# 040. Superplasticizer and water demand

**Question:** How does water content differ across observed superplasticizer dosage bands?

## Result

The largest computed metric is 192 for (-0.001, 0.0]; the smallest is 153.5 for (15.0, 40.0]. Metric: median_water (kg per cubic metre).

Mixtures were not randomly assigned to dosage bands. The source cannot establish optimal dosage.

![Superplasticizer and water demand](outputs/chart.png)

| dosage_band | samples | median_water | median_strength |
| --- | --- | --- | --- |
| (-0.001, 0.0] | 379 | 192 | 28.02 |
| (0.0, 5.0] | 63 | 190 | 32.72 |
| (10.0, 15.0] | 207 | 160.7 | 40.87 |
| (15.0, 40.0] | 61 | 153.5 | 52.2 |
| (5.0, 10.0] | 320 | 178.1 | 35.23 |

The chart shows 5 of 5 result rows; the table previews the first 5 in the analysis-defined order. [Download the full result table](outputs/results.csv). Numerical values are computed from the source; missing results stay unavailable.

## Method

Dosage groups with water and strength summaries.

The study uses shared source preparation and reusable statistical routines. Its specific transformations are in [analysis.py](analysis.py), and common model/evaluation code is in [portfolio/methods.py](../../portfolio/methods.py). The [notebook](analysis.ipynb) executes the study and displays the saved results.

## Decision and limitations

Design matched laboratory comparisons before inferring a dosage effect.

Laboratory observations do not certify a construction mix. Composition and curing conditions are confounded. Models require independent engineering validation and cannot replace strength testing. 

Related studies may share observations or holdouts. These are focused analytical studies, not independent replications or deployed business systems. Any model refinements informed by these results need new untouched evaluation data. No commercial impact is inferred from an association or backtest.

## Reproduce

From the repository root, after installing `requirements.txt`:

```powershell
python projects/040-superplasticizer-and-water-demand/analysis.py
```

Source data are downloaded automatically if absent. Original archives are retained unchanged and checked by SHA-256. The cleaned cache normalizes column names; field-specific changes are visible in [data preparation](../../portfolio/data.py). Runtime evidence is in [receipt.json](outputs/receipt.json).

## Source

[Concrete Compressive Strength](https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength), Yeh (1998). [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Source data are transformed and aggregated in this study. [Source provenance](../../data/provenance/concrete.json) and [prepared-data audit](../../data/provenance/concrete_prepared.json) record the downloaded files, field coverage and hashes.