methodatlas
Session Builder

Plan my session

Plan a concrete work block with agenda, roles, preparation, and a copyable result artifact.

Method session30-90 min for creation, plus data preparationWorkshop or asyncScatter Plot

Session: Scatter Diagram

The plan translates the method into a concrete facilitated work block. Your inputs flow directly into the session brief and work artifact.

Derived automatically

Method session with 1-4. The plan uses the existing method logic and the runsheet.

Runsheet
Participation logic
Team round, shared work and alignment

Use the session for shared understanding. Contributions are collected visibly, assumptions are aligned, and open differences remain traceable in the artifact.

Outcome logic
Finish artifact

The session works directly toward Scatter Plot. After the session, the artifact should be shareable, reviewable, or reusable.

  1. 1

    Phase 1: Sharpen hypothesis and variables

    10 min

    Formulate hypothesis as an if-then sentence with concrete variables and units. Record expected direction (positive, negative). Define at least 30 data pairs as the threshold. Hint: If the hypothesis mixes several variables ("bad weather makes employees tired"), specify one variable per pair. Otherwise the diagram cannot be interpreted.

    FacilitatorScatter Plot
  2. 2

    Phase 2: Collect or prepare data

    Variable, often 30-60 min

    Extract or collect the dataset. Ensure clean pairing (same time, same context). Check data quality: outliers, missing values, collection conditions. Hint: Data context must be documented: who measured when, under which conditions. Without context, outliers will later be misinterpreted.

    FacilitatorKorrelationsnotiz
  3. 3

    Phase 3: Create plot

    15-20 min

    Scatter plot with X = cause, Y = effect. Axis labels with units. Optionally color points by time or context. Trendline optional (use cautiously). Hint: Trendlines often suggest more certainty than the data supports. First show the plot without a line, add the line only as a second layer if correlation is strong.

    FacilitatorHypothesenliste
  4. 4

    Phase 4: Describe pattern

    20-30 min

    Systematically review patterns: positive trend, negative trend, clusters, outliers, no pattern, U-shape. Calculate correlation coefficient (Pearson, Spearman), but do not overinterpret. Hint: Correlation 0.3 is not "light evidence", it is often noise. Speak of a robust relationship only from around 0.7, and even then only in context of sample size.

    FacilitatorScatter Plot
  5. 5

    Phase 5: Interpretation and next step

    15-20 min

    Classify result as hypothesis supported, contradicted or open. Discuss confounders. Derive action or further analysis (for example controlled experiment, Pareto, regression). Hint: Correlation is not causation. Before deriving measures, plan at least a controlled test or check stratification, otherwise symptoms get treated as causes.

    OwnerKorrelationsnotiz
  6. 6

    Publish artifact

    10 min

    Check the artifact for completeness, define location, set version or status, and name review recipients.

    OwnerScatter Plot
Usable artifact

Session Brief

For invitations, boards, tickets, PR descriptions, or workshop notes.

session-brief.md

Session Brief: Scatter Diagram

Goal

Artifact: Scatter Plot

Working Question

Do the paired data show a pattern that supports, contradicts or leaves open the suspected cause-effect hypothesis?

Context

Hypothesis as a sentence ("we suspect that X influences Y"); definition of variables with units; measurement interval and collection period; known confounders; minimum data volume (30+ pairs).

Setup

  • Format: Method session
  • Duration: 30-90 min for creation, plus data preparation
  • Mode: Workshop or async
  • Participants: One analyst who prepares and visualizes data; one domain expert who interprets context and anomalies; one data-source owner for data-quality questions.
  • Owner: One analyst who prepares and visualizes data
  • Participation mode: Team round, shared work and alignment
  • Outcome logic: Finish artifact

Participation Logic

Use the session for shared understanding. Contributions are collected visibly, assumptions are aligned, and open differences remain traceable in the artifact.

Outcome Logic

The session works directly toward Scatter Plot. After the session, the artifact should be shareable, reviewable, or reusable.

Input

Spreadsheet or statistics tool (Excel, Google Sheets, R, Python with pandas/matplotlib, JMP); dataset with paired values; timestamp per measurement; optional Pareto tool for comparison.

Preparation

Data in two columns: X (cause), Y (effect). Timestamp as third column. Tool open. Hypothesis visible at the top of the sheet.

Agenda

  1. Phase 1: Sharpen hypothesis and variables (10 min) Owner: Facilitator Action: Formulate hypothesis as an if-then sentence with concrete variables and units. Record expected direction (positive, negative). Define at least 30 data pairs as the threshold. Hint: If the hypothesis mixes several variables ("bad weather makes employees tired"), specify one variable per pair. Otherwise the diagram cannot be interpreted. Output: Scatter Plot

  2. Phase 2: Collect or prepare data (Variable, often 30-60 min) Owner: Facilitator Action: Extract or collect the dataset. Ensure clean pairing (same time, same context). Check data quality: outliers, missing values, collection conditions. Hint: Data context must be documented: who measured when, under which conditions. Without context, outliers will later be misinterpreted. Output: Korrelationsnotiz

  3. Phase 3: Create plot (15-20 min) Owner: Facilitator Action: Scatter plot with X = cause, Y = effect. Axis labels with units. Optionally color points by time or context. Trendline optional (use cautiously). Hint: Trendlines often suggest more certainty than the data supports. First show the plot without a line, add the line only as a second layer if correlation is strong. Output: Hypothesenliste

  4. Phase 4: Describe pattern (20-30 min) Owner: Facilitator Action: Systematically review patterns: positive trend, negative trend, clusters, outliers, no pattern, U-shape. Calculate correlation coefficient (Pearson, Spearman), but do not overinterpret. Hint: Correlation 0.3 is not "light evidence", it is often noise. Speak of a robust relationship only from around 0.7, and even then only in context of sample size. Output: Scatter Plot

  5. Phase 5: Interpretation and next step (15-20 min) Owner: Owner Action: Classify result as hypothesis supported, contradicted or open. Discuss confounders. Derive action or further analysis (for example controlled experiment, Pareto, regression). Hint: Correlation is not causation. Before deriving measures, plan at least a controlled test or check stratification, otherwise symptoms get treated as causes. Output: Korrelationsnotiz

  6. Publish artifact (10 min) Owner: Owner Action: Check the artifact for completeness, define location, set version or status, and name review recipients. Output: Scatter Plot

Closeout

  • Update result artifact: Scatter Plot
  • Define location, version, and review recipients.
  • Define owner, next step, and review date.
Usable artifact

Work artifact

Pre-filled starting point based on the matching template.

work-artifact.md

Scatter Plot: Scatter Diagram

Working Question

Do the paired data show a pattern that supports, contradicts or leaves open the suspected cause-effect hypothesis?

Context

Hypothesis as a sentence ("we suspect that X influences Y"); definition of variables with units; measurement interval and collection period; known confounders; minimum data volume (30+ pairs).

Participants

  • Owner: One analyst who prepares and visualizes data
  • Participants: One analyst who prepares and visualizes data; one domain expert who interprets context and anomalies; one data-source owner for data-quality questions.

Input

Spreadsheet or statistics tool (Excel, Google Sheets, R, Python with pandas/matplotlib, JMP); dataset with paired values; timestamp per measurement; optional Pareto tool for comparison.

Template

Scatter Diagram Canvas

Context

What is this method used for?

Core question

Which question should be answered at the end?

Input

Which data, observations, or materials are available?

Working area

  • Area 1:
  • Area 2:
  • Area 3:
  • Relationships / patterns:

Output artifacts

  • Scatter plot:
  • Correlation note:
  • Hypothesis list:

Open questions

  • ...

Next step

Owner, date, success signal.

Completion Check

  • Scatter Plot is complete enough for review:
  • Location:
  • Version / status:
  • Review by:
  • Next step:

Next Step

  • Review result
  • Mark open questions
  • Schedule review or decision
Template base

Scatter Diagram Working Template

View templateCompact working template for Scatter Diagram with context, input, output artifacts, and next step.
canvas

scatter-diagram-working-template.md

Compact working template for Scatter Diagram with context, input, output artifacts, and next step.

Scatter Diagram Canvas

Context

What is this method used for?

Core question

Which question should be answered at the end?

Input

Which data, observations, or materials are available?

Working area

  • Area 1:
  • Area 2:
  • Area 3:
  • Relationships / patterns:

Output artifacts

  • Scatter plot:
  • Correlation note:
  • Hypothesis list:

Open questions

  • ...

Next step

Owner, date, success signal.

Ready to use when
  • Working question, owner, and target artifact are visible.
  • The result fits Scatter Plot.
  • Date, data source and collection period in the header. Save follow-up analyses with newer data as a new plot in the same repo. Archive raw data as CSV with the plot, not only the image.
  • Open questions are noted as follow-ups.
  • The next review or decision point is scheduled.
ProfileRunsheet