Course Details

Subject {L-T-P / C} : CH4374 : Chemical Engineering Data Analysis Laboratory {0-0-2 / 1}
Subject Nature : Practical
Coordinator : Prof. Madhusree Kundu


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

  1. To make the students learn the attributes of data and its effective presentation.
  2. Data preprocessing including data outlier detection, data filtering, and data smothering.
  3. Non-stationary behavior of data and its conversion to stationary data.
  4. Statistical interpretation of data.

Course Outcomes

1. Enable the students to analyze and present data effectively.
2. Enable the students to pre-process data using SPSS and MATLAB and make the Statistical interpretation of data.
3. Enable the students to identify the non-stationary behavior of data using MATLAB.

Essential Reading

  1. Robert M. Bethea, Statistical Methods for Engineers and Scientists, CRC Press , 3rd Edition, 2019
  2. 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

  1. SPSS tutorial, SPSS tutorial, IBM , 2017
  2. , ,

Journal and Conferences

  1. Journal of Chemometrics, Elsevier Publication
  2. Chemometrics and Intelligent Laboratory Systems, Elsevier Publication