Forecasting Demand with Linear Regression

Forecasting demand using linear regression involves using historical data to create a linear equation that can be used to predict future demand. This is done by fitting a line to the historical data points, where the independent variable represents time and the dependent variable represents demand. The equation of the line can then be used to forecast demand for future time periods.

The assumptions of linear regression in forecasting demand include: linearity, independence, homoscedasticity, and normality. Linearity assumes that there is a linear relationship between the independent and dependent variables. Independence assumes that the errors are not correlated with each other. Homoscedasticity assumes that the variance of the errors is constant across all levels of the independent variables. Normality assumes that the errors follow a normal distribution.

Linear regression can be used to evaluate the effectiveness of demand forecasting by comparing the predicted values to the actual values. The difference between the predicted and actual values, known as the residual or error, can be calculated for each data point. By analyzing the distribution of the residuals and calculating metrics such as the mean squared error or R-squared, one can determine how well the linear regression model fits the data and how accurate the demand forecasts are.

<p>$y = mx + b$</p> Signup and view all the answers

<p>The slope coefficient represents the change in the dependent variable for a one-unit change in the independent variable.</p> Signup and view all the answers

<p>The purpose of using linear regression in forecasting demand is to establish a relationship between the independent variable(s) and the dependent variable, allowing for the prediction of future demand based on observed data.</p> Signup and view all the answers