Lectures schedule (L02)

Tutorials schedule
(T05/T06/T07)

Tuesday/Thursday
15:3016:45
(EDC 386)

T05/07: Wednesday 13:0013:50 (MS521) T06: Wednesday 15:0015:50 (MS571) 
Class
work:
Inclass lectures with typical examples (lecture notes will be posted
on the webpage in the form of pdffiles);
your computer must have an Adobe Acrobat reader (for free downloading
see www.adobe.com).
Midterm
and Quizzes:
There will be 1 Midterm ( Oct 23, 2007) and 4 Quizzes.
Final
Exam:
It will cover all the materials covered in this course.
Grading scheme (Course Evaluation):
Exam, Midterm and Quizzes

Value (% of your final mark)

Dates

Midterm

15%

October 23, Tuesday, 15:3016:45 (EDC 386) 
Quizzes (4)

35%

Sept 26,
Oct 10, Nov 21, Dec 5

Final Exam  50%  Thu, December 13, 3:305:30pm, KN AUX 
Month 
Day 
Tuesday 
Day 
Thursday 
Sep 
11 
Lec1:
Introduction to Statistics: Probability and Inference. A Review of Set
Notation. Introduction
to
Probability. (Sections
1.1,
1.4, 2.12.3). 
13  Lec2:
Foundations of Probability. The Discrete
Case Probability Model. Probability of Events. Counting
Sample Points (Sections
2.42.6). 
Sep 
18 
Lec3:Conditional
Probability and Independence. Two Laws of Probability. Event
Composition Method (Sections 2.72.9). Answers to
some evennumbered exercises. 
20 
Lec4:
Law of Total Probability and Bayes' Rule. Numerical Events and Random
Variables. Random Sampling (Sections 2.102.12) 
Sep 
25 
Lec5:
Definitions of Discrete Random Variables. Distributions, Expected Value
and Variance of Discrete
Random Variables (Sections 3.13.2) 
27 
Lec
6: Properties of the Expected Value and Variance of a
Random Variable. (Section 3.3) 
Oct 
2 
Lec7: Binomial Distribution (Sections 3.4)  4 
Lec8: Geometric Distribution, Negative
Binomial Distribution. Hypergeometric
Distribution. (Sections 3.5.3.7) 
Oct 
9 
Lec9:
Poisson
Distribution (Section
3.8) 
11 
Lec10:
MomentGenerating Function, Tchebysheff's Theorem (Sections
3.9,
3.11) 
Oct 
16 
Lec11:
Distribution of Continuous Random Variables (CRV) (Sections
4.14.2) 
18 
Lec12:
Expected Value for CRV.Uniform
Distribution (Sections 4.34.4) 
Oct 
23 
Midterm  25 
Lec13: Normal Distribution (Section 4.5). 
OctNov 
30 
Lec14: Gamma Distribution (Section 4.6)  1 
Lec15: The Beta Distribution and Other Expected Values (Section 4.84.9) 
Nov 
6 
Lec16:
Tchebysheff's Theorem (Section 4.10) 
8 
Lec17: Definition of Sampling: Sampling Related to Normal Distribution (Section 7.17.2) 
Nov 
13 
Reading Day (No Lecture) 
15 
Lec18: The Central Limit
Theorem. The Normal
Approximation to Binomial Distribution. (Sections 7.3,
7.5) 
Nov 
20 
Lec19: Estimation: Confidence
Intervals. (Sections 8.1,8.2,8.5) 
22 
Lec20: LargeSample
Confidence Intervals and Selecting
the Sample Size (Sections 8.6, 8.7) 
Nov 
27 
Lec21: Hypothesis
Testing: Definition. Statistical Tests (Sections 10.110.2) 
39 
Lec22: Common LargeSample
Test. (Section 10.3) 
Dec 
4 
Lec23: Type II Error
Probabilities and Sample Size for the Z Test. pvalue.
(Sections
10.4, 10.6) 
6 
Lec24:
SmallSample
Hypothesis Testing (Section 10.8). Course Review. 
Statistical Applets 