Statistics for Six Sigma and Business with ChatGPT

Name: Statistics for Six Sigma and Business with ChatGPT
Rating: 4.7 (22 reviews)

Data Cleaning and Statistics for Lean Six Sigma Projects, Quality Improvement, and Business Insights Using AI.

Highest Rated

Created bySandeep Kumar Quality Gurus Inc.

Last updated 7/2025

English

English [Auto],Korean [Auto],

What you'll learn

Master the fundamentals of statistics with direct application to Six Sigma and business processes.
Apply a structured, 7-step data cleaning process using a custom-designed ChatGPT workflow.
Analyze real-world datasets to calculate descriptive statistics like mean, median, mode, variance, and standard deviation.
Understand and apply probability concepts and probability distributions including Binomial, Poisson, and Normal.
Leverage ChatGPT as your AI-powered assistant for data analysis, visualization, and decision support.
Build confidence to interpret statistical outputs and make data-driven decisions in professional environments.

Course content

4 sections • 48 lectures • 4h 48m total length

Promotional Video2:08
Learn practical statistics for six sigma and business with ChatGPT, from data cleaning to descriptive statistics and key distributions like binomial, Poisson, and normal, using AI guidance to drive improvements.
Quality Gurus Inc Certificate, Digital Badge, PMI PDUs, SHRM PDCs (Optional)2:53
Introduction and Welcome5:55
This course teaches statistics for Six Sigma and business using ChatGPT, focusing on data cleaning, descriptive statistics, probability distributions, and visualization to enable fast, accessible data-driven decision making.
Introduction to Generative AI and ChatGPT5:37
Introduce generative AI and ChatGPT fundamentals, including GPT and multimodal GPT-4o. Compare free and plus plans and discuss data interpretation and hypothesis testing in statistics.
Key Statistical Tems and Concepts4:07
Explore key statistical terms: population, sample, parameter, and statistic, and learn how ChatGPT supports understanding statistics and estimating population characteristics for Six Sigma projects.
Data Types6:50
Explore data types in statistics: distinguish quantitative and qualitative data, with continuous vs discrete, and nominal vs ordinal, plus scales like interval and ratio.
Measurement Scales4:34
Explore the four scales of measurement—nominal, ordinal, interval, and ratio—and learn how zero meaning affects arithmetic, with examples like defects (nominal), customer satisfaction (ordinal), Celsius temperature (interval), and weight (ratio).
Descriptive vs Inferential Statistics2:03
Explore descriptive statistics, which summarize data with charts or tables, mean, median, and standard deviation, and distinguish them from inferential statistics used to predict populations from samples.
Introduction to Datasets and Data Cleaning11:56
Learn to apply descriptive and inferential statistics to real-world data, including data cleaning, using ChatGPT to compute mean, median, standard deviation, variance, and visualize results.
Data Cleaning Step 1: Missing Values5:05
Identify and address missing values to prevent biased results, and explore causes like manual entry errors and system failures. Apply deletion, mean/median/mode imputation, or time-series predictive methods, with ChatGPT assistance.
Data Cleaning Step 1: Missing Values (ChatGPT Demo)6:14
Apply step-by-step data cleaning to address missing values with ChatGPT's data cleaner, imputing dates and call durations, and detect duplicate records in unclean datasets like call resolution and waiting time.
Data Cleaning Step 2: Duplicate Records2:28
Identify and remove duplicate rows to prevent inflated counts and skewed statistics; learn detecting duplicates without Python using Excel, keeping the first occurrence, with preview of using Excel and ChatGPT.
Data Cleaning Step 2: Duplicate Records (ChatGPT Demo)2:22
Identify and remove duplicate records using Excel's remove duplicates, selecting the full data range to check all columns, demonstrated in a ChatGPT demo for data cleaning step 2.
Data Cleaning Step 3: Outliers5:40
Identify and handle outliers in data cleaning using box and whisker plots, histograms, z scores, and interquartile range to protect the mean and control charts.
Data Cleaning Step 3: Outliers (ChatGPT Demo)4:50
Identify and address outliers in duration minutes using a box and whisker plot and IQR in a ChatGPT demo, including 120-minute examples and winsorizing or custom thresholds.
Data Cleaning Step 4 to 7: Various Other Issues4:56
Identify and correct inconsistent categories, data type errors, and logic or range issues in datasets. Fix formatting such as misspellings, trailing spaces, and date formats to improve grouping and analysis.
Data Cleaning Step 4 to 7: Various Other Issues (ChatGPT Demo)10:35
Discover data cleaning steps four through seven using a customized ChatGPT workflow to fix inconsistent categories, apply fuzzy matching, standardize formatting, and generate a clean CSV dataset.
Data Cleaning of a Untidy Dataset Using ChatGPT18:40
Clean a messy patient wait time dataset using a chatgpt data cleaner assistant, address missing values, duplicates, outliers, and formatting through stepwise checks and an audit log.
How to Create Your Own GPT?7:11
Learn how to create your own GPT by specifying instructions, configuring settings, and naming or sharing options for data cleaning and column summary tasks.
DOWNLOAD Course Slides0:02
Introduction to Statistics and Data Cleaning

Descriptive Statistics4:43
Explore descriptive statistics by summarizing data with central tendency and dispersion, using mean, median, and mode, and visualize with histograms and box plots, including a practical waiting-time example.
Measurement of Central Tendency8:53
Explore the mean, median, and mode as measures of central tendency, with real-world waiting-time examples, outliers, skewed data, and histogram interpretation.
Measurement of Dispersion11:38
Explore the measurement of dispersion alongside central tendency, using range, variance, and standard deviation to interpret data reliability, normal distribution, and chatgpt-assisted analysis in process improvement.
ChatGPT Demo - Central Tendency4:01
Explore central tendency with descriptive statistics for waiting time, using Python code via ChatGPT. Learn mean and median, note mode is not meaningful for continuous data.
Why Data Visualization?5:54
Show how descriptive statistics can mislead, using Anscombe's quartet to reveal mean values for x and regression lines but different data shapes, emphasizing the need for graphical data visualization.
Data Visualization - Bar Plot4:46
Learn how bar plots visualize the frequency or proportion of discrete categories, such as severity levels and daily patient counts, as part of the three plots in this visualization series.
Data Visualization - Box Plot7:28
Understand box plots for continuous data like waiting time, with a box from Q1 to Q3, a median line, whiskers to min and max, and outliers beyond 1.5x IQR.
Data Visualization - Histogram7:57
Learn how histograms visualize continuous data using bins and frequencies, interpret shapes from normal, skewed, and bi-modal distributions, and compare with box-and-whisker plots to identify outliers.
Data Visualization - ChatGPT Demonstration9:41
Visualize patient waiting time data with ChatGPT by generating bar charts, box plots, and histograms from features like arrival shift, day of week, severity, and staffing level, addressing data cleaning.
Descriptive Statistics

Introduction to Probability3:36
Explore the foundations of probability, including the normal probability distribution, binomial and Poisson distribution, and learn how probability equals favorable outcomes over total outcomes through coin and weather examples.
Calculating Probability - Simple Examples3:48
Learn the probability formula by calculating favorable over total outcomes through simple examples: coin flips, a six-sided die, and a marble draw, including blue, red, and not red cases.
Basic Terms and Rules in Probability9:10
Learn basic probability terms such as experiments, sample space, and events, and apply the complementary, addition, and multiplication rules through coin flips, dice rolls, marbles, and defects.
Probability Distributions - Introduction6:52
Explore the binomial distribution through real-world two-outcome scenarios with independent trials and a fixed number of trials, using syringe defects and treatment success as examples.
Probability Distributions Professional Examples6:52
Explore how binomial distribution models two-outcome, independent trials with constant success probability, from syringe defects to patient recoveries, counting successes in fixed trials.
Binomial Distribution Introduction7:44
Explore the binomial distribution: model the probability of x successes in n independent trials with two outcomes and fixed p. Use a practical sampling plan example and ChatGPT for calculations.
Binomial Distribution Using ChatGPT4:48
Demonstrate binomial distribution using ChatGPT to calculate the probability of accepting a lot with at most one defect in 100 items (defect rate 1.5%), and compare exact versus rounded results.
Poisson Distribution Introduction7:47
Explore the Poisson distribution for discrete events per time or space, using lambda as the average number of occurrences and its probability formula.
Poisson Distribution Using ChatGPT - Example 13:42
Shows how to solve a Poisson distribution problem using ChatGPT, with three calls per minute when the average is two, via a step-by-step demonstration of the Poisson pmf.
Poisson Distribution - Example 26:14
Explore Poisson distribution conditions for counting rare, independent events in fixed intervals, apply lambda, and use the fabric-roll defect example to calculate probabilities with ChatGPT.
Poisson Distribution Using ChatGPT - Example 22:51
Explore Poisson distribution with ChatGPT, solving defects probabilities and plotting discrete data distributions, and contrast with binomial, preparing for the normal distribution next.
Normal Distribution Introduction3:16
Explore the normal distribution, a continuous probability distribution that models natural variation using the mean and standard deviation, producing a bell-shaped curve.
Key Features of a Normal Distribution8:20
Identify the normal distribution's key features: symmetry with mean = median = mode, the standard normal with mean zero, and range probabilities via the 68-95-99.7 rule.
Standard Normal Distribution7:01
Convert any normal distribution to a standard normal distribution to simplify area calculations with z-values, then use the standard normal table to find areas and probabilities.
Normal Distribution - Example4:57
Apply a normal distribution example to quality control: rods with diameter mu=50 and sigma=2 have 68% between 48 and 52, with 16% below 48, via z-values.
Normal Distribution Using ChatGPT - Example 14:41
Use ChatGPT to solve a normal distribution with mean 50 and sd 2, finding the 48–52 area is about 0.6826 (68%) and the less-than-48 area is 15.87% with a plot.
Normal Distribution Using ChatGPT - Example 28:40
Learn to fit a normal distribution to real waiting-time data, estimate mu and sigma, and test normality with Shapiro‑Wilk, Anderson‑Darling, and QQ plots; explore alternatives when data isn’t normal.
Normal Distribution Using ChatGPT - Example 37:36
Use ChatGPT to assess normal distribution of 100 dimension measurements, compute mean and standard deviation, and apply Shapiro‑Wilk, D’Agostino and Pearson, and Anderson‑Darling tests to estimate rejection for 49–51 mm.
Probability and Probability Distributions

Requirements

No prior programming or advanced math background needed.
Familiarity with basic business processes is helpful but not required.

Description

In today’s data-driven business environment, the ability to analyze and interpret data effectively is no longer optional. It is a critical skill for driving process improvements, making informed decisions, and achieving Six Sigma excellence. However, for many professionals, statistics can seem intimidating, overly complex, and disconnected from real-world applications.

This course, “Statistics for Six Sigma and Business Using ChatGPT”, has been created to address that challenge directly. Guided by Sandeep Kumar, a seasoned quality and data professional with over 40 years of hands-on experience, you will learn how to simplify statistics and apply it confidently in practical business contexts. Whether you are involved in manufacturing, healthcare, logistics, or service industries, this course provides the tools and knowledge needed to transform data into actionable insights.

Designed for Six Sigma practitioners, business analysts, and anyone working with process data, the program focuses on real-world relevance. You will not only learn statistical concepts but also how to implement them using ChatGPT as your AI-powered assistant. By the end of this course, you will be equipped to lead data-driven initiatives and contribute to continuous improvement efforts in your organization.

What Makes This Course Unique

This is not a traditional statistics course that overwhelms learners with formulas and theoretical derivations. Instead, it combines core statistical principles with modern AI capabilities to create a unique and practical learning experience.

The integration of ChatGPT sets this course apart. You will discover how this AI tool can support you by:

Cleaning and preparing messy, real-world datasets with speed and accuracy.
Performing step-by-step statistical analysis and providing plain-language explanations of each output.
Generating professional-quality charts, graphs, and summaries for effective communication.
Freeing your time to focus on interpreting results and making business decisions rather than manual calculations.

The course is built around realistic datasets that reflect actual challenges faced in manufacturing, healthcare, and service environments. This ensures that your learning is directly applicable to your workplace and your Six Sigma projects.

What’s Inside the Course

Module 1: Introduction to Statistics and Lean Six Sigma Context

Understand why statistics is a cornerstone of Lean Six Sigma and business improvement.
Explore the role of ChatGPT in supporting data analysis and decision-making.
Learn a structured seven-step process for preparing real-world datasets for analysis.
Address common issues such as missing data, duplicate entries, and inconsistencies using AI assistance.

Module 2: Descriptive Statistics – Summarizing Data

Master key measures of central tendency including mean, median, and mode.
Understand measures of dispersion such as range, variance, standard deviation, and interquartile range.
Use visual tools including histograms, boxplots, Pareto charts, and run charts to explore and communicate data.

Module 3: Probability Concepts and Probability Distributions

Gain a solid foundation in probability theory with practical, business-focused examples.
Learn how probability underpins decision-making in Six Sigma projects.
Study key probability distributions: Binomial, Poisson, and Normal.
Understand how each distribution applies to real-world business situations.
Explore standardization and Z-scores to assess process capability and performance.

Who this course is for:

Six Sigma Green Belts and Black Belts seeking to strengthen their statistical skills.
Business professionals working in quality, operations, or analytics roles.
Anyone dealing with process data who wants to make data-driven decisions.
Learners who prefer a practical, application-focused approach over theoretical derivations.

Statistics for Six Sigma and Business with ChatGPT

What you'll learn

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Course content

Introduction and Data Cleaning20 lectures • 1hr 54min

Descriptive Statistics9 lectures • 1hr 5min

Probability and Probability Distributions18 lectures • 1hr 48min

BONUS1 lecture • 1min

Requirements

Description

Who this course is for: