Use cases include forecasting, time series modeling, and discovering the causal effect relationship between variables. Common regression models include logistic, polynomial, and linear regression models.
Supervised learning techniques include regression models and classification models: Some popular statistical model examples include logistic regression, time-series, clustering, and decision trees.
The most common statistical modeling methods for analyzing this data are categorized as either supervised learning or unsupervised learning. The first step in developing a statistical model is gathering data, which may be sourced from spreadsheets, databases, data lakes, or the cloud. The application of statistical modeling to raw data helps data scientists approach data analysis in a strategic manner, providing intuitive visualizations that aid in identifying relationships between variables and making predictions.Ĭommon data sets for statistical analysis include Internet of Things (IoT) sensors, census data, public health data, social media data, imagery data, and other public sector data that benefit from real-world predictions. A statistical model is a mathematical relationship between one or more random variables and other non-random variables.
Statistical modeling refers to the data science process of applying statistical analysis to datasets.