Minitab: Course Introduction What Is Minitab? Minitab is a statistical software package designed for Six Sigma practitioners. It was developed at the Pennsylvania State University in 1972 by researchers: ● Barbara F. Joiner What Is Minitab? Simplification of input process Greater focus on identifying trends and patterns Analysis and interpretation of Automation of calculations data and results Find solutions to the Creation of graphs problem at hand Why Minitab? Provides a quick and Has a very user-friendly Has several features that effective solution for interface help Six Sigma practitioners complex Six Sigma projects work with data and statistics Minitab over Other Tools Minitab Stats Package for Microsoft Excel Social Sciences (SPSS) Minitab • Used for process improvement, quality management, and Six Sigma • Contains excellent support and infrastructure blogs • Is easy to diagnose and correct errors • Has inbuilt functions, easy-to-make graphs, and automated analysis of complex data Stats Package for Social Sciences (SPSS) • Is used in research in the field of social sciences • Does not offer any blog-based support • Is easy to diagnose and correct errors • Offers automated analysis of data and easy-to- make graphs MS Excel • Performs complex analysis by building macros and using formulas • Uses spreadsheets to organize data with formulas and functions • Has support functionality • Is cumbersome while analyzing and diagnosing an error • Has very few inbuilt functions • Does not support many Six Sigma tools Target Audience Students who need help in understanding the concepts of statistics and in applying the different methods to solve problems. ● Innovation, transformation, and change leaders ● Professionals managing Lean Six Sigma teams ● Lean Six Sigma practitioners involved in high- impact, transformational projects Target Audience Professionals involved in process control, quality, and improvement Aspirants for data analytics, Aspirants for Lean improvement, research, process engineering, waste reduction, production, and and reengineering initiatives service efficiency Target Audience ● Process improvement engineers ● Process improvement managers ● Students learning the Six Sigma methods ● Black Belts and Green Belts working on projects ● Anyone working to improve a process, a product, or service quality Prerequisites • Install the current version of Minitab • Have a computer with Windows 10 and at least 4GB of RAM • Understand the DMAIC method and tools used • Have an elementary knowledge of statistics Learning Outcomes By the end of this course, you will be able to: • List the various features of Minitab • Import data into Minitab from Excel and other sources • Create various graphs based on the data type and problem • Perform various statistical tests based on the type of problem and data set • Analyze the output of each test and graph • Monitor a process over time and find patterns within it • List down root causes and prioritize them • Build prediction models Course Outline Introduction to Minitab Graphical Analysis Control Charts Measurement System Analysis Process Capability Analysis Hypothesis Testing Correlation and Regression Analysis of Variance (ANOVA) Design of Experiments Course Components Knowledge checks • Multiple choice questions placed at the end of each lesson to check learners' understanding.
Course-end assessments • Quizzes conducted at the end of the course to check the learners’ overall knowledge of the concepts taught. Ebooks • PDF versions of the online self learning videos that learners can refer to. Introduction to Minitab Learning Objectives By the end of this lesson, you will be able to: Outline the importance of Minitab Use the Minitab to perform various analyses List the common pitfalls in data analysis Importance of Minitab The Minitab software is designed for the needs of Six Sigma practitioners. • It uses a series of elements to help Six Sigma practitioners work with data and statistics.
• The elements like box plots, scatter plots, and histograms collectively help calculate descriptive statistics. Microsoft Excel Excel is one of the most used software created by Microsoft. It offers a variety of features such as: • Storing and compiling data • Calculating, sorting and formulating data • Running pivot tables • Creating macro programming • Tools to create different graphics MS Excel is user-friendly for data analysts as it stores all graphs and formulae results and reflects all the data in an active worksheet. Standout Features of Minitab Minitab is a much more proficient tool to perform an in-depth exploration.
Industry experts prefer Minitab over other conventional tools. Standout Features of Minitab The features allow you to effectively structure and format data and generate relevant output. Conditional Formatting Project Manager Prediction Enables you to apply a Enables you to adjust Enables you to predict different format to data between multiple the output based on the in every cell of a worksheets, graphs, entered values column and statistical outputs Minitab Interface Install Minitab in your laptop or desktop from the official website. Minitab: Menu Bar The File menu is used to perform actions such as open, close, save, print, import data, or run the various file types that can be used in Minitab.
Minitab: Menu Bar The Edit menu provides options to edit, undo, redo, clear, delete, or clear data from cells in a worksheet. Minitab: Menu Bar The Data menu allows to perform complex actions that would be difficult or tedious to replicate in other ways. Minitab: Menu Bar The Calc menu enables to calculate mathematical expressions and transformations, individual row and column statistics, and center and scale columns of data. Minitab: Menu Bar The Stat menu enables to run different tests and retrieve the corresponding statistical information.
Minitab: Menu Bar The Graph menu provides flexible suite of graphs to support a variety of analysis needs. Minitab: Menu Bar The Editor menu consists of dynamic commands that change depending on the active window. Minitab: Menu Bar The Tools menu enables to open and use tools such a Calculator, or Notepad. This menu item enables to set General Settings and manage file security.
Minitab: Menu Bar The Window menu helps manage different windows used in the project. Minitab: Menu Bar The Help menu provides options to display table of contents, User Manual, Tutorial, and Glossary. Minitab: Menu Bar The Assistant menu provides aid for analytic options available in Minitab. Common Pitfalls in Analyzing Data An organization benefits when an expert delivers the work accurately.
If the work is not delivered per the requirements: Customers do not accept it Top management loses faith in such initiatives Affects employee morale Application Virtualization The Six Sigma professionals must analyze the collected data and working on it. This enables them to determine: Where we are. Where we need to go. Three Common Pitfalls Bias Error in methodology Problems in interpretation Bias Are you giving a fair representation of population parameters? Are you sampling only a positive representation of the population? Bias When you select your sample, be neutral in selecting them.
There should not exist any factor that will influence any of the other factors resulting in an unfair output of the process. Data Collection Plan To get sample without bias, it is important to have accurate details of the data collection plan in the Six Sigma journey. For your results to be bias free, have a clear idea of the reasons of data collection and the type of data that is required. Data Collection Plan A few key questions that can help a Six Sigma professional to have an effective data collection plan in place are: ● How will the data be collected? ● Who will collect the data? ● When will the data be collected? ● How much data should be collected? ● What data stratification would be required? Collect the data required for the analysis and deal with the problem at hand.
Sampling Strategy The next bias that comes while analyzing data is Sampling Strategy. Sampling strategy helps you decide if the data you currently have is enough, or if you need new data. Obtaining a new sample is a difficult and time-consuming procedure. Sampling Strategy Collecting data is a crucial part of applying Six Sigma methods to your problem.
Define Create a baseline for process output, which is Y Analyze Verify the suspected causes (X’s) of variation and defects Improve Quantify the effects of the solutions Control Control the X’s and monitor the Y To get accurate data, the most appropriate strategy to select samples is Random sampling or stratification. Error in Methodology Applying an inappropriate tool or technique to a problem can lead to inaccurate results. The two types of error: Statistical power Measurement error Statistical Power If there is little statistical power, one risks overlooking the effect that one is attempting to prove. In hypothesis testing, there is always a risk associated with the decision that is made.
Accept null hypothesis Reject null hypothesis Null hypothesis True Correct conclusion Type I error or 𝛂 error Null hypothesis False Type II error or 𝛃 error Correct conclusion Measurement Error Statistical models assume error-free measurement. When dealing with different types of data, pay closer attention to effects of measurement error. Any measurement system must be consistent, reliable, and valid. Problems in Interpretation The other problem that one encounters is incorrect interpretation of data.
The reasons that lead to Problems in Interpretation are: • Significance • Precision and accuracy • Graphical representation • Causality Problems in Interpretation ● A word can have different meanings in different contexts. Significance ● Significance is not used in the same sense in statistics as in real life. Precision and accuracy ● The term Significance in statistics is as much a function of sample size and experimental design as it is a function of the strength of Graphical the relationship between variables. representation ● With low power, one may ignore a useful relationship.
With high Causality power, one may find minute effects that have no practical value. Problems in Interpretation Significance ● Precision refers to how finely an estimate is specified. ● Accuracy refers to the difference between observed and Precision and standard value. accuracy ● Estimates can be precise without being accurate.
Graphical representation Example: When interpreting a decimal output with the fourth or fifth decimal, one should not report any more decimal than necessary. Causality Problems in Interpretation Significance ● Data is analyzed to draw inferences or conclusions. ● Any graphical representation must be able to explain the Precision and nature of data variation and help display the context of the accuracy data. Graphical ● The key to drawing inferences from data depends on: representation ○ Appropriate tools and techniques ○ The nature of the data gathered followed by the its conditions and environment Causality Problems in Interpretation Significance ● Assessing causality between variables is an outcome of any statistical analysis.
Precision and accuracy ● A statistical study must be able to show the cause-and-effect relationship and essential variation between the variables in Graphical the absence of any causal factors.