Multiple regression is a fundamental data analysis tool that offers a powerful way to predict dependent variables based on multiple independent variables. There are many methods and techniques that can be applied to improve the accuracy and performance of the model, but it is important to understand the assumptions and limitations associated with using multiple regression. This method finds application in several fields and is the basis for much more complex models in machine learning. Experimental studies of modern network multimedia in operational conditions are important for revealing bottlenecks in their functioning. On this basis, recommendations can be made for improving operational indicators, such as performance, reliability, serviceability, etc. Forecasting with multiple nonlinear regression (including polynomial models, which are often used for nonlinear models) is the process of using a regression model to calculate predicted values of the dependent variable for new or unknown values of the independent variables. This publication is dedicated to the experimental study of the BigBlueButton platform. After making predictions with new data, the model with the predicted new values was evaluated. This is done by using various metrics to assess the accuracy of the model.