Multiple Linear Regression for Sales Forecasting: Theory and Practice

Abstract

The Company will use sales forecasting to plan inventory, allocate the marketing budget and develop a business plan for consumer goods. Due to their simplicity and intuitiveness, some have been employed for causal analysis in recent years. Offer practical references for the company's future development plan. Systematically introduce simple and multiple linear regression theory, ordinary least-squares parameter estimation, and necessary model diagnostic assumptions in this paper. Based on published retail and macro-economic case studies, this paper analyzes the model's performance indicators, such as R², adjusted R² and mean absolute percentage error (MAPE), and discusses real-world business value, inherent deficiencies and optimization paths. Based on the above, a good regression model can be used to forecast the demand for consumer goods. The model has the following deficiencies in a complex market environment: linearity assumption, multicollinearity and unmodeled nonlinear relationship. Future work can combine regularization, polynomial feature engineering and hybrid time-series models to improve the prediction accuracy of interpretability under a fluctuating retail environment.