Statistics 330 Schedule

Fall 2013


Statistics 330 Home Page




Lecture Topics and Related Readings

Week Monday Wednesday Friday
1 (08/26) Introduction, Randomness, Sample Space
Hofmann 1.1-1.2
Baron pp. 9-13
Venn diagrams, Probability
Hofmann 1.3
Baron pp. 14-16
Kolmogorov Axioms, Counting
Hofmann 1.4
Baron 2.3
2 (09/02) Labor Day (labor hard!) Counting, Independence, Conditional Probability
Hofmann 1.4-1.7
Baron 2.3-2.4
Bayes theorem, Total Probability, Tree diagrams
Hofmann 1.7
Baron 2.4
3 (09/09) Random Variables and Discrete Distributions
Hofmann 2.1
Baron 3.1,3.2
Expectation, Variance, Covariance and Correlation
Hofmann 2.1, 2.2.5
Baron 3.3
Discrete Distributions
Hofmann 2.2
Baron 3.4
4 (09/16) Expectation & Variance, Covariance, Correlation
Hofmann 2.1.1, 2.1.2, 2.2
Baron 3.3
Specific Discrete Families:
Binomial, Geometric
Hofmann 2.2.2, 2.2.3
Baron 3.4.2, 3.4.3
Specific Discrete Families:
Poisson
Hofmann 2.2.4
Baron 3.4.5
5 (09/23) Connections between specific discrete families
Additivity of Binomial,Poisson
Continuous R.V.'s, PDF's, CDF's
Hofmann 2.3
Baron 4.1
Exam
6 (09/30) Exam discussion Joint densities, marginals, expectations, variance, covariance of continuous r.v.s
Baron 4.1
Specific Continuous Families:Uniform, Exponential
Hofmann 2.4.1, 2.4.2
Baron 4.2.1, 4.2.2
7 (10/07) The Normal distribution and Central Limit Theorem
Hofmann 2.4.4, 2.5
Baron 4.2.3, 4.2.4, 4.3
Central Limit Theorem: Normal approximations to the Binomial and Poisson
Hofmann 2.5
Baron 4.3
Introduction to Stochastic Processes and Markov Chains
Baron 6.1, 6.2
8 (10/14) Markov Chains: Transition Probability Matrices and Stationary Distributions
Baron, 6.2, 6.3
Homogeneous Poisson Process
Hofmann 4.1
Baron 6.3
Poisson Process
Hofmann 4.1
Baron 6.3
9 (10/21) Birth and Death Process
Hofmann 4.1
Birth and Death Process, balance equations
Hofmann 4.1
Exam
Baron 4, 6
10 (10/28) Exam discussion
Hofmann 4.1
Queueing Systems and Little's Law
Hofmann 5.1
Baron 7.1, 7.2
M/M/1 Queue
Hofmann 5.2, Baron 7.4
11 (11/04) M/M/1/K, M/M/c queue
Hofmann 5.3, 5.4
Baron 7.5
Introduction to Statistics
Hofmann 6.1, Baron 8.1, 8.2
Descriptive Statistics
Baron 8.2, 8.3
12 (11/11) Parameter Estimation: Method of Moments
Hofman 6.1.2, Baron 9.1.1
Parameter Estimation: Method of Maximum Likelihood
Hofmann 6.1.1, Baron 9.1.2
Introduction to R
13 (11/18) Confidence Intervals
Baron 9.2
Exam Exam discussion
(11/22) Break Break Break
14 (11/29) Confidence Intervals
Baron 9.3
Hypothesis Testing, Connection with CI's
Hofmann 6.3, Baron 9.4
Hypothesis Testing
Hofmannn, 6.3, Baron 9.4
15 (12/06) Linear Regression and ANOVA
Hofmann 6.5, Baron 10.2, 10.3
Multiple Linear Regression and Model-building
Hofmann Baron 10.4, 10.5
What did we learn?

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