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# Pass Six Sigma LSSGB Exam in First Attempt Easily

## Latest Six Sigma LSSGB Practice Test Questions, Exam Dumps Accurate & Verified Answers As Experienced in the Actual Test!

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Exam Code: LSSGB
Exam Name: Lean Six Sigma Green Belt
Certification Provider: Six Sigma
Corresponding Certification: Lean Six Sigma
Bundle includes 3 products: Premium File, Training Course, Study Guide

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Last Update: Jan 18, 2023
• Training Course 212 Lectures
• Study Guide 664 Pages
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Exam Info
FAQs
Last Update: Jan 18, 2023
Includes questions types found on actual exam such as drag and drop, simulation, type in, and fill in the blank.
LSSGB Training Course
Duration: 23h 51m
Based on Real Life Scenarios which you will encounter in exam and learn by working with real equipment.
LSSGB Study Guide
664 Pages
The PDF Guide was developed by IT experts who passed exam in the past. Covers in-depth knowledge required for Exam preparation.
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### Part 1 - Lean Six Sigma Green Belt Introduction Video

1. About Excelr, Agenda For Introduction

2. Preliminaries of Lean Six Sigma

3. Six Sigma Management System

4. Six Sigma Maturity Continuum

5. Introduction to Elementary Statistics

Interaction to elementary statistics In this section, we will learn how to make inferences based on data provided to us. And of course, we'll be making use of a statistics tool called Minita in actually accomplishing this, right? Statistics is broadly divided into two branches. One is descriptive, one is inferential. Descriptive statistics focuses on how to collect data, how to analyse the data, and how to present and describe the data set that is provided to you. And what is "inferential statistics"? Inferential statistics is all about making decisions about a larger set of data, which is called a population. You make inferences about the population based on a small set of the data, which is called a sample. Think about Apple conducting a survey to collect what the bad features of the iPhone 6 are. Suppose this is what Apple wants to do. Can Apple go and approach each and every iPhone 6 user and collect the data? And what are the features they do not like the most? It will be extremely cumbersome. It would take another five years to collect the data, right? It's extremely difficult. So what do you do? Instead of approaching all iPhone 6 users, select a subset, a section of this population, and begin collecting data on which features they dislike. Once you collect the data, you analyse the data and make statements that will be applicable to the entire population.

So using a small sample, you are going to make statements about the population that are inferential statistics. All right. We will also explore the statistical tools and techniques that enable decision making, which would include what are the data types, what are the measures of central tendency, what are the measures of dispersion, and what is the probability distribution? There's also beta distribution, gamma distribution, binomial post, and so on. But we'll be primarily discussing normal distribution in this section and its applications, basically. And also, we look into how to graphically represent the data so that it's extremely relevant for us. Rather than crunching a huge set of numbers, there is a famous saying that a picture speaks ten words. As a result, we will investigate a few graphical representations such as box plots, etc. Let us look into the two main data types. The first data type is attributed, which is also called categorical data. What can be done using categorical data? You can count it, you can rank it in order, or you can sort it in ascending or descending order. But you cannot perform any of the mathematical operations on it. You cannot add; you cannot subtract; you cannot divide, right? So these kinds of operations would not be possible with categorical data or attribute data. attribute data or categorical data. Here is an example. It's also called discrete data or deficit data, right? Alright, that's fine. So, discrete data: number of cars I can see that I have one car, or I have two cars, or three cars, or four cars, or five cars, but can I say I have 1.5 cars? Or can I say I have 2.5 cars, or can I say I have 3.6 cars? Does it make sense? It's not going to make any sense, right? You can have one car or two or three or four or five cars, but you cannot have zero cars in decimal format. So the moment I divide the number, it's not making sense for me.

And that is a way to identify tribute data, categorical data, or discrete or deficit data. Or it can also be something like this I have so many red balls and so many blue balls. Gender, male or female, though there are genders, is not something we will discuss, good or bad. So these kinds of things are called attribute or categorical data. On the other side of the spectrum, we have continuous data, which is also called variable data, which can be measured on a scale and can be divided nightly into parts. And it's still going to make sense if you have the examples. Wait, I can go to any accuracy level. I can say that I weigh 80 gauges, or I can say I weigh 80.5 gauges, or I can say I weigh £160, or I can say I weigh 160 points at £.9, or I can say I weigh 170 points at £68. I can go to any accuracy level possible andit's still going to make sense for me. I can say I arrived office in 1 hour, orI can say I arrived office in 60 minutes, Ican say I've arrived office in 3600 seconds. It's still going to make the same sense for me, right? You can do as much wide continuous data as you want, and it will still make sense. unlike your attribute and categorical data.

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Last Update: Jan 18, 2023
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