Predictive Analytics & Modeling with Minitab

Build practical data analysis skills with Minitab and learn to turn statistical results into actionable insights. You’ll begin with the foundations of predictive modeling and regression, exploring how variables relate and how statistical outputs support data-driven decisions. You’ll then use ANOVA to compare group differences, interpret variations across datasets, and examine NAV price data to identify volatility and risk patterns. As you progress, you’ll apply descriptive statistics and hypothesis testing to real-world scenarios involving customer complaints, resting heart rates, and loan applicants. You’ll summarize datasets, evaluate distribution patterns, analyze socio-economic and savings patterns, and conduct paired and independent t-tests to determine statistical significance. Designed for analysts, researchers, and professionals seeking to strengthen their statistical skills, this course combines statistical theory with hands-on Minitab implementation. Its case-based approach emphasizes interpretation as well as analysis, helping you move beyond calculations to explain findings, recognize patterns, assess variability, and make informed decisions. Enroll to build confidence in applying predictive analysis, ANOVA, descriptive statistics, and t-tests to practical datasets.

Build practical predictive analytics skills and learn to turn complex data into actionable insights for business, finance, and research. You’ll begin with predictive modeling foundations, regression approaches, ANOVA, control charts, and Minitab before examining observations, NAV results, descriptive statistics, and case-based datasets involving customer complaints, health indicators, and loan applicants. You’ll learn to select, apply, and interpret t-tests, ANOVA, and chi-square tests; evaluate variable relationships through correlation analysis; and use Minitab to interpret statistical results. You’ll then develop regression skills by constructing scatterplots, formulating regression equations, identifying significant variables, computing predicted values, and evaluating p-values and t-values across demographic, scientific, and financial datasets. You’ll also implement and interpret statistical outputs using Minitab and Excel. Designed for learners who want to apply statistical reasoning to real-world data, this course combines predictive analytics theory with structured, practical examples. Its emphasis on both application and interpretation helps you move beyond calculating results to understanding what they mean. By the end, you’ll be able to identify patterns, evaluate statistical significance, build predictive and regression models, and translate analytical findings into informed decisions.