Which statistical technique estimates unknown data based on known data?

Multiple Regression Analysis is the statistical technique that estimates unknown data based on known data. This method involves using multiple independent variables to determine their relationship with a dependent variable. By analyzing the known data, multiple regression can predict the value of the dependent variable when the values of the independent variables are known.

For instance, in an assessment context, if a property’s sale price (dependent variable) is influenced by various factors such as square footage, number of bedrooms, and location (independent variables), multiple regression can help assessors estimate sale prices for properties with similar characteristics based on available data.

The other options serve different purposes. Correlation Analysis examines the strength and direction of the relationship between two variables but does not involve prediction based on multiple factors. Variance Analysis focuses on measuring the spread of data and understanding the distribution, rather than estimating unknowns. Descriptive Statistics summarizes and describes the features of a dataset without making predictions. Thus, while all these techniques are valuable in data analysis, Multiple Regression Analysis is specifically designed for the task of estimating unknown values based on known inputs.