Linear regression is a statistical modeling tool that we can use to predict one variable using another. This is a particularly useful tool for predictive modeling and forecasting, providing excellent insight on present data and predicting data in the future.

## Is linear regression used in predictive analytics?

Linear regression is a statistical method that analyzes and finds relationships between two variables. In predictive analytics it can **be used to predict a future numerical value of a variable**. … Linear regression is (as you might imagine) most suitable for linear data.

## Is linear regression predictive or descriptive?

Linear regression has many practical uses. Most applications fall into one of the following two broad categories: If the goal is prediction, forecasting, or error reduction, linear regression can be used to fit a **predictive model** to an observed data set of values of the response and explanatory variables.

## What is linear regression in predictive analytics?

Linear regression is **the most commonly used method of predictive analysis**. It uses linear relationships between a dependent variable (target) and one or more independent variables (predictors) to predict the future of the target. … You can use linear regression for causal research, result prediction, or trend prognosis.

## What are predictive analytics tools?

**Here are eight predictive analytics tools worth considering as you begin your selection process:**

- IBM SPSS Statistics. You really can’t go wrong with IBM’s predictive analytics tool. …
- SAS Advanced Analytics. …
- SAP Predictive Analytics. …
- TIBCO Statistica. …
- H2O. …
- Oracle DataScience. …
- Q Research. …
- Information Builders WEBFocus.

## What are the possible types of predictive models?

There are many different types of predictive modeling techniques including **ANOVA**, linear regression (ordinary least squares), logistic regression, ridge regression, time series, decision trees, neural networks, and many more.