Scope 2011Final
2
Preparing the Final Exam, STAT 3021, Fall 2011 Date: December 22, Thursday Time: 10:30 am -12:30 pm Final Scope Handout 1 Probability • Sample Space, Events. • Probability of an Event, Conditional Probability. • Multiplicative Rules, Bayes’ Rule. Handout 2 Random Variables and Probability Distributions • Concept of a Random Variable. • Discrete and Continuous Probability Distributions. • Joint Probability Distributions. Handout 3 Mathematical Expectation • Mean, Variance, and Covariance of Random Variables. • Means and Variances of Linear Combinations of Random Variables. • Chebyshev’s Theorem. Handout 4 Some Discrete Probability Distributions • Hypergeometric Distribution • Binomial and Multinomial Distributions. • Negative Binomial and Geometric Distributions. • Poisson Distribution.. Handout 5 Some Continuous Probability Distributions • Normal Distribution and Application of Normal Distribution. • Normal Approximation to the Binomial. • Gamma, Exponential, and Chi-Squared Distributions. • Relation between the Poisson Process and Exponential Distribution. • The Memoryless Property.
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Final 11
Transcript of Scope 2011Final
Preparing the Final Exam, STAT 3021, Fall 2011
Date: December 22, Thursday Time: 10:30 am -12:30 pm
Final Scope
Handout 1 Probability
• Sample Space, Events.
• Probability of an Event, Conditional Probability.
• Multiplicative Rules, Bayes’ Rule.
Handout 2 Random Variables and Probability Distributions
• Concept of a Random Variable.
• Discrete and Continuous Probability Distributions.
• Joint Probability Distributions.
Handout 3 Mathematical Expectation
• Mean, Variance, and Covariance of Random Variables.
• Means and Variances of Linear Combinations of Random Variables.
• Chebyshev’s Theorem.
Handout 4 Some Discrete Probability Distributions
• Hypergeometric Distribution
• Binomial and Multinomial Distributions.
• Negative Binomial and Geometric Distributions.
• Poisson Distribution..
Handout 5 Some Continuous Probability Distributions
• Normal Distribution and Application of Normal Distribution.
• Normal Approximation to the Binomial.
• Gamma, Exponential, and Chi-Squared Distributions.
• Relation between the Poisson Process and Exponential Distribution.
• The Memoryless Property.
Handout 6 Moments and Moment-Generating Functions.
Handout 7 Fundamental Sampling Distributions and Data Descriptions.
• Box Plot.
• Central Limit Theorem.
• Sampling Distribution of S2.
• t-Distribution.
• F -Distribution.
Handout 8 & 9 One and Two-Sample Estimation Problems.
• Point Estimation: Unbiasedness and Efficiency.
• Estimating the Mean and Variances.
• Prediction Interval of a Future Observation.
• Confidence Intervals for µ and µ1 − µ2. Scenarios 1-4.
Final Preparation
• Make sure you totally understand the homework problems.
• For the final exam, you are allowed to use three two-page two-sided ”cheat sheets”.
• There are about 6-8 problems in the final exam. The total score is 100 points.
• You can only use standard calculators that do not have external memory drives ordata ports to be used as communication devices.
• Probability tables for distributions will be given.
• Office hours for the final exam: STSS 118, 3:30pm-4:30pm, December 14th andFord Hall 350, 2:30pm-4:30pm, December 21th.
