STAT 110 B, Probability & Statistics for Engineers II

 
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):
  • Brian Ng  E-mail: bybn@stat.ucla.edu

    • Lectures: (Botany 325), MWF, 9:00 - 9:50 AM

    Discussions Section Information
    Section ID Section Classroom Time TA Name
    263362221 2a (Disc.) Boelter 5127 T 9:00-9:50 AM Brian Ng

    Instructor Office: Main: MS 8142E (alternative: CHS, UCLA School of Medicine, Reed 4-238, by appt. only)
    TA Offices: MS 3355D
    Virtual Office Hours (STAT 110B 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. Review:  Statistics Summaries, Population vs Sample, Plots, Probability, Independence, Bayes Theorem, Discrete & Continuous random variables: Binomial, Negative Binomial, Hypergeometric, Poisson, Normal , Exponential distribution, Joint vs. Marginal distribution, Central Limit Theorem, Bias & Efficiency, Confidence Intervals.
    2. Confidence intervals: CI for the variance of a normal population (Ch. 01-06)
    3. Hypothesis testing on a single sample: One-sided and two-sided tests, Type I and II errors (Ch. 07)

    4. Hypothesis testing on a single sample: p-values (Ch. 08)
    5. Inferences on two samples: Confidence intervals and Hypothesis testing for a difference between means

    6. Inferences on two samples: proportions, paired data (Ch. 09)
    7. Inferences on two samples: equal variances
    8. The Analysis of Variance: Comparison of means of more than two populations (Ch. 10)

    9. The Analysis of Variances: Unequal sample size (Ch. 11)
    10. Simple Linear Regression: Scatterplot, Least squares estimates, interpretations, Confidence intervals (Ch. 12)
    11. Simple Linear Regression: Prediction intervals, Diagnostics, Transformations
    12. Multiple regression: Least square estimates, CI and prediction intervals, Dummy variables (Ch. 13)
    13. Chi-Square (χ2) Goodness of Fit Test (Ch. 14)
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    Ivo D. Dinov, Ph.D., Departments of Statistics and Neurology, UCLA School of Medicine