A statistical technique that can be used to analyze the relationship between a single dependent variable and several independent variables. The objective of multiple regression analysis is to use the independent variables whose values are known in the past and future to predict the future values of the single dependent variable. Each predictor variable (Xi) is weighted, the weights (bn) denoting their relative contribution to the overall prediction. In calculating the weights, the regression analysis procedure ensures maximal prediction from the set of independent variables. These weights also facilitate interpretation as to the influence of each variable making the prediction, although correlation among the independent variables can complicate the interpretative process.
General Notation:
Y = b0 + bX1 + bX2 + bX3...bXn + ei
Where:
Y = Dependent variable
b0 = Y intercept or constant
bn = Coefficients or weights
Xi = Independent variables
ei = Residual or prediction error
Example:
Consumer demand for product Y = b0 + bprice + badvertising + bmerchandising + bdistribution + bcompetitive price
More terms such as multiple linear regression in Dictionary M.
- Manual MPI Buffer:
- Help Data packet moved through memory pipes (MPI) from the sender to the receiver multiple linear regression definition.
- Manual Market Value (FS-CMS):
- Help that a property would fetch in the open market conditions at a particular point in time. This value is derived based on usual way of business, actual property features such as type of usage and multiple linear regression explain.
- Manual Margin:
- Help Difference between the delivered price and the sales price as a fixed amount or a percentage multiple linear regression what is.
- Manual Master Data Generator:
- Help Program that uses the master data template to create master data (such as business partners and contracts multiple linear regression meaning.
- Manual Middle Rate:
- Help The mean of the bid rate and the ask rate multiple linear regression abbreviation.