (DBSCAN/HDBSCAN), and table/co

## Quick starts

### 1) Use it like `Math` (one-liners)

```js
import { Stats } from 'als-statistics';

const X = [10, 12, 13, 9, 14];

const mu  = Stats.mean(X);
const sd  = Stats.stdDevSample(X);
const p90 = Stats.p90(X);

console.log({ mu, sd, p90 });
// → { mu: 11.6, sd: 1.923..., p90: 13.8 }
```

You can also access many metrics via `Column`:

```js
import { Column } from 'als-statistics';

const col = new Column([10, 12, 13, 9, 14], 'Score');
const { mean, stdDev, median, frequencies, flatness } = col;
```

### 2) Quick analysis: correlation in one line

```js
import { Analyze } from 'als-statistics';

const data = {
  gender: [0, 1, 0, 1, 1, 0], // 0=female, 1=male
  score:  [62, 75, 70, 81, 64, 78],
};

const pearson = new Analyze.Correlate(data).pearson('gender', 'score');
const { r, t, df, p } = pearson;

console.log({ r, t, df, p });
// r in [-1, 1], two-sided p-value in [0, 1]
```

### 3) Compare means: Welch t-test (unequal variances)

```js
import { Analyze } from 'als-statistics';

const data = {
  men:   [62, 75, 70, 81, 64],
  women: [78, 73, 69, 71, 74, 77],
};

const test = new Analyze.CompareMeans(data).independentWelch('men', 'women');
console.log({ t: test.t, df: test.df, p: test.p });
```

### 4) One-way ANOVA (classic & Welch)

```js
import { Analyze } from 'als-statistics';
const { CompareMeans } = Analyze;

const data = {
  A: [10, 11,  9, 10],
  B: [10, 30, -10, 50, -20],
  C: [12, 13, 12, 11, 14],
};

const classic = new CompareMeans(data).anova();       // pooled (equal variances)
const welch   = new CompareMeans(data).anovaWelch();  // unequal variances

console.log({
  classic: { F: classic.F, df1: classic.dfBetween, df2: classic.dfWithin, p: classic.p },
  welch:   { F: welch.F,   df1: welch.dfBetween,   df2: welch.dfWithin,   p: welch.p },
});
```

### 5) Table-first workflow (filter → split → analyze)

```js
import { Table } from 'als-statistics';

const t = new Table(
  { gender: [0,1,0,1,1,0], age: [21,22,20,23,19,22], score: [62,75,70,81,64,78] },
  { name: 'Survey' }
);

// Keep adults 21+
t.filterRowsBy('age', a => a >= 21);

// Compare score by gender with Welch
// Option A: already split into columns:
import { Analyze } from 'als-statistics';
const { CompareMeans } = Analyze;

const cm = new CompareMeans({ men: [...], women: [...] }).independentWelch('men', 'women');

// Option B: split first, then pass to CompareMeans:
const groups = t.splitBy('gender'); // returns { groupName: number[] }
const test = new CompareMeans(groups).independentWelch('0', '1');
```

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