Engineering Statistics (MT3073)

Pre-requisite(s)

None

Recommended Book(s)

  • Miller & Freund’s Probability And Statistics For Engineers By R.A. Johnson, 7th Edition     

Reference Book(s)

  • Probability And Statistics (Jay L Devore) 7th Edition
  • Probability & Statistics For Engineering & Sciences (William W. Hines, Douglas C. Montgomery)
  • Introductory Statistics (Neil A Weiss) 4th Edition
  • Introduction To Probability And Statistics (J S Milton, J. C. Atnold)
  • Probabilities, Random Variables, & Random Processes Michel O’Flynn.

Course Objectives

1. The course will provide knowledge and skills for the students to apply statistical techniques to complex engineering problems 2. This course will provide knowledge regarding Probability Theory, Random Variables, Distributions and Estimation, emphasizing the link between Statistics and Engineering. 3. Students are expected to develop a statistical way of thinking and ability for the successful usage of the statistical concept, theory and notations.

Course Learning Outcomes (CLO)

  1. CLO-1: Explain the use of descriptive techniques to describe the statistical data. (C2)
  2. CLO-2: Use the concepts and methods of probability theory for solving problems in engineering sciences. (C3)
  3. CLO-3: Infers the population parameters on the basis of sample study using the techniques of inferential statistics. (C4)

Course Contents

1 – Introduction   

  • What is statistics,
  • Elements of statistics
  • Types of statistics
  • The Role of Scientist and Engineers in Quality Improvement

2 – Descriptive Statistics  

    • Methods For Describing Sets Of Data
    • Graphical Methods For Describing Quantitative Data. Pareto Diagrams and Dot Diagrams
    • Frequency Distributions. Histograms,
    • Quantitative Data, Multivariate Data.
    • Measures of Location, The Mean, Median And Mode. Quartiles Percentiles And Trimmed Mean.
    • Measures of Variability. Range, Mean (Absolute) Deviation
    • Standard Deviations
    • Skewness and coefficient of Skewness
    • Chebyshev’s Theorem, Empirical Rule
    • Coefficient of Variation
    • Measures of Relative Standing, The pth Percentile, z-scores.

3 - Probability 

    • Random Experiment
    • Event, Sample Spaces And Probability
    • Axioms, Interpretations And Properties Of Probability
    • The Additive Rule And Mutually Exclusive Events
    • Complementary Events
    • Counting Techniques

4 – Law of Total Probability and Bayes’ Rule    

  •  Conditional Probability
  • The Multiplicative Rule And Independent Event
  • Law of Total Probability
  • Prior and Posterior Probabilities, Bayes’ Rule.

5 – Discrete Random Variables  

    • Random Variables, Discrete and Continuous
  • Probability Distribution For Discrete r.v's
  • Mathematical Expectations, or Expected Value Of Discrete r.v’s
  • Binomial Experiment
  • The Binomial Probability Distribution
  • The Poisson Probability Distribution

6 – Continuous Random Variable                                                                    

    • Continuous Probability Distributions
    • The Uniform Distribution
    • The Normal Distributive
    • Approximating A Binomial Distribution With A Normal Distribution
    • Other Continuous Distributions

7 – Sampling Distributions  

    • Introduction To Sampling Distributions. Properties
    • The Sampling Distribution of the Sample Mean (σ known)
    • The Sampling Distribution of the Sample Mean (σ unknown)
    • The Sampling Distribution of the Variance
    • t-Distribution and Chi-Square Distribution

8 – Regression Analysis 

 

    • Probabilistic Modals and Curve Fitting
    • Fitting the Model (Method of Least Squares)
    • Estimating Model Parameters. Correlation (Measure Of Usefulness Of Model)
    • The Coefficient of Determination
    • Multiple Regression

9 – The Statistical Content of Quality-Improvement Programs               

    • Control Charts for Measurements
    • Control Charts for Attributes
    • Design of Experiments

Mapping of CLOs to Program Learning Outcomes

CLOs/PLOs

CLO:1

CLO:2

CLO:3

PLO:1 (Engineering Knowledge)

 

 

 

PLO:2 (Problem Analysis)

 

 

PLO:3 (Design Development of Solutions)

 

 

 

PLO:4 (Investigation)

 

 

 

PLO:5 (Modern Tool Usage)

 

 

 

PLO:6 (Engineer & Society)

 

 

 

PLO:7 (Environment and Sustainability)

 

 

 

PLO:8 (Ethics)

 

 

 

PLO:9 (Individual & Team Work)

 

 

 

PLO:10 (Communication)

 

 

 

PLO:11 (Project Management)

 

 

 

PLO:12 (Life Long Learning)