Introduction

Minitab is one of the leading software tools for statistical analysis, widely used across industries to improve decision-making, optimize processes, and enhance product quality. Minitab for Advanced Statistical Analysis is a specialized training course designed for professionals who seek to deepen their understanding of statistical analysis through Minitab. This course provides in-depth knowledge on advanced statistical techniques, data manipulation, and interpretation of complex statistical results using Minitab. Whether you’re involved in quality improvement, data science, or research, mastering Minitab will empower you to make data-driven decisions with confidence.

Course Objectives

By the end of this course, participants will be able to:

1. Master Advanced Statistical Techniques: Learn advanced statistical tools available in Minitab to analyze data effectively, including regression analysis, multivariate analysis, and time-series forecasting.
2. Perform Hypothesis Testing and Statistical Inference: Understand the concepts of hypothesis testing, confidence intervals, p-values, and statistical power in complex datasets.
3. Use Regression Analysis for Predictive Modeling: Apply multiple regression, logistic regression, and advanced regression models to predict outcomes and uncover relationships between variables.
4. Conduct Multivariate Analysis: Explore techniques such as Principal Component Analysis (PCA), Factor Analysis, and Discriminant Analysis to analyze multivariate data sets.
5. Analyze Variability with Advanced Control Charts: Learn how to implement advanced control charts, process capability analysis, and stability analysis using Minitab.
6. Time Series Forecasting and Analysis: Gain the skills to forecast future trends using time series analysis, including trend models, seasonal models, and forecasting techniques.
7. Implement Statistical Process Control (SPC): Understand how to apply SPC techniques to monitor and control production processes, with a focus on advanced charting and analysis.
8. Create and Interpret Complex Reports: Learn to create comprehensive statistical reports in Minitab, providing clear, actionable insights based on advanced data analysis.

Who Should Attend?

This course is ideal for:

• Data Analysts and Statisticians looking to deepen their expertise in statistical analysis using Minitab.
• Quality Professionals involved in Six Sigma, Lean, or Total Quality Management (TQM) initiatives who want to use advanced statistical tools for process improvement.
• Research and Development Professionals who need advanced statistical methods for research projects and product development.
• Engineers in fields like manufacturing, automotive, and healthcare who require advanced analysis for process optimization and quality control.
• Business Analysts and Consultants who use statistical data for predictive modeling, market analysis, and decision-making.
• Graduate Students and Academics involved in statistical research or academic projects requiring advanced data analysis techniques.

Day-by-Day Outline

Day 1: Introduction to Minitab and Data Preparation

• Overview of Minitab’s Interface and Features:
  • Introduction to Minitab’s workspace, menus, and statistical tools.
  • Navigating through Minitab’s Data, Graph, and Stat menus.
  • Importing and exporting data in various formats (Excel, CSV, etc.).
  • Data preparation techniques: cleaning, transforming, and organizing data for analysis.
• Descriptive Statistics and Data Summarization:
  • Using Minitab to calculate measures of central tendency, dispersion, and shape (mean, median, standard deviation, skewness).
  • Visualizing data using histograms, boxplots, and stem-and-leaf displays.
  • Exploring data distributions and outlier detection methods.

Day 2: Hypothesis Testing and Statistical Inference

• Understanding Hypothesis Testing in Minitab:
  • Setting up null and alternative hypotheses for different tests.
  • Conducting t-tests, chi-square tests, ANOVA, and non-parametric tests.
  • Interpreting p-values, confidence intervals, and type I/II errors.
  • Assumptions and considerations when selecting appropriate hypothesis tests.
• Advanced Inference Techniques:
  • Power analysis and sample size determination.
  • Applying statistical inference to real-world datasets.
  • Interpreting results and drawing conclusions from statistical outputs.

Day 3: Regression Analysis for Predictive Modeling

• Simple and Multiple Regression:
  • Building simple linear regression models to predict outcomes.
  • Interpreting regression coefficients, R-squared values, and residual plots.
  • Creating multiple regression models to analyze relationships between multiple variables.
  • Handling multicollinearity, outliers, and influential points in regression models.
• Logistic Regression and Classification:
  • Conducting binary logistic regression for classification problems.
  • Understanding odds ratios, classification accuracy, and model diagnostics.
• Regression Diagnostics and Model Validation:
  • Validating models through cross-validation techniques.
  • Residual analysis and diagnostic plots for regression models.

Day 4: Multivariate Analysis

• Principal Component Analysis (PCA):
  • Performing PCA to reduce dimensionality in multivariate datasets.
  • Visualizing and interpreting principal components.
  • Selecting the optimal number of components based on eigenvalues and scree plots.
• Factor Analysis:
  • Using factor analysis for identifying latent variables.
  • Understanding factor loadings and rotation methods.
• Discriminant Analysis:
  • Conducting discriminant analysis to classify cases into predefined groups.
  • Interpreting classification results and the importance of each predictor.

Day 5: Time Series Analysis and Advanced Control Charts

• Time Series Forecasting:
  • Performing time series analysis in Minitab using moving averages, exponential smoothing, and ARIMA models.
  • Identifying trends, seasonal variations, and cycles in data.
  • Forecasting future values based on time series models.
• Advanced Control Charts and Process Monitoring:
  • Implementing advanced control charts such as X-bar, R, P, NP, and C charts for process control.
  • Analyzing process capability and stability with Minitab’s capability analysis tools.
  • Understanding control chart patterns and identifying out-of-control signals.
• Process Improvement with Statistical Process Control (SPC):
  • Implementing SPC for process monitoring and quality control.
  • Analyzing process variation and improving stability using SPC.