Unit 06 · lab

Evidence Check: Separate Pattern From Explanation

Use the honey example from the Microsoft lesson or another non-sensitive public dataset.

Your evidence

Create a short relationship defense:

Name the two variables being compared.
Predict the relationship before looking at the chart.
Choose an appropriate visualization.
Describe the pattern you observe.
Identify one possible confounding variable.
State one conclusion the chart supports.
State one conclusion the chart does not support.

Stress the relationship

Open the Regression Playground. Add the outlier and move it. Record how slope and R² change, then explain why a changed fit still does not identify the mechanism that caused the relationship.

Check your reasoning

A visible relationship is evidence of a pattern. It is not automatically evidence of the mechanism that caused the pattern.

Done when

Your supported conclusion and unsupported conclusion are clearly different statements.

1. Dataset

Fictional robot test runs. No student or personal data.

run	model	sec	battery	°C	done
R-101	Atlas	52	88	22	yes
R-102	Atlas	57	79	23	yes
R-103	Nova	64	74	24	yes
R-104	Nova	71	—	27	no
R-105	Pulse	48	92	21	yes
R-106	Pulse	55	84	25	yes
R-107	Atlas	68	69	29	no
R-107	Atlas	68	69	29	no

2. Quick chart

ViewXY

Change the variables before deciding what the data “says.” The same rows can support different questions.

3. Editable Pandas analysis

Runs locally in your browser. First use downloads Pandas and its verified dependencies from Robotnix.

import pandas as pd

rows = [
    {"run_id": "R-101", "model": "Atlas", "duration_s": 52, "battery_pct": 88, "ambient_c": 22, "completed": True},
    {"run_id": "R-102", "model": "Atlas", "duration_s": 57, "battery_pct": 79, "ambient_c": 23, "completed": True},
    {"run_id": "R-103", "model": "Nova", "duration_s": 64, "battery_pct": 74, "ambient_c": 24, "completed": True},
    {"run_id": "R-104", "model": "Nova", "duration_s": 71, "battery_pct": None, "ambient_c": 27, "completed": False},
    {"run_id": "R-105", "model": "Pulse", "duration_s": 48, "battery_pct": 92, "ambient_c": 21, "completed": True},
    {"run_id": "R-106", "model": "Pulse", "duration_s": 55, "battery_pct": 84, "ambient_c": 25, "completed": True},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
]
df = pd.DataFrame(rows).drop_duplicates("run_id")

print(df[["duration_s", "battery_pct", "ambient_c"]].corr(numeric_only=True).round(2))
print("\nA correlation is a pattern, not a causal explanation.")

Output

Run the analysis to see evidence.

Ready. Predict before you run.

Runtime boundary: Robotnix serves the core Pyodide runtime and the SHA-256-verified Pandas dependency set. This workbench does not expose pip, micropip, a shell, filesystem projects, cloud credentials, or arbitrary package installation.