AMOS was one of the first applications that offered Full Information Maximum
Likelihood (FIML) to deal with the item called non-response, which reduces statistical power and may lead to biased parameter estimates. The usual assumptions about uncorrelated uniqueness and a simple structure factor pattern in the measurement model are applied. When there is only one item representing a latent construct, in this thesis for example behaviour, the measurement error cannot be estimated but has to be set to a fixed value. So, the error variance is fixed to zero.
After collecting the data researcher searches various methodologies which go through the methodology for finding the answer. He chooses the suitable technique, formula, algorithms, and methods for the customer data base. Researchers Collects the entire customer buying transaction database. After collecting the data, researcher selects the suitable method from various alternatives. He select association rule for checking the association between the various products which are bought by the customer. He implement the market Basket Analysis for this database.
For example according to the output association rule,
The association rule will have the following form X→ Y that form has meaning that people who bought items of set x are often also bought items on set Y e.g. if X = { sugar, wheat} and Y ={Rice, Pulses} and we get association rule indicates that people who bought sugar and wheat also bought Rice and pulses. Support is the frequency of transaction to have all the items on both sets and Y are bought together.
阿摩司是第一个应用提供充分信息最大
可能性（电影）应对所谓的项目无反应，从而减少统计权力，并可能导致偏见的参数估计。通常的假设无关的独特性和简单的结构模式在测量模型的应用。当只有一个项目代表一个潜在的构建，本文例如行为，测量误差无法估计，但已被设置为一个固定值。所以，误差方差固定到零。
在收集数据的各种方法，通过研究搜索的方法找到答案。他选择合适的技术，配方，方法，和方法的客户数据库。研究人员收集整个客户的购买交易数据库。在收集数据，研究人员选择适当的方法从各种备选方案。他选择关联规则检查协会之间的各种产品，顾客购买的。他实施市场篮分析数据库。
例如根据输出关联规则，
关联规则将有以下形式→，形式是有意义的，人们谁买了项目集×往往也买了项目集你如果×= {糖，小麦和水稻= { } }，脉冲和我们得到的关联规则表明，人谁买了糖和小麦也买了大米和豆类。支持的频率是交易的所有项目都和你一起买。
