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
Textbook:
Probability and Statistics for Engineering and the Sciences
, Jay Devore, Duxbury, 5
^{th}
edition (2000).
Tentative schedule of topics to be covered
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.
Confidence intervals: CI for the variance of a normal population (Ch. 01-06)
Hypothesis testing on a single sample: One-sided and two-sided tests, Type I and II errors (Ch. 07)
Hypothesis testing on a single sample: p-values (Ch. 08)
Inferences on two samples: Confidence intervals and Hypothesis testing for a difference between means
Inferences on two samples: proportions, paired data (Ch. 09)
Inferences on two samples: equal variances
The Analysis of Variance: Comparison of means of more than two populations (Ch. 10)
The Analysis of Variances: Unequal sample size (Ch. 11)
Simple Linear Regression: Scatterplot, Least squares estimates, interpretations, Confidence intervals (Ch. 12)
Simple Linear Regression: Prediction intervals, Diagnostics, Transformations
Multiple regression: Least square estimates, CI and prediction intervals, Dummy variables (Ch. 13)
Chi-Square (χ
^{2}
) Goodness of Fit Test (Ch. 14)
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Ivo D. Dinov
, Ph.D., Departments of Statistics and Neurology, UCLA School of Medicine