STAT 110 A, Probability & Statistics for Engineers I
UCLA Statistics, Spring 2003
http://www.stat.ucla.edu/~dinov/courses_students.html
Instructor:
Ivo D. Dinov,
Ph.D.
Assistant Professor in Statistics,
Research Scientist, Department of Neurology,
UCLA School of Medicine
E-mail:
Teaching Assistan(s):
C. Chang
E-mail:
cchang@stat.ucla.edu
Textbook:
Probability and Statistics for Engineering and the Sciences
, Jay Devore, Duxbury, 5
^{th}
edition (2000).
Tentative schedule of topics to be covered
Introduction: What is Statistics? Population vs. Sample, Collection of data, random samples
Descriptive Statistics: Stem and leaf displays, Dotplots, Histograms, Numerical measures for center of distributions
Descriptive Statistics: Numerical measures for spread, Boxplot
Probability: Events, Axioms, Properties
Probability: Product Rules, Permutations, Combinations, Conditional probability, Independence, Bayes theorem
Discrete random variables: Probability distributions, Binomial, Negative Binomial, Hypergeometric, Poisson
Continuous random variables: Probability density function, Normal distribution
Continuous random variables: Normal approximation to the binomial, Exponential distribution
Two random variables: Joint, marginal and conditional distribution
Sampling: Simple random sampling, Sampling distribution, Central Limit Theorem (CLT)
Point estimation: Bias, Efficiency
Confidence intervals: CI for means and proportions on a single sample
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Ivo D. Dinov
, Ph.D., Departments of Statistics and Neurology, UCLA School of Medicine