Statistical Modeling

A model of global temperatures

Statistical Modeling is the process by which events of interest are approximated using statistical methods and interpretation. The goal of statistics is to bring uncertainty and complexity into an organized form, allowing it to be quantified. This can be very useful when trying to determine accuracy of collected data, or for approximating the course of an event which has not yet occurred. Though much of statistics can be performed without computers, technology plays a critical role in the ability to generate accurate and useful models for any amount of data.

Background

Generalized Approach

A typical research approach that can be applied to statistical modeling is the scientific method. It begins with a question and the desire to find an answer to the question. This can be formulated by an individual or group of researchers or by a company. It is important to formulate a strong hypothesis, as this will be the basis of the research being conducted. The next step is to decide what sort of data needs to be collected in order to help answer the question and provide enough evidence to accept or reject the hypothesis. The independent and dependent variables should be incorporated into the hypothesis to allow for a relationship between the variables (if any exists) to be exposed. The researcher then goes on to plan out a methodology for data collection. The methodology forms the basis of how the results may turn out, so the researcher focuses a large amount of time planning out an experiment that will be effective while also minimizing errors. The experiment is then executed and the data is tabulated. At this point, data can be quantified and an interpretation can be made.

See Also

Data Mining
Data Aggregation Online