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

    • Lectures: (Botany 325), MWF, 8:00 - 8:50 AM

    Discussions Section Information
    Section ID Section Classroom Time TA Name
    263362221 1a (Disc.) Boelter 5419 R 9:00-9:50 AM C. Chang

    Instructor Office: Main: MS 8142E (alternative: CHS, UCLA School of Medicine, Reed 4-238, by appt. only)
    TA Offices: MS 3903
    Virtual Office Hours (STAT 110A Forum)
     STAT Computer Lab: http://www.stat.ucla.edu/undergraduate/icl/
    Grading policy and basis for Final Grade:
    HW Assignment Policy:

    Textbook:  Probability and Statistics for Engineering and the Sciences, Jay Devore, Duxbury, 5th edition (2000).
      Tentative schedule of topics to be covered
    1. Introduction: What is Statistics?, Population vs Sample, Collection of data, random samples
    2. Descriptive Statistics: Stem and leaf displays, Dotplots, Histograms, Numerical measures for center of distributions
    3. Descriptive Statistics: Numerical measures for spread, Boxplot
    4. Probability: Events, Axioms, Properties
    5. Probability: Product Rules, Permutations, Combinations, Conditional probability, Independence, Bayes theorem
    6. Discrete random variables: Probability distributions, Binomial, Negative Binomial, Hypergeometric, Poisson
    7. Continuous random variables: Probability density function, Normal distribution
    8. Continuous random variables: Normal approximation to the binomial, Exponential distribution
    9. Two random variables: Joint, marginal and conditional distribution
    10. Sampling: Simple random sampling, Sampling distribution, Central limit theorem
    11. Point estimation: Bias, Efficiency
    12. 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