Minitab Essentials Course Overview

Target Audience for Minitab Essentials

1. The Minitab Essentials course is designed for professionals seeking to master statistical analysis and process improvement using Minitab software.

   
   

2. Target audience for the Minitab Essentials course:

   
   

• Quality Assurance Engineers
• Data Analysts
• Process Improvement Specialists
• Six Sigma Green and Black Belts
• Research Scientists
• Production Managers
• Business Analysts
• Statisticians
• Manufacturing Engineers
• Product Development Professionals
• Quality Control Technicians
• Lean Practitioners
• Operations Analysts
• Project Managers involved in data-driven decision-making
• Supply Chain Analysts
• Academic Researchers and Graduate Students in statistics or related fields

Learning Objectives - What you will Learn in this Minitab Essentials?

Introduction to Course Learning Outcomes and Concepts

The Minitab Essentials course equips learners with foundational skills in statistical analysis and data interpretation using Minitab software, covering data visualization, inferential statistics, and hypothesis testing.

Learning Objectives and Outcomes

• Understand how to import and format datasets effectively within Minitab for accurate analysis.
• Create and interpret Bar Charts to visualize categorical data and identify patterns.
• Generate Histograms to analyze and display the distribution of continuous data sets.
• Utilize Boxplots for comparing distributions and identifying outliers in data sets.
• Construct and interpret Pareto Charts to prioritize problem areas or identify the most significant factors in a dataset.
• Create Scatterplots to examine relationships between two continuous variables and identify potential correlations.
• Perform and interpret Chi-Square Analysis to test relationships between categorical variables using contingency tables.
• Calculate and understand Measures of Location (mean, median, mode) and Variation (range, variance, standard deviation) in data sets.
• Conduct and interpret t-Tests to compare means and assess statistical significance in the differences observed.
• Execute Proportion Tests to analyze categorical data and compare sample proportions to a hypothesized value.
• Test for Equal Variance to verify assumptions for parametric tests or to compare the variability between groups.
• Determine Power and Sample Size to design experiments with adequate power to detect meaningful effects.
• Evaluate Correlation coefficients to measure the strength and direction of linear relationships between variables.
• Apply Simple Linear and Multiple Regression techniques to model relationships between variables and make predictions.
• Perform One-Way ANOVA to test for significant differences between means across multiple groups.
• Conduct Multi-Variable ANOVA to understand the effects of two or more categorical independent variables on a continuous dependent variable.