Linear Regression & Predictive Modeling with SPSS

Overview

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.

Syllabus

Foundations of Linear Regression in SPSS

This module introduces the fundamentals of linear regression modeling using SPSS. Learners will explore the conceptual foundations of regression, understand the importance of statistical significance, and practice visualizing data relationships. By the end of this module, students will be able to construct regression equations, interpret coefficients, and evaluate the strength of predictive models.

Applied Regression with Real-World Data

This module demonstrates the practical application of regression modeling across engineering and energy datasets. Learners will examine case studies such as copper expansion and energy consumption, applying regression to interpret real-world phenomena. The focus is on extending regression analysis to scientific and applied contexts while validating model consistency with new data.

Advanced Regression for Financial Insights

This module focuses on financial applications of regression, particularly in assessing debt, credit risk, and forecasting. Learners will build regression models to evaluate debt-to-income ratios, credit card liabilities, and predictive outcomes using Excel and SPSS. By mastering these skills, students will enhance their ability to make data-driven financial decisions.