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Basic Details:

  • Course Code: EM 3010
  • Credits: 3
  • Pre-requisites: EM 2020
  • Compulsory/Optional: Compulsory

Aim :

To provide in-depth knowledge of the concepts and tools required to analyze data toestimate underlying statistical parameters and make inferences about data.

Intended Learning Outcomes:

At the end of this course, students should be able to:

  • Design statistical surveys and experiments to collect quantitative and qualitative data.
  • Determine statistical parameters using appropriate techniques to describe and summarize data.
  • Analyze a given dataset using appropriate statistical techniques, including parametric, non-parametric and Bayesian methods, to make inferences.
  • Apply computer aided statistical tools for problem solving and inference.

Couse Content:

Types and sources of data, Survey design for qualitative and quantitative data, population estimation, statistical sampling methods

Types of data and representation, Measures of central tendency, skewness, kurtosis and their implications, rank statistics

Bayesian hypothesis testing, Minimax hypothesis testing, Neyman-Pearson hypothesis testing, Composite hypothesis testing, non-parametric hypothesis testing

Cramer-Rao bound, Minimum mean squared error estimation, Minimum variance unbiased estimation, Maximum Likelihood estimation. information entropy

Minimum mean squared error (MMSE) estimation, Maximum a posteriori estimation, Linear MMSE estimation.

Outlier detection, handling missing values, standardization, feature extraction

Analyzing a given dataset using a statistical package/language

Time Allocation (Hours):

Lectures
0
Lectures
0
Assignments
0
Lectures
0

Recommended Texts:

  • “Design of Experiments”: A Modern Approach, Bradley Jones, Douglas C. Montgomery, Wiley.
  • “Probability and Statistics for Engineers & Scientists”, Roland Walpole, Raymond Myers, Sharon Myers, 9th Edition or later, Pearson.
  • “Applied Statistics and Probability for Engineers”, Douglas C. Montgomery, George C. Runger, Wiley.

Assessment:

In - course:

Tutorials
10%
Assignments (Project)
20%
Practical Work
10%

End-semester:

60%