Hướng dẫn sử dụng phần mềm Minitab

Hướng dẫn sử dụng Minitab cụ thể, chi tiết. Có phần câu hỏi kiểm tra lại kiến thức. Hướng dẫn tất cả các công cụ trong minitab Hữu ích cho người mới làm quen, tiếp xúc với phần mềm liên hệ : dsnghia96gmail.com nếu có thắc mắc về bài giảng

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 Ryan

● Thomas A Ryan Jr

● Brian L Joiner

Simplification of input process

Why Minitab?

Provides a quick and

effective solution for

complex Six Sigma projects

Has a very user-friendly

interface

Has several features that help Six Sigma practitioners work with data and statistics

Minitab over Other Tools

Minitab Stats Package for

Social Sciences (SPSS)

Microsoft Excel

• Is easy to diagnose and correct errors

• Used for process improvement, quality management, and Six Sigma

• Contains excellent support and infrastructure blogs

• Has inbuilt functions, easy-to-make graphs, and

automated analysis of complex data

Stats Package for Social Sciences (SPSS)

• Does not offer any blog-based support

• Is used in research in the field of social sciences

• Offers automated analysis of data and make graphs

easy-to-• Is easy to diagnose and correct errors

• 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 impact, transformational projects

high-Target Audience

Professionals involved in process control, quality, and improvement

Aspirants for Lean improvement, waste reduction, production, and service efficiency

Aspirants for data analytics,

research, process engineering,

and reengineering initiatives

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

• 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

Regression

Course-end assessments

• PDF versions of the online self learning videos that learners can refer to

Ebooks

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 analysesList 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

Industry experts prefer Minitab over other conventional tools

Minitab is a much more proficient tool to perform an in-depth exploration.

Standout Features of Minitab

Conditional Formatting Project Manager Prediction

Enables you to predict the output based on the

entered values

Enables you to adjust between multiple worksheets, graphs, and statistical outputs

Enables you to apply a

different format to data

in every cell of a

columnThe features allow you to effectively structure and format data and generate relevant output

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:

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:

Three Common Pitfalls

Bias

Error in methodology

Problems in interpretation

Are you giving a fair representation of population parameters? Are you sampling only a positive representation of the population?

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

● 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

A few key questions that can help a Six Sigma professional to have an effective data

collection plan in place are:

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

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

● A word can have different meanings in different contexts.

● Significance is not used in the same sense in statistics as in real life

● 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 the relationship between variables

● With low power, one may ignore a useful relationship With high power, one may find minute effects that have no practical value

● Precision refers to how finely an estimate is specified.

● Accuracy refers to the difference between observed and standard value

● Estimates can be precise without being accurate

Example: When interpreting a decimal output with the fourth

or fifth decimal, one should not report any more decimal than necessary

● Data is analyzed to draw inferences or conclusions

● Any graphical representation must be able to explain the nature of data variation and help display the context of the data

● The key to drawing inferences from data depends on:

○ Appropriate tools and techniques

○ The nature of the data gathered followed by the its conditions and environment

Avoiding These Common Pitfalls

What can you do to ensure you avoid these pitfalls?

• Ensure that data is representative of the population It should be random and stratified It should also be

defined

• Apply the right tools to get accurate results

• Confirm that there are no measurement system errors while analyzing data

• Be trained to collect the data with an unbiased approach

• Form graphs in such a way that they reflect data variation

• Categorize the data by type

• Have an appropriate amount of statistical power and a clear understanding of the conditions for causal

inference

Key Takeaways

Minitab uses a series of elements to help Six Sigma

practitioners work with data and statistics

The three common pitfalls in analyzing data are bias, error in methodology, and problems in interpretation

Sub points to the major pitfalls include:

• Bias: Data collection plan and sampling strategy

• Error in methodology: Statistical power and measurement error

• Problems in interpretation: Significance, precision and

accuracy, graphical representation, and causality

Knowledge Check

Which of the following Minitab feature enables you to apply a different format to data

in every cell of a column?

Project manager windowPrediction feature

Conditional formattingNone of the options

Which of the following Minitab feature enables you to apply a different format to data

in every cell of a column?

The conditional formatting feature in Minitab enables you to apply a different format to data in every cell

2 The "Control Charts" option can be found under menu.

The Stat menu bar contains the "Control Charts" option.

B

DataStatGraphTools

Graphical Analysis

Learning Objectives

By the end of this lesson, you will be able to:

Generate bar charts and pie charts

Create pareto charts and histogram

Generate box plots, dot plots, and individual value plotsCalculate mean, median, and mode for a given data set Conduct normality test

Basic Statistics

Basic Statistics

Basic statistics refers to collecting and analyzing data from any improvement initiative

● Provides a numerical summary of the data being analyzed

● Provides the basis for making inferences about the future

● Sets the foundation for assessing the process capability

● Establishes a common language to describe processes throughout an organization

Types of Basic Statistics

● Infers the parameters of the populations from which the data is collected

● Draws statistical conclusions about the population by

analyzing sample data

● Describes a set of data

● Provides summaries about

a sample and its measures

in a quantitative manner

Standard Deviation

Variance Range

Case Study: Amazing Inc.

Amazing Inc is an HR outsourcing partner for leading companies in India

Many companies outsource their Employee Resource Centers or ERC to Amazing Inc

ERC is a function where employees update their personal and job-related records and seek

resolutions for their queries

Case Study: Amazing Inc.

Great American Publication (GAP) is one of the leading clients of Amazing Inc

GAP has outsourced its ERC to Amazing Inc for its office in California

Case Study: Amazing Inc.

Amazing Inc went live with the office in California in 2020

Liaisons from GAP observed that the hiring process was causing significant

delays in the transition of work from GAP to Amazing Inc

Case Study: Amazing Inc.

Amazing Inc has hired Gary, a Six Sigma professional, to:

Improve the overall hiring

process to ensure transition or

Case Study: Amazing Inc.

What was causing this increase in hiring cycle time?

The Six Sigma professional:

• Defined and identified the pain areas

• Conducted surveys and interviews

• Held discussions with recruiters, hiring managers, interviewers, and candidates

Graphical Analysis Tools

A graph is an effective way to visualize data patterns and provide key insights into the data

The visual analysis of the graph will qualify further investigation of the quantitative

relationship between the variables

Graphical Analysis Tools

Minitab has several graphical analysis tools such as:

Demo: Bar Chart

The data gathered is entered into a Minitab

worksheet and subject to analysis

The first step is to classify the collected

data into Nominal and Ordinal categories

Nominal data is categorized according to

descriptive or qualitative information, and

this indicates the relative importance of

parameters under consideration

Here, the graph shows each potential cause

responsible for the high hiring cycle time

with its value

Demo: Pie Chart

The Six Sigma professional further wanted

to examine the number of potential causes

in percentage or proportional data

This chart is a circular graph that resembles

a pie therefore deriving its name as pie

chart

The pie chart has been cut into different

sized slices (called as wedge) that show the

relative contribution that different

categories contribute to total

The pie chart is useful to get the relative

importance of the parameters under study

Demo: Pareto Chart

The current study is still under the Define

phase of their research

Pareto Analysis is used for selection of

problem(s) and initiating the improvement

projects during the Define phase

Pareto chart practices the 80/20 theory

As you can see in the Cumulative

percentage row, the first eight potential

causes are responsible for 80 percent of

the total issues in the system