 To my course on Quality Control and Improvement with Minitab, I am Professor Indrajit Mukherjee from Shailesh J. Mehta School of Management Indian Institute of Technology Bombay. So, I will give a brief introduction to this course ah what are the topics that we will cover but before that let me just highlight what are the ah reference book that I will suggest you ah to study ah to go through this course. So, one is Applied Statistics and Probability for Engineers by Montgomery and Roongar, then introduction to statistical quality control also by DC Montgomerys. So, then Douglas Montgomerys, then design and analysis of experiments by same author, then Amitabh Mithra's book on Fundamentals of Quality Control and Improvement, then Westerfield's book on Total Quality Management and the Management and Control of Quality by Evans. These are the reference book which you can see ah because the examples that we will discuss is taken from ah some of these books ok. So, ah what are the topics that we will cover we will cover in quality. So, ah the key topic that we will cover ah is quality of design and quality of conformance. So, within that ah we will try to restrict our discussion on quality of design and quality of conformance. Within quality of design what are the key topics that we will discuss is basically we will try to understand ah voice of the customer, how the voice of the customer are identified ah and then Gano model which helps to identify critical voice of the customer that is which is prioritized the voice of the customer. And then we will also highlight about critical to quality characteristics and the linkage between voice of the customer and critical to quality characteristics by using house of quality or QFD quality ah function deployment ah matrix. So, in that case we will see that one. Then we will talk about also failure mode and effect analysis in design ah and which can be implemented also in process. So, ah this is one important aspects with example we will try to discuss and then we will move forward main part of the course that is ah what is ah quality of conformance and ah quality of conformance means whenever it is ah design is freezed and in that case it comes to production and we have to ensure that ah whatever is the key design parameters are within the specified limits ah that is that is defined by the designer. So, in that case ah we want to ensure the quality aspects of that which is coming out of the process basically and deliver to the end user ok. So, for that what is required what is coming out of the process that key characteristics we need to measure that one and try to visualize the data and based on that we need to infer whether the ah things are ah going as per what is required ok. So, data visualization so some histogram ah some box plot we will try to visualize the data like that. So, ah that is data visualization using Minitab interface. So, we will introduce Minitab 19 interface and then we will also talk about Pareto chart techniques and cause and effect using examples like that ok. Then the main main part of ah quality control ah we will discuss using Minitab example. So, in control phase what we will discuss is that statistical process control approaches one is variable control chart and then we will talk about attribute control chart with examples ah and solve it in Minitab. So, we will give some examples over here. So, variable control chart means X bar R chart X bar S chart and all these charts we will discuss attribute P chart C chart all these things we will discuss over here ok. Then we will try to see that the process data that is coming out ah coming out ah while we are manufacturing. So, whether it is capable or not that means process is capable or not. So, process capability analysis we will try to understand over here. Then process performance, long term process performance we will discuss and sigma level of the process also we will discuss over here and z match mark values that we will also discuss over here. So, ah and with in Minitab interface only. So, all these quality control techniques we will discuss in Minitab interface and then before we start the improvement phase of quality what is required is some basic understanding on statistics. So, we will we will touch upon hypothesis testing. We will discuss about the important techniques used in design of experiment analysis of variance and then we will see non-parametric options for this doing hypothesis testing and analysis of variance. Then we will talk about developing response surface. So, for that simple regression and multiple regression are the two techniques that is used widely classical techniques. So, those two techniques we will discuss briefly on this in the sessions on basic statistics. So, then we will we will also try to discuss measurement system analysis which is an important part because if the instrument that is measuring the dimensions is not correct in that case and all the analysis will be wrong all the interpretation will belong all conclusion will belong. So, in that what we will study is that then measurement system bias, linearity, stability, gauge repeatability and reproducibility. So, variability aspects of gauge that we will try to understand and and what is the what is the industry standards to say that the instrument is correctly measuring and or the instrument needs to be replaced like that. So, that all that all the things we will discuss in measurement system analysis. Then we will go to improvement phase that is the most important phase which is improvement quality improvement phase and we will talk about design analysis of experiments that is systematic experimentation and or statistical experimentation basically we will discuss over here. So, 2 k factorial design that is the general expression we will talk about this ok. So, then we will talk about blocking in factorial design we will talk about multiple response optimization when we have multiple characteristics how do we how do we ensure optimal setting conditions like that. Then we will also briefly cover response surface methodology and what what what is the methodology all about. So, then we will talk about screening experimentation that is fractional factorial design which is an important aspects we need to reduce the number of variables for for doing this final global optimal solution to find the global optimal solution we do we do not need to see all variables and try to do experimentation it will be huge experimentation. So, we try to screen the number of variables for that fractional factorial design is one of the techniques that is used and then we also use Taguchi's method of experimentation. So, Taguchi's method which can drastically reduce the number of experiment experiments that you do actually. So, based on certain assumptions over there. So, we will discuss about Taguchi's method and all these things using mid-dab 19 interface and examples from the books. So, we will cover all of this. So, that is all about the course of 20 hours that we will deliver. Thank you and I hope you will have a great learning experience ok. Thank you.