By the end of this course, learners will be able to apply linear regression techniques, interpret statistical outputs, and implement predictive models using SPSS and Excel. Through a blend of foundational theory and real-world applications, students will gain hands-on experience in analyzing datasets across engineering, energy, and finance.

The course begins with the fundamentals of regression, covering model building, scatter plots, T-values, and interpretation of results. It then progresses to practical case studies, where learners apply regression to scenarios such as copper expansion and energy consumption. Finally, the course explores advanced financial applications, including debt-to-income analysis, credit card debt modeling, and predictive forecasting. What makes this course unique is its practical, cross-domain approach—learners don’t just study equations, but apply regression to engineering problems, sustainability data, and financial risk analysis. By combining SPSS with Excel-based forecasting, the course equips students with industry-relevant skills for predictive analytics, risk assessment, and strategic decision-making. Whether you are a data analyst, business professional, or student, this course will help you transform raw data into actionable insights using regression modeling.