Information Technologies for Statistical Analysis and Quality Control

Section outline

• Select section Welcome to the course Information Technologies for Statistical Analysis and Quality Control

  Welcome to the course Information Technologies for Statistical Analysis and Quality Control

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  Course Duration: 30 hours

  
  

  Course objectives:

  Information technology systems can improve quality and increase productivity. In addition to software different statistical methods can be used to evaluate critical processes in order to control production procedures and performances. The course in Information Technologies for Statistical Analysis and Quality Control includes basic issues of mathematical statistics and its application for the needs of the textile and clothing industry. The training will be carried out mainly with universal and, if necessary, with specialized software products.
  

  
  

  Learning outcomes:

  Knowledge	Skills	Responsibilities/autonomy
  To understand the basics of the quality control tools To aware and understand the essentials of QFD To comprehend the elements of FMEA method To understand different probability distribution, parameters and properties related to textile variables To apprehend how to treat random variables in order to consider them in the quality control process To adopt different kind of hypothesis testing to support your decisions To comprehend variance and regression analysis principles	To solve problems using the quality control tools To respond to the needs and expectations of the customers using QFD To take actions to eliminate or reduce failures using FMEA To represent test results using different methods To use specialised software for data analysis to support quality control To apply statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS) To apply variance and regression analysis to concrete cases both for forecasting and control purposes	To assure and manage the quality control process by using the quality control tools To bring new and improved products to market while reducing development time To documents current knowledge and actions about the risks of failures To optimize the regression model in order to get to the best possible hypothesis considering data variables- To support the quality control process by applying relevant statistical models

  
  
  

  Syllabus:

  The course in Information Technologies for Statistical Analysis and Quality Control includes basic issues of mathematical statistics and its application for the needs of the textile and clothing industry.

  The training will be carried out mainly with universal and, if necessary, with specialized software products, through lessons:

  1. The Seven Basic Quality Control Tools

  2. The Seven Management and Planning Tools

  3. Quality function deployment (QFD)

  4. Failure modes and effects analysis (FMEA)

  5. Probability distributions with application in textile practice

  6. Statistical estimates of random variables

  7. Testing of non-parametric statistical hypothesis

  8. Testing of parametric statistical hypothesis

  9. Application of statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS)

  10. Analysis of variance (ANOVA)

  11. Regression analysis

  12. Design of experiments

  
  
  

  • Select activity Announcements

    

    Announcements Forum
• Select section 1. The Seven Basic Quality Control Tools

  1. The Seven Basic Quality Control Tools

  This lesson describes the Seven Basic Quality Control Tools:

  Cause-and-effect diagram, Check Sheets, Control charts, Histogram, Pareto chart, Scatter diagram and Stratification
  

  
  
  

  • Select activity The Seven Quality Control Tools

    

    The Seven Quality Control Tools Lesson
• Select section 2. The Seven Management and Planning Tools

  2. The Seven Management and Planning Tools

  This lesson describes the The Seven Management and Planning Tools:

  Affinity Diagram, Relations diagram, Tree diagram, Matrix diagram, Prioritization matrix, Arrow diagram and Process Decision Program Chart
  

  
  

  
  

  • Select activity The Seven Management and Planning Tools

    

    The Seven Management and Planning Tools Lesson
• Select section 3. Quality Function Deployment (QFD)

  3. Quality Function Deployment (QFD)

  The QFD process includes different quality control tools. In this lesson only one of these tools will be included and it is called the House of Quality. Other tools used in the QFD process for example are the 7 basic quality control tools and the 7 management and planning tools.

  
  

  
  

  • Select activity Quality Function Deployment (QFD)

    

    Quality Function Deployment (QFD) Lesson
• Select section 4. Failure Modes and Effects Analysis (FMEA)

  4. Failure Modes and Effects Analysis (FMEA)

  This lesson deals with FMEA

  
  

  • Select activity Failure Modes and Effects Analysis (FMEA)

    

    Failure Modes and Effects Analysis (FMEA) Lesson
• Select section 5. Probability Distributions with Application in Textile Practice

  5. Probability Distributions with Application in Textile Practice

  This lesson deals with the probability distributions with application in textile practice.

  
  

  The lesson starts with an brief explanation of Probability density functions.
  

  It also includes the following discrete distributions:

  • Binomial Distribution
    
  • Poisson Distribution
    

  
  

  
  

  
  

  and the following continuous distributions:

  • Normal Distribution
    
  • Standard Normal Distribution
  • t Distribution
  • Chi-Square Distribution
  • F distribution

  
  

   
  

  • Select activity Probability Distributions with Application in Textile Practice

    

    Probability Distributions with Application in Textile Practice Lesson
  • Select activity Probability distributions - Examples

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Probability distributions - Examples
      • 00_probability_distributions_Excel.xlsx
      • 01_probability_distributions_Minitab.mpx
      • 02_probability_distributions_Python.html
• Select section 6. Statistical Estimates of Random Variables

  6. Statistical Estimates of Random Variables

  This lesson is related to the measures of the central tendency and the measures of variability.

  
  

  
  

  

  
  

  The included measures of the central tendency are:

  • Mean
  • Weighted Mean
  • Median
  • Mode

  Comparison between the Median and Mean is given.

  
  

  
  

  
  

  
  

  The lesson continues with a description of the Variability.

  
  

  
  

  

  
  

  The included measures of variability are:
  

  
  

  • Range
    
  • Standard Deviation
    
  • Variance

  
  

  Comparison between the Standard Deviation and Variance is given.

  
  

  
  

  
  

  • Select activity 6. Statistical Estimates of Random Variables

    

    6. Statistical Estimates of Random Variables Lesson
  • Select activity Statistical estimates of random variables - Examples

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Statistical estimates of random variables - Examples
      • 00_statistical_estimates.xlsx
      • 01_statistical_estimates.mpx
      • 02_statistical_estimates.html
• Select section 7. Testing of Non-parametric Statistical Hypothesis

  7. Testing of Non-parametric Statistical Hypothesis

  This lesson deals with nonparametric statistical tests. The usage of these tests allows analyzing data that come as frequencies, such as the number of people in a sample who fall into different categories of age, income, and job classification.

  Examples for Chi-Square Goodness-of-Fit Test and Chi-Square Test of Independence are given.

  
  

   
  

  • Select activity Testing of Non-parametric Statistical Hypothesis

    

    Testing of Non-parametric Statistical Hypothesis Lesson
  • Select activity Nonparametric Statistical Tests - Examples

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Nonparametric Statistical Tests - Examples
      • 00_nonparametric_statistical_tests.xlsx
      • 01_nonparametric_statistical_tests.mpx
      • 02_nonparametric_statistical_tests.html
• Select section 8. Testing of Parametric Statistical Hypothesis

  8. Testing of Parametric Statistical Hypothesis

  This lesson deals with the parametric statistical tests.

  Examples for One-sample z-test and t-Test for Independent samples are given.

  
  

    
  

  • Select activity Testing of Parametric Statistical Hypothesis

    

    Testing of Parametric Statistical Hypothesis Lesson
  • Select activity Testing of Parametric Statistical Hypothesis

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Testing of Parametric Statistical Hypothesis
      • 00_testing_parametric_statistical_hypothesis.xlsx
      • 01_testing_parametric_statistical_hypothesis.mpx
      • 02_testing_parametric_statistical_hypothesis.html
• Select section 9. Application of statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS)

  9. Application of statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS)

  The first part of this lesson deals with the Statistical Process Control (SPC).

  The main tools of SPC are discussed: The Control Charts for Variables and the Control Charts for Attributes.
  

  

  
  

  The second part of the lesson deals wit the Acceptance Sampling (AS)

  The OC Curve is described.

  
  

  Different sampling plans are presented: Single Sampling Plan, Double Sampling, Multiple Sampling and Sequential Sampling.
  

  
  

  • Select activity Application of statistical hypothesis tests in Statistical Process Control and Acceptance Sampling

    

    Application of statistical hypothesis tests in Statistical Process Control and Acceptance Sampling Lesson
  • Select activity Application of statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS)

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Application of statistical hypothesis tests in Statistical Process Control (SPC) and Acceptance Sampling (AS)
      • Acceptance Sampling
        • 00_acceptance_sampling.xlsx
        • 01_acceptance_sampling.mpx
      • Statistical Process Control
        • 00_statistical_process_control.xlsx
        • 01_statistical_process_control.mpx
• Select section 10. Analysis of variance (ANOVA)

  10. Analysis of variance (ANOVA)

  This lesson is about the Analysis of variance (ANOVA).
  

  
  

  ANOVA comes in different flavors. The simplest kind is the Single Factor ANOVA.

  Two-Factor ANOVA Without Replication is used to estimate how the mean changes according to the levels of two categorical variables.

  In Two-Factor ANOVA With Replication each combination of factor levels has several sample elements.

  
  

  • Select activity Analysis of Variance (ANOVA)

    

    Analysis of Variance (ANOVA) Lesson
  • Select activity ANOVA - Examples

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • ANOVA - Examples
      • 00_anova.xlsx
      • 01_anova.mpx
      • 02_anova.html
• Select section 11. Regression Analysis

  11. Regression Analysis

  This lesson discuses the most straightforward regression type - the simple linear regression.

  
  

  The lesson gives the essentials of the Simple Linear Regression.

  Another topic of the lesson deals with the The Method of Least Squares.

  
  

  How Good Is the Regression? The lesson gives an answer to question with the help of the coefficient of correlation and the coefficient of determination.
  

  
  

  • Select activity Regression Analysis

    

    Regression Analysis Lesson
  • Select activity Regression Analysis - Examples

    Here you can find the examples from the lesson, developed with:

    MS Excel (for Students)

    Minitab (for Staff)
    

    Python (for Trainers/Researchers)

    Download folder

    • Regression Analysis - Examples
      • 00_simple_linear_regression.xlsx
      • 02_simple_linear_regression.mpx
      • 03_simple_linear_regression.html
• Select section 12. Design of experiments

  12. Design of experiments

  • Select activity Design of Experiments

    

    Design of Experiments Lesson

    This lesson is adopted from the book "Design of Experiment and Optimization of Textile Processes" by Prof. Georgi Damyanov and  Prof. Diana Germanova-Krasteva

    It includes topics like Main Concepts in Mathematical Modeling and Optimization, Choice of Parameters of Optimization, Choice of Input Factors and Main Stages of Experimental Modeling.

    
    

    
    

• Select section References

  References

  • Select activity Reference List

    

    Reference List Page
• Select section Quiz

  Quiz

  • Select activity Information Technologies for Statistical Analysis and Quality Control Quiz

    

    Information Technologies for Statistical Analysis and Quality Control Quiz

    There are 60 questions related to the lessons.

    The quiz is time limited to 60 minutes.

    Each question gives 1 point.

    40 - 45 points - Average

    46 - 50 points - Good

    51 - 55 points - Very Good

    56 - 60 points - Excellent