Four-acceleration - Revision history

66.188.89.180: spelling error corrected

2014-06-25T03:38:33Z

spelling error corrected

← Older revision		Revision as of 05:38, 25 June 2014
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	~~In [[statistics]], '''Yates' correction for continuity''' (or '''Yates' chi-squared test''') is used in certain situations when testing for [[independence (probability theory)\|independence]]~~ in ~~a [[contingency table]]~~. ~~In some cases~~, ~~Yates~~' ~~correction may adjust too far~~, ~~and so its current use~~ is ~~limited~~.		"Era la notte prima di Natale e tutta la casa era in silenzio. Oh, I forgot, my articles are supposed to be inside English. My bad! Today I desired to tell y'all regarding my Christmas adventure inside Italy, however I usually be certain to keep this short article inside my native code, which naturally is English - Southern style.<br><br>Now [http://safedietplansforwomen.com/bmr-calculator bmr calculator], we get into the mechanics of our bodies. Forget all the fluff you've heard on TV; fat control is regarding Calories In vs. Calories Out. Where those calories come from, be it carbs, protein, fat, or alcohol, or the time of day you eat those calories, doesn't matter. What matters is the number of calories you eat or drink ("Calories In") plus the number of calories you burn ("Calories Out").<br><br>The most well known means to calculate your BMR is the Harris Benedict equation. This formula accounts for the factors of height, fat, age, plus sex to calculate the basal metabolic rate (BMR). This calculation is a bit more accurate than determining calorie requires based only on total bodyweight. But, the Harris Benedict equation refuses to take lean body mass into consideration. Calculations is exact inside all however, the very muscular plus the very overweight.<br><br>Carbs, when converted to glucose, are utilized mainly for energy. Foods that are classified basically as carbs include: grains plus their flours, potatoes, sugars (all forms), fruits, vegetables, plus anything made of them.<br><br>So my friend's bmr is 1969 calories a day. With this info plus her doctor's recommendation, we've decide to go for 1400-1500 calories consumed a day plus 50-60 minutes of truly little exercise a day. According to the objective calculator a reduction of 400-500 calories a day from the BMR may cause about 1 pound of fat lose per week, that is ideal.<br><br>To calculate a activity level, see this free online calculator: http://exercise.about.com/cs/fitnesstools/l/blcalorieburn.htm which can utilize a current fat, the kind of exercise, and the amount of time you are able to afford to spend doing that exercise to tell you how many calories you're going to burn. Take note of the results - should you use this calculator realistically, we can plug it right into the BMR results.<br><br>McArdle, William, Katch, Frank, and Katch, Victor. (1998). Exercise Physiology: Energy, Nutrition, plus Human Performance. Baltimore, Maryland: Williams and Wilkins.

	~~==Correction for approximation error==~~
	~~Using the~~ [~~[chi~~-~~squared distribution~~]~~] to interpret [[Pearson's chi-squared test\|Pearson's chi-squared statistic]] requires one to assume that~~ the ~~[[Discrete probability distribution\|discrete]] probability~~ of ~~observed [[binomial distribution\|binomial frequencies]] in~~ the ~~table can~~ be ~~approximated by the continuous [[chi-squared distribution]]. This assumption is not quite correct~~, ~~and introduces some error.~~

	~~To reduce the error in approximation~~, ~~[[Frank Yates]]~~, ~~an [[England\|English]] [[statistician]]~~, ~~suggested a correction for continuity that adjusts~~ the ~~formula for [[Pearson~~'~~s chi-squared test]] by subtracting 0~~.~~5 from~~ the ~~difference between each observed value and its expected value in a 2 × 2 contingency table.<ref name=Yates>[[Frank Yates\|Yates, F]]~~ (~~1934).~~ "~~Contingency table involving small numbers and the χ<sup>2</sup> test~~"~~. ''Supplement to~~ the ~~[[Journal~~ of ~~the Royal Statistical Society]]'' '''1'''~~(2)~~: 217–235~~. ~~{{jstor\|2983604}}~~<~~/ref~~> ~~This reduces the chi-squared value obtained and thus increases its [[p-value]].~~

	The ~~effect of Yates' correction~~ is ~~to prevent overestimation of statistical significance for small data~~. This formula ~~is chiefly used when at least one cell~~ of ~~the table has an expected count smaller than 5. Unfortunately~~, ~~Yates' correction may tend~~ to ~~overcorrect. This can result in an overly conservative result that fails to reject~~ the ~~[[null hypothesis]] when it should~~ (~~a [[type II error]]~~). ~~So it~~ is ~~suggested that Yates' correction~~ is ~~unnecessary even with quite low sample sizes~~,<~~ref name=Sokal1981~~>~~Sokal RR~~, ~~Rohlf F.J~~. (~~1981~~). '~~'Biometry: The Principles and Practice of Statistics in Biological Research~~.'' ~~Oxford: W.H. Freeman, ISBN 0~~-~~7167~~-~~1254~~-7.<~~/ref~~> ~~such as:~~

	:<~~math~~> ~~\sum_{i=1}^N O_i = 20 \~~, ~~</math>~~

	~~The following is Yates' corrected version of [[Pearson's chi-squared test\|Pearson's chi-squared statistic]]~~:

	:~~<math> \chi_\text{Yates}^2 = \sum_{i=1}^{N} {(\|O_i - E_i\| - 0~~.~~5)^2 \over E_i}<~~/~~math>~~

	~~where:~~

	~~:''O<sub>i<~~/~~sub>'' = an observed frequency~~
	~~:''E<sub>i<~~/~~sub>'' = an expected (theoretical) frequency~~, ~~asserted by~~ the ~~null hypothesis~~
	~~:''N'' = number~~ of ~~distinct events~~

	~~== 2 × 2 table ==~~

	~~As a short-cut~~, ~~for a 2 × 2 table with~~ the ~~following entries:~~

	~~{\| class="wikitable"~~
	~~!   !! S !! F !!  ~~
	\|-
	~~! A~~
	\| '~~'a'' \|\| ''b'' \|\| ''N''<sub>A</sub>~~
	\|-
	~~! B~~
	~~\| ''c'' \|\| ''d'' \|\| ''N''<sub>B</sub>~~
	\|-
	~~!  ~~
	~~\| ''N''<sub>S</sub> \|\| ''N''<sub>F</sub> \|\| ''N''~~
	\|}

	we can ~~write~~

	: <~~math~~>~~\chi_\text{Yates}^2 = \frac{N(\|ad - bc\| - N/2)^2}{N_S N_F N_A N_B}.~~<~~/math~~>

	~~In some cases~~, ~~this is better~~.

	~~: <math>\chi_\text{Yates}^2 = \frac{N~~( ~~\max(0, \|ad - bc\| - N/2)~~ )~~^2}{N_S N_F N_A N_B}~~.~~</math>~~
	~~<!--~~
	~~Some sources say that this correction should be used when the expected frequency is less than 10 {{Citation needed\|date=February 2007}}~~, ~~yet other sources say that Yates' corrections should always be applied {{Citation needed\|date=February 2007}}~~. ~~However, in situations with large sample sizes, using the correction will have little effect on the value of the test statistic~~, and ~~hence the p-value obtained~~.
	~~-->~~

	~~== See also ==~~

	* [[Continuity correction]]
	* [[Binomial_proportion_confidence_interval#Wilson_score_interval_with_continuity_correction\|Wilson score interval with continuity correction]]

	~~==References==~~

	~~{{reflist}}~~

	~~{{DEFAULTSORT:Yates' Correction For Continuity}}~~
	~~[[Category:Categorical data]]~~
	~~[[Category:Statistical tests]]~~

en>Dr Greg: the distinction should be inertial v. non-inertial rather than SR v. GR

2014-01-20T20:08:28Z

the distinction should be inertial v. non-inertial rather than SR v. GR

← Older revision		Revision as of 22:08, 20 January 2014
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	[~~http://Www.answers.com/topic/Grateful Grateful~~] ~~to meet you! My current name is Eusebio Ledbetter. It~~'~~s not a common stuff but what I much like doing~~ is ~~bottle counter tops collecting and now My partner have time to look at on new things. Software developing is how My personal~~ [~~http://search.about.com/?q=support support~~] ~~my family. My house is but~~ in ~~Vermont~~. I'~~ve been working out on my website just for some time now. Inspection it out here: http://prometeu~~.~~net<br><br>~~		In [[statistics]], '''Yates' correction for continuity''' (or '''Yates' chi-squared test''') is used in certain situations when testing for [[independence (probability theory)\|independence]] in a [[contingency table]]. In some cases, Yates' correction may adjust too far, and so its current use is limited.

	~~Also visit my webpage clash~~ of ~~clans hack~~, [~~http~~://~~prometeu~~.~~net click through~~ the following ~~page~~],		==Correction for approximation error==
			Using the [[chi-squared distribution]] to interpret [[Pearson's chi-squared test\|Pearson's chi-squared statistic]] requires one to assume that the [[Discrete probability distribution\|discrete]] probability of observed [[binomial distribution\|binomial frequencies]] in the table can be approximated by the continuous [[chi-squared distribution]]. This assumption is not quite correct, and introduces some error.

			To reduce the error in approximation, [[Frank Yates]], an [[England\|English]] [[statistician]], suggested a correction for continuity that adjusts the formula for [[Pearson's chi-squared test]] by subtracting 0.5 from the difference between each observed value and its expected value in a 2 × 2 contingency table.<ref name=Yates>[[Frank Yates\|Yates, F]] (1934). "Contingency table involving small numbers and the χ<sup>2</sup> test". ''Supplement to the [[Journal of the Royal Statistical Society]]'' '''1'''(2): 217–235. {{jstor\|2983604}}</ref> This reduces the chi-squared value obtained and thus increases its [[p-value]].

			The effect of Yates' correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. Unfortunately, Yates' correction may tend to overcorrect. This can result in an overly conservative result that fails to reject the [[null hypothesis]] when it should (a [[type II error]]). So it is suggested that Yates' correction is unnecessary even with quite low sample sizes,<ref name=Sokal1981>Sokal RR, Rohlf F.J. (1981). ''Biometry: The Principles and Practice of Statistics in Biological Research.'' Oxford: W.H. Freeman, ISBN 0-7167-1254-7.</ref> such as:

			:<math> \sum_{i=1}^N O_i = 20 \, </math>

			The following is Yates' corrected version of [[Pearson's chi-squared test\|Pearson's chi-squared statistic]]:

			:<math> \chi_\text{Yates}^2 = \sum_{i=1}^{N} {(\|O_i - E_i\| - 0.5)^2 \over E_i}</math>

			where:

			:''O<sub>i</sub>'' = an observed frequency
			:''E<sub>i</sub>'' = an expected (theoretical) frequency, asserted by the null hypothesis
			:''N'' = number of distinct events

			== 2 × 2 table ==

			As a short-cut, for a 2 × 2 table with the following entries:

			{\| class="wikitable"
			!   !! S !! F !!
			\|-
			! A
			\| ''a'' \|\| ''b'' \|\| ''N''<sub>A</sub>
			\|-
			! B
			\| ''c'' \|\| ''d'' \|\| ''N''<sub>B</sub>
			\|-
			!
			\| ''N''<sub>S</sub> \|\| ''N''<sub>F</sub> \|\| ''N''
			\|}

			we can write

			: <math>\chi_\text{Yates}^2 = \frac{N(\|ad - bc\| - N/2)^2}{N_S N_F N_A N_B}.</math>

			In some cases, this is better.

			: <math>\chi_\text{Yates}^2 = \frac{N( \max(0, \|ad - bc\| - N/2) )^2}{N_S N_F N_A N_B}.</math>
			<!--
			Some sources say that this correction should be used when the expected frequency is less than 10 {{Citation needed\|date=February 2007}}, yet other sources say that Yates' corrections should always be applied {{Citation needed\|date=February 2007}}. However, in situations with large sample sizes, using the correction will have little effect on the value of the test statistic, and hence the p-value obtained.
			-->

			== See also ==

			* [[Continuity correction]]
			* [[Binomial_proportion_confidence_interval#Wilson_score_interval_with_continuity_correction\|Wilson score interval with continuity correction]]

			==References==

			{{reflist}}

			{{DEFAULTSORT:Yates' Correction For Continuity}}
			[[Category:Categorical data]]
			[[Category:Statistical tests]]

81.71.198.114: /* References */

2012-08-22T05:35:21Z

References

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