Читать книгу End-to-end Data Analytics for Product Development - Chris Jones - Страница 26

As mentioned in Stat Tool 1.3, we often want to answer questions about our processes or products to make improvements and predictions, save money and time, and increase customer satisfaction:

 What is the stability of a new formulation?

 Which attributes of a product do consumers find most appealing?

 What is the performance of a new product compared with products currently on the market?

 What is causing high levels of variation and waste during processing?

 Can a process change reduce production time to get the product in stores more quickly?

These questions are examples of inferential problems.

How can we use inferential techniques to answer these questions?

Inferential problems are usually related to:

 Estimation of a population parameter:What is the stability of a new formulation?Which attributes of a product do consumers find most appealing?

 Comparison of a population parameter to a specified value or among groups:What is the performance of a new product compared with the industry standard or products currently on the market?

 Assessing relationships among variables:What is causing high levels of variation and waste during processing?Can a process change reduce production time to get the product in stores more quickly?

We may use several inferential techniques to answer different questions:

 Estimation of a population parameter:Point estimate and confidence intervals

 Comparison among groups:Hypothesis testing (one‐sample tests; two‐sample tests; analysis of variance, ANOVA)

 Assessing relationships among variables:Regression models