Explanatory statistical model

As explained in Effective Data Analysis

An explanatory model represents relationships between variables and an outcome with emphasis on interpretability and evaluation of theoretical effects.

An explanatory statistical model describes how predictors relate to an outcome, emphasizing interpretability, effect direction, strength, and statistical evaluation. It supports recommendations and strategic decisions rather than primarily optimizing predictions for new cases. section 9.3.1 section 9.4

The modeling objective should follow stakeholder needs: a team seeking the factors contributing to conversions needs an explanatory deliverable, while a team estimating each person’s conversion likelihood needs a predictive one. section 9.3

Explanatory modeling typically requires:

• Grounding predictor selection in strong theoretical and logical justification. section 9.3.1
• Explaining each predictor’s relationship with the outcome, including its importance, direction, shape, and expected effect. section 9.3.1
• Using domain expertise because stakeholders may interpret explanatory results causally. section 9.3.1
• Checking assumptions such as linearity, normality, homoscedasticity, and residual independence before relying on results. section 9.4

Questions an explanatory model can address

Examples include recommending actions for patients with diabetes and chronic kidney disease, selecting educational programs to improve test scores, and assessing whether webinar participation relates to increased product usage. section 9.3.1

Figure 9.36 Slides showing the summary of methods and results that document conclusions about individual input variables.

Use the results-summary slides to document methods, findings, and conclusions about individual input variables for stakeholder communication. section 9.3.1

How Effective Data Analysis covers it

How deep it goes

Describes predictor relationships, assumptions, and interpretation criteria, while stopping short of detailed algorithmic implementation.

Examples

Uses conversion analysis, diabetes recommendations, school-program selection, webinar participation, and documented variable-level results.

What it assumes

Assumes domain expertise for responsible interpretation and requires checking foundational model assumptions.

What you can do after

Evaluate/understand predictor relationships and communicate recommendations supported by interpretable model results.

How it connects

Connects regression, classification, statistical significance, feature importance, domain knowledge, and predictive modeling.

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Related concepts

• independent variablehas part · An independent variable is an input or explanatory attribute whose values are used to model, explain, or predict variation in an outcome.
• labelhas part · A label is a value or annotation associated with an item that identifies its target category, outcome, or reference answer for learning or evaluation.
• statistical modelingkind of · Statistical modeling is the use of mathematical or probabilistic models to represent patterns, relationships, or variability in data for description, inference, or prediction.
• importance scoreuses · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
• linear regressionuses · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
• deliverablekind of · A deliverable is a defined work product, output, result, capability, or change that a project or team is expected to produce.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.