INTRODUCTION TO PROBABILITY AND STATISTICS

Course Description

The objective of this course is to introduce basic probability concepts and basic statistics. The focus of this course is on both applications and theory. Topics include: introduction to random variables, simple data analysis and descriptive statistics, frequency distribution, cumulative distribution, sample space, events, counting sample points (basic combinatorics), probability of an event, probability axioms, laws of probability, conditional probability, Bayes’ rule, discrete and continuous random variables, probability distributions, cumulative probability distributions, discrete and continuous probability distributions, discrete uniform, Binomial, Geometric, Hypergeometric, Poisson, Continuous uniform, Normal Disributions, Gamma and Exponential distribution, jointly distributed random variables, expectation and covariance of discrete and continuous random variables, random sampling, sampling distributions, distribution of Sample Mean, Central Limit Theorem(CLT).

INTRODUCTION TO PROBABILITY AND STATISTICS

Evaluation Tools (Active Term)

Course outcomes

1. 01 Apply(4) statistical methods in the engineering problem-solving approach
2. 02 Compute(3) and interpret(3) descriptive statistics using numerical and graphical techniques
3. 03 Identify(2) and apply(4) the basic concepts of probability
4. 04 Apply(4) probability theory to set up tree diagrams
5. 05 Apply(4) probability theory via Bayes’ Rule.
6. 06 Describe(3) the properties of random variables, discrete and continuous distribution functions.
7. 07 Identify(2) the basic concepts of joint probability distribution.

Course Syllabus

Reference Books & Course Materials

1. 01 Jay L. Devore, Probability and Statistics for Engineering and Sciences, 8th ed., Brooks/Cole Cengage Learning
2. 02 R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, Probability and Statistics for Engineers and Scientists, 7th ed., Prentice Hall, 2002.

Learning Outcomes

1. L01 Apply(4) statistical methods in the engineering problem-solving approach
2. L02 Compute(3) and interpret(3) descriptive statistics using numerical and graphical techniques
3. L03 Identify(2) and apply(4) the basic concepts of probability
4. L04 Apply(4) probability theory to set up tree diagrams
5. L05 Apply(4) probability theory via Bayes’ Rule.
6. L06 Describe(3) the properties of random variables, discrete and continuous distribution functions.
7. L07 Identify(2) the basic concepts of joint probability distribution.

Program Outcomes

1. P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix