The course covers numerical and graphical methods of descriptive statistics, basic probability theory, the notion of discrete and continuous random variables and their probability distributions (focussing on the binomial, geometric, hypergeometric, Poisson, uniform, exponential, normal and Erlang-k distributions), the connection between probability and statistical inference (population and sample), sampling and sampling distributions, computation of confidence interval estimates and testing of hypotheses (involving means, differences of means, proportions, difference of proportions, variances), an introduction to simple and multiple linear regression, an introduction to experimental design and analysis of variance, and an introduction to basic principles of quality control.
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By the end of the course, the student is expected to be able to:
Explain the relationship between statistical populations and random samples, listing reasons why a sampling approach is usually necessary in determining characteristics of populations.
Calculate common numerical characteristics of data sets (measures of central tendency, of variability, and of relative standing).
Relate the relative frequency concept of probability to the basic properties of a probability.
Apply the notion of conditional probability to situations in which the notion of independence of events is relevant.
Describe the notion of a both a discrete and continuous random variable and its probability and cumulative probability distributions, mean value, standard deviation.
Justify the use of the binomial, Poisson, geometric, hypergeometric, uniform, exponential, Wiebull, Erlang-k, or normal distributions probability distributions to solve probability problems involving these distributions.
Support the major implications of the Central Limit Theorem using sound statistical reasoning.
Outline the major issues of statistical estimation (point estimates, interval estimates, characteristics of estimators).
Support the standard hypothesis test procedure using logical reasoning and correct terminology.
Describe the basic features of a completely randomized single factor statistical experiment, and the principle behind the ANOVA method, and demonstrate how to carry out the F-Test and basic approaches to multiple comparisons.
Evaluate the advantage of randomized block designs over completely randomized experimental designs.
Perform the computations involved in carrying out the basic ANOVA for a randomized block design.
Interpret results of multiple linear regression calculations.
Justify statistical process control conclusions using fundamental statistical reasoning.
Effective as of Fall 2003
ELEX 7010 is offered as a part of the following programs:
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