In data analysis, fitting statistical models to data is indispensable, illuminating the path toward informed decision-making. This process involves finding the best-fitting mathematical representation of data points, enabling analysts to extract meaningful insights, make predictions, and uncover hidden trends.

Statistical modeling involves selecting a mathematical equation or formula that most accurately captures the relationship between input variables and the observed outcomes. This relationship could be linear, nonlinear, or even more complex, depending on the nature of the data. One of the critical goals of model fitting is to minimize the error between predicted values and actual data points, a process often achieved through optimization techniques.

Some of the most commonly used statistical models include:

- Regression Models – Regression models are used to examine the relationships between variables. This technique is often used by organizations for determining which independent variables hold the most influence over the dependent variables in the dataset.
- Classification Models – The process of classification involves an algorithm which analyzes an existing data set of known points. The result inferred from this analysis is then leveraged as a means of appropriately classifying the data. Classification models are said to be a form of supervised machine learning often used when the analyst needs to understand how they got to a certain point.

## List of recommended resources #

### For a broad overview #

This video tutorial by XLSTAT provides a broad overview of statistical modeling used in data science.

What Is Statistical Modeling For Data Analysis?

This blog post provides a brief introduction to statistical modeling and its important techniques used in data analysis.

### For in depth understanding #

This journal by Sage Publications aims to be the major resource for statistical modeling, covering both methodology as well as practice.

Statistical modeling methods: challenges and strategies

This article by Steven S. Henley, Richard M. Golden and T. Michael Kashner describes model validity in statistics and the various methods designed for assessing the model fit, selection, specification etc.

Written by A. C. Davison as part of the Cambridge Series in Statistical and Probabilistic Mathematics, this book provides an in-depth study of statistical models in data analysis.

### Case study #

This report describes the process involved and gives a summary of the results from the pilot implementation of statistical models used for measuring the value addition to Bulgarian schools through an analysis of the national student assessment results.

This Policy research Working Paper, written as part of the Fragility, Conflict and Violence Global Theme and the Development Economics Vice Presidency, proposes a statistical forecasting method to predict food crises in advance, so that preventive measures can be taken. The results show that statistical models can effectively identify future food crises, and that using these models can help to increase the lead time for action.