Master the fundamentals and practical applications of linear regression while building predictive models using SPSS and Excel. In this hands-on course, you will learn how to construct regression models, interpret statistical outputs, evaluate statistical significance, and apply predictive analytics to solve real-world problems across engineering, energy, and finance.

You will begin by exploring the core concepts of linear regression, including scatter plots, T-values, regression equations, coefficient interpretation, and model evaluation in SPSS. As you progress, you will apply regression techniques to engineering and energy datasets, analyzing scenarios such as copper expansion and energy consumption while validating model performance with new data. In the final module, you will develop regression models for financial applications, including debt-to-income analysis, credit risk assessment, and predictive forecasting using SPSS and Excel. Designed for data analysts, business professionals, and students, this course combines statistical theory with practical case studies to help you build confidence in predictive modeling. By the end of the course, you will be able to interpret regression results, analyze diverse datasets, develop forecasting models, and transform data into actionable insights that support informed, data-driven decision-making.