STAT 360 Probability and Statistics Fall 2012
1) General information:
Crosslisted course offered as STAT 360, MATH 360 Semester: Fall 2012, Aug 20--Dec 07
Course name: Probability and Statistics Number of credits: 3
Course Prerequisite: MATH 172 or MATH 182
Section 1. Schedule Line Number (SLN): 29161, Lectures: TU,TH 10.35-11.50, Bldg & Room VUB 100 Section 2. SLN: 29163, Lectures: TU,TH 12.00-1.15, Bldg & Room VUB 100
2) Instructors Instructor: Nikolay Strigul
E-mail: [email protected] Phone: 360-546-9788
Office Hours: M, W 10.00-12.00 and by appointment Teaching Assistant: Ward, Jennifer Schmidt
E-mail: [email protected]
Office Hours: Monday 12.00 – 2.00 and Tuesday 12.00-2.00.
3) Required textbook
Probability & Statistics for Engineers & Scientists by R.E. Walpole, R.H. Myers, S.L. Myers, K. Ye 4) Course description
STAT360 is a standard undergraduate course in probability and statistics for undergraduate students in sciences and engineering. This course covers basic concepts and methods in probability and statistics such as sample space, discrete and continuous random variables, probability distributions, introduction to the statistical inference, estimation, and statistics. Students will have weekly homework. There will be 2 quizzes and midterm and final exams.
5) Workload and grading
Homework assignments: There will be weekly homework assignments.
Exams: There will be midterm and final exams.
Quizzes: There will be 2 quizzes.
Grading:
Homework assignments: 20 % Quizzes: 20 %
Midterm: 25 % Final: 35 %
6) Learning Outcomes
Graduates will have a basic understanding of probability reasoning required for a broad range of industrial and scientific applications; students will also have an elementary understanding of statistics.
At the end of this course, students should be able to:
Course topics (and dates) that address these learning
outcomes are:
This outcome will be evaluated primarily by:
LO1
Understand and apply for problem solving the
following concepts: sample space, probability,
conditional probability, dependent and independent events, and Bayes rule.
(WSU Goal: Quantitative Reasoning)
2 Sample space (8/23/2012) 3 Counting sample points (8/28/2012, 8/30/2012) 4 Probability (9/4/2012, 9/6/2012)
5 Conditional probability and independence (9/11/2012, 9/13/2012)
1) Homework assignments 2) Quiz 1
3) Midterm exam 4) Final exam
LO2 Understand and employ the concept of random
variables; use distribution and cumulative distribution of random variables, and calculate moments of functions of random variables.
(WSU Goal: Quantitative Reasoning)
6 Random variables (9/20/2012)
7 Joint probability
distributions, independence (9/25/2012, 9/27/2012) 8 Mathematical expectation (10/2/2012, 10/4/2012) 10 Means of linear combinations of random variables (10/11/2012) 11 Variances of functions of random variables (10/16/2012)
1) Homework assignments 2) Midterm exam
3) Final exam
LO3 Use the concept of bivariate distributions to calculate basic two-variable statistics (covariance, correlation).
(WSU Goal: Quantitative Reasoning)
9 Covariance (10/9/2012) 1) Homework assignments 2) Midterm exam
3) Final exam
LO4 Demonstrate in depth knowledge of the basic discrete (Binomial, Geometric, Negative Binomial, Hypergeometric, and Poisson) and continuous (Uniform, Normal, and Student t) distributions and apply these distributions to the standard problems from engineering and scientific
13 Discrete probability distributions 1 (10/30/2012, 11/1/2012)
14 Discrete probability distributions 2 (11/6/2012, 11/8/2012)
15 Continuous probability distributions (11/15/2012) 16 Fundamental sampling distributions
(11/20/2012, 11/22/2012)
1) Homework assignments 2) Quiz 2
3) Final exam
practice.
(WSU Goal: Quantitative Reasoning)
LO5 Use graphs, histograms and computer programs to visualize distributions of discrete and continuous random variables.
(WSU Goal: Quantitative Reasoning)
6 Random variables (9/20/2012)
7 Joint probability
distributions, independence (9/25/2012, 9/27/2012) 8 Mathematical expectation (10/2/2012, 10/4/2012) 13 Discrete probability distributions 1 (10/30/2012, 11/1/2012)
14 Discrete probability distributions 2 (11/6/2012, 11/8/2012)
15 Continuous probability distributions (11/15/2012) 16 Fundamental sampling distributions
(11/20/2012, 11/22/2012)
1) Homework assignments
LO6 Perform simple hypothesis tests, calculate point estimates and confidence intervals for a single population sample. (WSU Goal: Quantitative Reasoning)
17 Single sample (11/27/2012) 18 Statistical hypotheses (11/29/2012, 12/4/2012)
1) Homework assignments 2) Final exam
7) Course outline
Date Topic
21 T 1 Introduction to statistics and data analysis
August 23 Th 2 Sample space
28 T 3 Counting sample points
30 Th 3
4 T 4 Probability
6 Th 4
11 T 5 Conditional probability and independence
September 13 Th 5
18 T Quiz 1
20 Th 6 Random variables
25 T 7 Joint probability distributions, independence
27 Th 7
2 T 8 Mathematical expectation
4 Th 8
9 T 9 Covariance
11 Th 10
Means of linear combinations of random variables
October 16 T 11 Variances of functions of random variables
18 Th 12 Chebyshev's Theorem
23 T Review
25 Th Midterm Exam
30 T 13 Discrete probability distributions 1
1 Th 13
6 T 14 Discrete probability distributions 2
8 Th 14
13 T Quiz 2
November 15 Th 15 Continuous probability distributions 20 T 16 Fundamental sampling distributions
22 Th 16
27 T 17 Single sample
29 Th 18 Statistical hypotheses
December 4 T 18
6 Th Review
Final exam
8) Course topics
Topic 1. - Introduction to statistics and data analysis Sampling procedures
Measures of location: the sample mean Measures of variability
Discrete and continuous data
Graphical methods and data descriptions Topic 2. - Sample space
Sample space Events
Complement of an event
Intersection and union of events Disjoint events
Venn diagrams
Topic 3. - Counting sample points The multiplication rule Permutations
Number of permutations of n distinct objects taken r at a time Number of permutations of n things of k kinds
Number of ways of partitioning a set of n objects
Number of combinations of n distinct objects taken r at a time Topic 4. - Probability
Probability of an event
Probability and relative frequency Additive rule for two events Partition of sample space
Generalisation of additive rule for three events Topic 5. - Conditional probability and independence
Conditional probability Independent events Multiplicative rules Bayes' formula Topic 6. - Random variables
Concept of random variable Discrete probability distributions Continuous probability distributions
Topic 7. - Joint probability distributions, statistical independence Joint probability distributions
Marginal distributions Conditional distributions Statistical independence
Topic 8. - Mathematical expectation Mean of random variable Variance
Standard deviation Topic 9. - Covariance
Covariance
Correlation coefficient
Topic 10. - Means of linear combinations of random variables Expected value of a linear function of a random variable
Expected value of the sum or difference of functions of a random variable Expected value of multiplication of two independent random variables Topic 11. - Variances of functions of random variables
Variance of a linear function of a random variable
Variance of a linear combination of two random variables
Expected value and variance of non-linear functions of a random variable Linearization
Topic 12. - Chebyshev's Theorem Chebyshev's Theorem
Topic 13. - Discrete probability distributions 1 Discrete uniform distribution
The Bernoulli process Binomial distribution
Topic 14. - Discrete probability distributions 2 Hypergeometric distribution
Negative binomial distribution Poisson process
Topic 15. - Continuous probability distributions 1 Continuous uniform distribution
Normal distribution
Areas under the normal curve
Applications of the normal distribution Topic 16. - Fundamental sampling distributions
Random sampling
Some important statistics Sampling distribution of means
Sampling distribution of sample variances t-Distribution
Topic 17. - Single sample
Estimating the mean Standard error Prediction intervals Topic 18. - Statistical hypotheses
Testing a statistical hypothesis P-values
One- and two-tailed tests
