Multiple Regression Prediction Tool
Define Pre-Trained Model & New Observation
This tool uses a pre-trained multiple linear regression model. You need to provide the intercept and coefficients. It does not train a model or perform statistical analysis from raw data.
Prediction Output
Input model coefficients and new data, then click "Predict".
Predicted Value (Ŷ)
N/A
Inputs Used for Prediction:
Understanding Multiple Regression & Export
Multiple Linear Regression
Multiple Linear Regression is a statistical technique used to model the relationship between a single continuous dependent variable (Y) and two or more independent (predictor) variables (X₁, X₂, ..., Xn).
The goal is to find a linear equation that best describes or predicts the value of Y based on the values of the X variables.
The Regression Equation:
The general form of a multiple linear regression equation is:
Ŷ = β₀ + β₁X₁ + β₂X₂ + ... + βnXn
Where:- Ŷ (Y-hat) is the predicted value of the dependent variable.
- β₀ is the Intercept (or constant): The predicted value of Y when all X variables are zero.
- β₁, β₂, ..., βn are the Regression Coefficients: They represent the change in Ŷ for a one-unit change in the corresponding X variable, holding all other X variables constant. These coefficients are typically estimated from data using a training process (e.g., Ordinary Least Squares - OLS), which is not performed by this simplified tool.
- X₁, X₂, ..., Xn are the values of the independent variables for a specific observation.
Interpretation of Coefficients (βᵢ):
- The sign (+ or -) of a coefficient indicates the direction of the relationship between that X variable and Y.
- The magnitude indicates the strength of the effect of that X on Y, for a one-unit change in X, assuming other variables are constant.
Note: This tool requires you to provide the intercept (β₀) and coefficients (βᵢ) from an already trained multiple linear regression model. It then uses these to make a prediction for a new set of X values.