EM526: Industrial Statistics

Basic Details:

Course Code: EM 526
Credits: 3
Pre-requisites: EM 213
Compulsory/Optional: Optional

Aim :

To develop skills in analyzing problems in industry by applying statistical methods.

Intended Learning Outcomes:

On successful completion of the course, the students should be able to;

Apply statistical techniques to obtain a sample and apply statistical methods to make inferences about the population.
Apply regression analysis to model data, test the model adequacy and validate the model.
Analyze data and interpret results using statistical software.

Couse Content:

Sampling Methods

Simple random sampling, stratified sampling, cluster sampling, and systematic sampling.

Questionnaire design and analysis

Introduction to questionnaire design and analysis, introduction to statistical software.

Methods of Estimation

Minimum variance unbiased estimators, confidence interval on mean, variance, proportion and difference of means.

Testing of Hypotheses

Mean, variance and proportion, comparison of two means, two variances, and two proportions (independent and dependent samples), use of p-value for the analysis.

introduction, simple linear regression, polynomial regression, multiple linear regression, regression with dummy variables, intrinsically linear regression, inferences concerning the regression coefficients, multicollinearity, residual analysis, repeated measures and lack of fit, forward selection method, backward elimination method, step wise method, model validation.

Time Allocation (Hours):

Lectures

Tutorials

Practicals

Recommended Texts:

R.S.N. Pillai and Bagavathi, “Statistics: Theory and Practice”, 1st edition (2002), S. Chand &Company LTD.
D.C. Montgomery and G.C. Runger, “Applied Statistics and Probability for Engineers”, 6th edition (2013), John Wiley and Sons, Inc.
J.S. Milton, Jesse C. Arnold, “Introduction to Probability and Statistics, Principals and Applications for Engineering and Computing Sciences”, 4th edition (2002), McGraw-Hill, Inc.

Assessment:

In - course: