Statistical modeling has two main purposes: *prediction* and *explanation*. ** Prediction** –

*Given a set of input values, what is the output? Given a one unit change in a particular type of input, what is the effect on the output?*

**–**

*Explanation**How do the variables relate to each other? How strong is this relationship? How much of the variation in the dependent variable is explained by the model?*

There are many different statistical modeling techniques Cre8ive Measures can provide, including (but not limited to) the following:

**Regression Models**

Regression involves estimating the mathematical relationship between one variable called the response variable, and one or more explanatory variables.

Regression modelling can be used for forecasting time series data.

**Nonparametric Models**

Nonparametric models differ from parametric models (such as regression models), in that the model is data driven rather than structured with parameters to be estimated.

In the following plot, a smoother function was applied to the data:

**Bayesian Models**

Bayesian modelling has become increasingly popular in recent years due to the rise of computer technology. Bayesian models are based on probabilities rather than frequencies e.g. the probability of *A*, given *B, C *and D. Bayesian models are based around Bayes’ Theorem:

**Classification and Regression Trees**

Classification and Regression Trees (CART) take a rather blunt approach, where a hierarchical structure is fitted to the data. Classification trees are used for splitting categorical data. Regression trees are used for splitting quantitative data.

**Discriminant Analysis**

Discriminant analysis involves finding relationships which best separate the data into known groups. When used for prediction, discriminant analysis models classify observations into groups.