Probability & Statistics (MA 250)
Spring 2018

Dr. Nazar Khan

Lectures:

	Morning Session (Room 10)
Monday	08:15 am - 09:40 am
Wednesday	08:15 am - 09:40 am

Office Hours:

Monday	01:30 pm - 02:30 pm
Wednesday	01:30 pm - 02:30 pm

Programming Environment: Matlab

Grading Scheme/Criteria:

Quizes	20%
Assignments	5%
Mid-Term	35%
Final	40%

Text:
Statistics, Freedman, Pisani, and Purves, 4th edition, W.W. Norton and Co., New York, 2007.

Interesting Links:

Why Do People Find Probability Unintuitive and Difficult?

Grades:
Grading sheet (Accessible only through your PUCIT email account)

Content

1. Introduction
   • In order to understand the "real" world
   • Statistics are important
   • Probability is the tool
2. Statistics
   • Design of Experiments
   • Descriptive Statistics
   • Histogram
   • Mean and Standard Deviation
   • The Normal Curve
   • Correlation
   • Regression
3. Probability
   • Introduction to Probability
     • Predictions in the presence of uncertainty.
     • Sample Space (S), Events (E), Probabilities of Events (P).
     • Set Theory
   • Basics of Probability
     • Random Experiment, Outcome, Probability Space (S,E,P).
     • Axioms of Probability
     • Methods of Computing Probabilities
     • Counting Elements (when S is a simple sample space).
     • Measuring Sizes (when S is not a simple sample space).
     • Crucial Probability Tricks
     • Multiplication Principle.
     • Permutations (when order matters).
     • Combinations (when order does not matter).
   • Independence
   • Random Variables
   • Discrete
   • Continuous
   • Expectations
   • Multivariate Distributions
   • Conditional Probability
   • Law of Large Numbers and the Central Limit Theorem
4. Box Models
5. Tests of Significance
6. Applications
   • Model Fitting
   • Pattern Classification