Master predictive analytics with SPSS through a structured progression from data management and descriptive statistics to correlation, linear and multiple regression, logistic regression, and multinomial regression. You will learn to import and organize datasets, calculate mean and standard deviation, create scatter plots, examine relationships between variables, construct predictive models, refine predictors, calculate predicted values, and interpret coefficients, significance levels, odds ratios, model-fitting results, and parameter estimates.

Designed for students, researchers, and professionals who want to use SPSS for research, business, academic, health, psychology, or financial analysis, this course connects statistical concepts with practical decision-making. Guided SPSS demonstrations and hands-on case studies involving heart pulse, student test scores, copper expansion, energy consumption, debt assessment, credit card data, smoking preferences, and health outcomes help you apply each technique to varied datasets. By the end of the course, you will be able to select and apply appropriate predictive modeling techniques, evaluate predictors, interpret SPSS regression outputs, validate results, and turn raw data into meaningful insights. Its step-by-step design, diverse case-based practice, and balance of statistical interpretation with software application make it a practical path from SPSS fundamentals to advanced regression analysis.