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Module Two: Graphical and Numerical Methods for One Variable
Module Two: Graphical and Numerical Methods for One Variable

Lecture 6
Lecture 6

... • The sample space S of a random process is the set of all possible outcomes Example: one coin toss S = {H,T} Example: three coin tosses S = {HHH, HTH, HHT, TTT, HTT, THT, TTH, THH} Example: roll a six-sided dice S = {1, 2, 3, 4, 5, 6} Example: Pick a real number X between 1 and 20 S = all real numb ...
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... hypothesis test. This is implicitly done when doing the chi-square computations by hand and using tabled chisquare values to determine CRVAI.. Additional detsils regarding the usage of the 'round' parameter are given near the top of Listing 1. If the user wishes to suppress this option, 'round=.' ca ...
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Some Applications of Probability 1 Ramsey Numbers COS 341 Fall 2005

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... SOLUTION:  . Though it is possible to put together a rejection region, the easiest way H 1 : not Poisson6 to do this is to use the Poisson(6) table and a p-value approach. If we look up the probability that x is 12 or larger we find: pvalue  2Px  12   21  Px  11  21  .9799   2.0 ...
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ch 5 - Mathematics and Statistics

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File - Md Afnan Hossain

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History of statistics

The History of statistics can be said to start around 1749 although, over time, there have been changes to the interpretation of the word statistics. In early times, the meaning was restricted to information about states. This was later extended to include all collections of information of all types, and later still it was extended to include the analysis and interpretation of such data. In modern terms, ""statistics"" means both sets of collected information, as in national accounts and temperature records, and analytical work which requires statistical inference.Statistical activities are often associated with models expressed using probabilities, and require probability theory for them to be put on a firm theoretical basis: see History of probability.A number of statistical concepts have had an important impact on a wide range of sciences. These include the design of experiments and approaches to statistical inference such as Bayesian inference, each of which can be considered to have their own sequence in the development of the ideas underlying modern statistics.
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