iPhone Sales Prediction Based on Multilinear Regression Model: Evidence from Statista

Currently, sales prediction remains a crucial element in the success of any business, and many forecasting models have been created and used. The iPhone is a line of smartphones by Apple Inc. that has become a leading product in the smartphone industry, and the accurate prediction of its sales is particularly important given its significant role in the global smartphone market. This study aims to contribute to the existing body of knowledge on sales prediction and provide valuable insights into the factors that impact the sales of iPhone. It uses multiple ways of regressions to predict the independent variables and forecasts the final sales of iPhone each year using Multi Linear Regression. The methods used to analyze the model included correlation analysis with a heatmap, and regression analysis with visualization to examine the relationship between the dependent and independent variables. The results indicate that the multilinear model is accurate, as evidenced by a high R-squared value and low MAPE and MSE values. However, limitations were identified, including the insufficiency of variables, the assumption of linear relationships, and the unexpected positive correlation between relative costs and sales.