Final answer:
The question is about collecting class-labeled training examples for machine learning from an electronics store database. These examples form the basis for analyzing trends, making predictions, and improving business strategies. Statistics play a crucial role in defining the population, sample, parameter, statistic, variable, and data.
Step-by-step explanation:
The student's question pertains to the collection and use of class-labeled training examples from a database, specifically within the context of an electronics store customer database. In the realm of machine learning and data science, this refers to the process of extracting data that can be used to train a model, where each training tuple consists of a set of features (attributes) and an associated label (the class). For example:
- A marketing manager at an electronics chain store collects ages and other variables from customers to analyze demographic trends.
- A librarian uses a tally sheet to label whether books are checked out by adults or children to study user demographics.
- After a debate, a political party's polling staff gathers voter preferences and opinions to gauge the debate's impact.
In these cases, the data (customer ages, book checkouts, voter preferences) serve as the features, while the labels might be categories like 'young adult', 'child', and 'candidate supported'. In a business context, such as an electronics store, this information is vital for creating targeted marketing strategies and improving customer service.
Statistics are heavily involved in this process. You identify the population (all customers), sample (selected customers), parameter (actual average amount spent), statistic (calculated average from the sample), variable (individual spending amount), and the data (collected spending amounts).