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Reasoning of significance tests
Reasoning of significance tests

Lecture #14: Confidence Intervals for the Proportion
Lecture #14: Confidence Intervals for the Proportion

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Home Work 4 Solutions

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... – My best estimate is that those “very likely” to patronize an upscale restaurant in the future, presently spend $281 dollars per month in a restaurant. In addition, I am 95% confident that the true population value falls between $267 and $297 (95% confidence interval). Therefore, Jeff Dean can be 9 ...
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Statistics PPT,

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Final Exam Name: MAT 118, Spring 2013 Part 1: Multiple Choice

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... • Except in trivial cases, populations are too large to measure. Instead, we collect a sample from the population of interest. • Sample: A subset of data selected from the population. • n is sample size, N is population size (if known). • Inference: An estimate, prediction, or other generalization a ...
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Chapter 10 Notes: Hypothesis Tests for two Population Parameters

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Confidence Intervals, Hypothesis Testing

... Example 1 You manage a large educational nonprofit and are trying to estimate the amount of deductions your teachers apply for in order to comment to the media (you can write off $250 for supplies annually on your federal tax return). Your assistant randomly samples 50 employees . The mean write-of ...
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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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