Six Sigma Statistic With Minitab: Time Series Plot

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Six Sigma Statistic With Minitab: Time Series Plot
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 251.81 MB | Duration: 0h 39m

Six Sigma Statistics with Minitab: Uncovering Trends Using Time Series Plots

What you'll learn

articipants will be able to create, customize, and export time series plots in Minitab.

Understand the fundamentals of time series analysis: Participants will learn the basics of time series, including seasonal patterns, trends, random variations.

Create time series plots in Minitab: Participants will be able to import time series data and graphically represent it using Minitab.

Interpret time series plots: Participants will gain the skills to identify trends, seasonal variations, and anomalies from time series charts.

Participants will confidently present the results of time series analysis to support decision-making processes.

Requirements

No prerequisites or prior knowledge are required.

Description

In this course, participants learn how to manage, cleanse, and analyze large datasets using Minitab's time series plot. The dataset, spanning 20 years of production at Smartboard Company, focuses on scrap rates in skateboard manufacturing. Key skills includeata Import and Cleansing: Extracting relevant data by removing non-production days (weekends, holidays) and unreliable data before January 1, 2017. Participants also learn to create new worksheets by filtering and refining raw data for better accuracy and usability.Trend Visualization: Creating and refining time series plots to identify chronological trends and tendencies in scrap rates. This includes exploring patterns over time, understanding seasonal variations, and isolating significant factors that influence production quality.Seasonal Analysis: Exploring patterns such as higher scrap rates during summer vacations and Christmas due to temporary staff. By identifying these inefficiencies, participants learn how to recommend actionable improvements to reduce waste and optimize workflows.Presentation: Exporting visualizations and worksheet information to PowerPoint for clear documentation and reporting. This ensures participants can effectively communicate insights and proposed solutions to key stakeholders and decision-makers.By the end of the course, participants will understand how to refine datasets, visualize trends, and apply statistical insights to address real-world challenges in quality management. They will gain hands-on experience with Minitab tools, enhancing their ability to extract actionable information and improve operational efficiency effectively. This combination of practical skills and analysis techniques prepares participants to drive impactful, data-driven decisions in their organizations.

Overview

Section 1: Time series plot with Minitab

Lecture 1 Preperation: Minitab User Interface

Lecture 2 Introduction and Business Case for time series plot analysis

Lecture 3 Data Import, Preparation, and Visualization with Minitab

Lecture 4 Generating and Interpreting a Time Series Plot

Lecture 5 Extracting and Refining Data for Time Series Analysis

Lecture 6 Refining Data and Analyzing Outliers in Time Series Plots

Lecture 7 Removing Holidays and Interpreting Seasonal Trends in Time Series Plots

Lecture 8 Investigating Scrap Rate Trends and Final Recommendations

Lecture 9 Final Insights, Data Cleaning, and Presentation

Lecture 10 Key Learnings from the Training Session

Beginners in Data Analysis: Individuals taking their first steps in data analysis and visualization who want to use a user-friendly tool like Minitab. No prior knowledge of statistics or Minitab is required.,Professionals and Managers: Decision-makers who need to analyze time series data to identify trends and patterns for strategic or operational decisions.,Analysts and Data Scientists: Professionals who regularly work with time series and are looking for an additional practical method to visualize and interpret data using Minitab.,Students and Researchers: Individuals in fields such as business, engineering, natural sciences, or social sciences who need to analyze time series data as part of their projects or theses.,Companies and Teams: Groups aiming to better understand and visualize process or production data, such as in quality management, supply chain management, or marketing.

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