## Practical patterns

### A. Pipeline “sort → split → test”

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

// sort by score, keep top 100 rows, split by gender, compare means
const t = new Table(data).sortBy('score', false);
const top = t.clone('Top').filterRows([...Array(100).keys()]); // keep first 100 indices
const groups = top.splitBy('gender'); // returns small structure per group

const cm = new CompareMeans(groups);
const res = cm.independentWelch('0','1');
console.log(res.p < 0.05 ? 'Significant' : 'NS');
```

### B. Correlations with filters

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

const t = new Table(data);
t.filterRowsBy('age', a => a >= 25 && a <= 40);
const corr = t.correlate('height','weight').pearson();
console.log(corr.r, corr.p);
```

### C. Quick reliability check

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

const items = { Q1: [...], Q2: [...], Q3: [...], Q4: [...] };
const alpha = new Analyze.Correlate.CronbachAlpha(items);
console.log(alpha.alpha, alpha.htmlTable);
```

### D. Minimal regression report

```js
import { Analyze } from 'als-statistics';
const reg = new Analyze.Regression(dataset, { yName: 'y', xNames: ['x1','x2'], type: 'linear' });
// step 1
reg.steps[0].calculate();
console.log(reg.steps[0].result); // table-like object for reporting
```

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