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Statistical Inference with SCILAB
Statistical Inference with SCILAB

this PDF file
this PDF file

Practice Problems from Levine, Stephan, Prentice-Hall, 2011
Practice Problems from Levine, Stephan, Prentice-Hall, 2011

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... Since both Odds Ratios for the two risk factors are greater than one, there is an increased risk in being a case when having an alcohol concentration greater than 0.02 or greater than 0.08. At the 0.08 level the risk is even greater when comparing the OR of 13.2 to 4.9. Moreover, both odds ratios h ...
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... The results of the test form a normal distribution with a mean of 78 and a standard deviation of 5. The students want to know their individual scores, but Mrs. Tucker won’t tell them. She tells them that their z-scores for the test are 1.3 and 2.1, respectively. What is the positive difference betwe ...
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Doing Statistics with WinStat

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Understanding Basic Statistics

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Poll: Iraq speeches, election don`t help Bush

... proportions. Students will then simulate samples for the creation of additional samples in an effort to understand what it means to say that one is 95% confident. The last part of this series addresses confidence intervals for sample means, including the use of data collection and simulation. The cu ...
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Group-Sequential Tests for Two Means

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Year-to-year correlation, record length, and overconfidence in wind

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A SAS Macro to Compute Effect Size (Cohen`s )

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Basic Business Statistics, 8th edition

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Day 12 Powerpoint Slides - University of Texas School of Information

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Small Sample Statistics for

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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