Statistical modeling

As explained in Demand Forecasting Best Practices

A statistical model represents relationships in data using probabilistic or statistical assumptions to describe or predict observations.

Statistical modeling supports practical supply-chain questions: What drives demand, why did a forecast look strange, and how might a promotion change future demand? It represents relationships between historical demand, demand drivers, and predicted outcomes in forms that people can inspect and interpret. section 14.5 section 15.1.2

Common uses differ in the relationships they expose:

• Time-series models reveal demand level, trend, and seasonality, making unusual forecasts easier to investigate. section 14.5
• Predictive models use demand drivers such as pricing, promotions, or weather to estimate effects and plan scenarios. section 14.5
• Compared with machine learning, statistical models offer greater interpretability but handle less complexity and may deliver less accuracy. section 14.5 section 15.1.2

Promotion planning

A planner can ask whether a promotion will raise future demand by 15%, then inspect the promotion effect alongside level, trend, and seasonality. If the forecast is unexpected, those components and drivers indicate where to investigate. section 14.5

Figure 15.3 Promo uplift and scenarios

Use the promotion-uplift and scenario figure to connect demand-driver effects with planning decisions. section 15.1.2

How Demand Forecasting Best Practices covers it

How deep it goes

Explains interpretable time-series components and demand-driver effects, while stopping short of machine-learning complexity and accuracy.

Examples

Uses level, trend, seasonality, pricing, promotions, weather impacts, and 15% promotion-uplift scenarios as examples.

What it assumes

Assumes historical demand and demand-driver data, with clean data required when extending models with drivers.

What you can do after

Evaluate forecasts by tracing unusual results to level, trend, seasonality, or demand-driver behavior.

How it connects

Connects forecasting with time series, demand drivers, level, trend, seasonality, promotions, and machine learning.

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Also explained in

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  Effective Data AnalysisA statistical model is a mathematical representation of relationships among variables and random variation in observed data.
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  Causal AIStatistical modeling represents data-generating relationships with probabilistic or deterministic mathematical models.
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  Grokking StatisticsStatistical modeling is the process of representing relationships or data-generating processes with mathematical or probabilistic models.

Compare how 4 books explain statistical modeling →

Related concepts

• datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
• linear regressionincludes · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
• trainingused by · Training is a process of developing or improving a person’s or model’s capabilities through instruction, experience, or adjustment using data.
• classifierincludes · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• Bayesian networkincludes · A Bayesian network is a probabilistic graphical model whose directed acyclic graph represents random variables and their conditional dependencies.
• structural causal modelincludes · A structural causal model represents causal relationships among variables through equations that determine each variable from other variables and exogenous factors.

This explainer was generated by Manning's knowledge graph from the text of Demand Forecasting Best Practices. The section links point to the parts of the book it draws on.