### Welcome to the course

**Syllabus:** Industrial Engineering, Quality Control and Management**Course:**** **Information Technologies for Statistical Analysis and Quality Control**Total:** 30 hours

### Welcome to the course

**Syllabus:**Industrial Engineering, Quality Control and Management**Course:****Total:**30 hours### Welcome to the course Information Technologies for Statistical Analysis and Quality Control

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)****7. Testing of non-parametric statistical hypothesis****8. Testing of parametric statistical hypothesis****10. Analysis of variance (ANOVA)**### 1. The Seven Basic Quality Control Tools

This lesson describes the Seven Basic Quality Control 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

### 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.

### 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:

and the following continuous distributions:- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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:Comparison between the Median and Mean is given.The lesson continues with a description of the Variability.The included measures of variability are:Comparison between the Standard Deviation and Variance is given.- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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.- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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.

- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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.

- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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.

- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 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.

- Here you can find the examples from the lesson, developed with:MS Excel (for Students)Minitab (for Stuff)Python (for Trainers/Researchers)

### 12. Design of experiments

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.

### 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