Course Details
Subject {L-T-P / C} : CH4374 : Chemical Engineering Data Analysis Laboratory { 0-0-2 / 1}
Subject Nature : Practical
Coordinator : Dr. Krunal M Gangawane
Syllabus
Various types of data, data presentation including bar chart pie-chart Histogram stem chart etc. Data pre-processing including outlier detection techniques, filtering and smoothening. Determination of data statistics including mean mode variance co-variance confidence interval various distributions (Gaussian and others) statistical inference estimation, correlation and regression analysis Hypothesis testing chi-square distribution precision and accuracy, hence the acceptance of data generated. All the activities are to be done using MATLAB and SPSS ( Statistical Package for the Social Sciences) software packages.
Course Objectives
- To make the students learn the attributes of data and its effective presentation.
- Data preprocessing including data outlier detection, data filtering, and data smothering.
- Non-stationary behavior of data and its conversion to stationary data.
- Statistical interpretation of data.
Course Outcomes
1. Enable the students to analyze and present data effectively. <br />2. Enable the students to pre-process data using SPSS and MATLAB and make the Statistical interpretation of data. <br />3. Enable the students to identify the non-stationary behavior of data using MATLAB.
Essential Reading
- Robert M. Bethea, Statistical Methods for Engineers and Scientists, CRC Press , 3rd Edition, 2019
- M.Kundu, P. Kundu, S. K. Damarla, A Chemometric Approach to Monitoring: Product Quality Assessment, Process Fault Detection and Miscellaneous Applications, CRC Press, Taylor & Francis Group , 2017
Supplementary Reading
- SPSS tutorial, SPSS tutorial, IBM , 2017
- , ,
Journal and Conferences
- Journal of Chemometrics, Elsevier Publication
- Chemometrics and Intelligent Laboratory Systems, Elsevier Publication