- Dr. Yao: MW 3:00-4:00 PM K203 or by appointment
- Dr. Iakovlev: by appointment
Aim of the Course
-
The course is intended to provide: (a) an introduction to the language, logic,
and math of probability and statistics as used in engineering and the
sciences; and (b) an opportunity to learn how probabilistic analyses and
statistical reasoning and testing can be applied to a wide range of problems
of importance in the sciences, industry, and society.
Textbook
- Applied Probability and Statistics, course notes, 250 pgs, by G.A. Fenton, D.J. Dupuis, and S. Iakovlev, available in the book store.
Additional References
- Probability and Statistics for Engineering and the Sciences, Jay L. Devore.
- Probability and Statistics for Engineers and Scientists, R.E. Walpole, R.H. Myers, and S.L. Myers, Prentice-Hall.
Course Requirements
|
1. |
There will be 10 assignments, due at the start of class on the date noted, unless otherwise arranged with the class. No late assignments are allowed. |
|
2. |
One midterm exam. As per the Faculty rules, in case of a health problem, only an official form duly filled in with "too ill to write" and signed by a physician will be accepted as an excuse to miss a test. |
| 3. | A three-hour final exam. |
-
There will be no Supplementary exam in this course.
Grading formula:
| Assignments: | 10% | ||
| Midterm: | 30% | ||
| Final Exam: | 60% |
Graded Assignments and Solutions
-
Graded assignments will typically be returned one week after its due date; any
unclaimed assignments will be left for pickup outside the Engineering Math Office, K-Building.
Solutions to
assignments and other handouts will be posted
on the web.
COURSE SYLLABUS
Week 1:- Introduction, Background, Descriptive Statistics (Chap 1)
- Sample Spaces, Events, Basic Probability Concepts (Chap 2)
- Counting Principles: Multiplication Rule, Permutations, Combinations (Chap 2)
- Mathematics of Probability (Chap 2)
- Conditional Probability, Independence, Total Probability and Probability Trees (Chap 2)
- Bayes' Theorem (Chap 2)
- Random Variables, Probability Distributions, Main Characteristics (Chap 3)
- Expectations, Variance, Covariance, Correlation Coefficient (Chap 3)
- Linear Combinations (Chap 3) Bernoulli Trials (Chap 4),
- Binomial, Geometric, and Negative Binomial Distributions (Chap 4)
- Poisson Distribution, Exponential, Gamma Distributions (Chap 4)
- Uniform, Weibull (Chap 4)
- Standard Normal, Central Limit Theorem, Normal Approximation to Binomial (Chap 4)
- Lognormal Distribution (Chap 4)
- Chi-Square and Student-t Distributions (Chap 4)
- Populations, Random Samples, Statistics, and Introduction to Sampling Distributions (Chap 5)
- Sampling distributions of the mean(s) and variance (Chap 5)
- Parameter Estimation, Point Estimates, Confidence Intervals for Single Samples (Chap 6)
- Two-Sample CI's, Paired Data, Proportions, CI on the Variance (Chap 6)
- Introduction to Hypothesis testing, Tests on One Mean (Chap 7)
- P-values, Type I and II Errors (Chap 7)
- Tests on Two Means, Paired Data, Proportion(s), Variance(s), Sample Sizes (Chap 7)
- Introduction to Simple Linear Regression, Least Squares Approach (Chap 8)
- Testing Hypotheses on the Least Squares Estimators (Chap 8)