Statistical Methods for Big Data Analysis in Technological Systems

New statistical models must exist for big data analysis in technology because they need to work with datasets of many dimensions to uncover valuable insights. The Generalized Linear Model (GLM) serves in this research to process large data while extending beyond regular regression prediction methods. Apache Spark MLlib enables the implementation of GLM through its massive dataset processing capabilities which benefit from distributed computing methods for scalability. The model finds application in network traffic analysis where it forecasts anomalies while enhancing system performance with better results. The experimental results confirm that GLM with Apache Spark leads to faster computations alongside solid statistical inference benefits throughout multiple technical applications. Real-time big data analytics reaches its best advantages through the fusion of statistical modeling and distributed frameworks according to the research results. Companies develop innovative prediction systems through the combination of GLM and machine learning which adapt well to dynamic technological systems.