Overview

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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.

Syllabus

Introduction to Predictive Analytics
- This module introduces the foundations of predictive analytics, covering the basics of predictive modeling and essential statistical tools. Learners will explore regression approaches, ANOVA, and control charts while gaining practical exposure to Minitab for data analysis and interpretation.
Data Observations and Results
- This module emphasizes the importance of observations in analytics. Learners will examine NAV prices, descriptive statistics, and case-based insights such as customer complaints, health data, and loan applicant results to build real-world analytical perspectives.
Hypothesis Testing Applications
- This module focuses on hypothesis testing with practical applications of t-tests, ANOVA, and chi-square tests. Learners will understand when to apply different tests and how to interpret results for business and research contexts.
Correlation Analysis
- This module explores correlation concepts, implementation, and interpretation. Learners will study correlation basics, use Minitab for analysis, and apply correlation results to financial and real-world datasets for better decision-making.
Regression Foundations
- This module provides a comprehensive study of regression, including basic principles, scatterplots, equations, applications, and advanced insights. Learners will analyze demographic, financial, and scientific datasets to master regression modeling and interpretation.