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		<title>Statistics Help @ Talk Stats Forum - Regression Analysis</title>
		<link>http://www.talkstats.com/</link>
		<description>Linear regression, linear models, nonlinear regression</description>
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		<lastBuildDate>Wed, 22 May 2013 21:52:22 GMT</lastBuildDate>
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			<title>Statistics Help @ Talk Stats Forum - Regression Analysis</title>
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			<title>Help understanding Microsoft Excel ANOVA output (Significance F ?)</title>
			<link>http://www.talkstats.com/showthread.php/43798-Help-understanding-Microsoft-Excel-ANOVA-output-(Significance-F-)?goto=newpost</link>
			<pubDate>Wed, 22 May 2013 13:29:40 GMT</pubDate>
			<description>Hi, 
 
So I created my own very simple excel table, with a set of X and Y values, and then I told Excel to run a regression analysis. 
...</description>
			<content:encoded><![CDATA[<div>Hi,<br />
<br />
So I created my own very simple excel table, with a set of X and Y values, and then I told Excel to run a regression analysis.<br />
<br />
<a href="http://s22.postimg.org/41djtsab3/stats.png" target="_blank">http://s22.postimg.org/41djtsab3/stats.png</a><br />
<br />
I have been going through every piece of info provided by the Excel output table, attempting to understand what it all means and how each piece of info is calculated.<br />
<br />
Sadly I am struggling to understand &quot;Significance F&quot;.<br />
I am really rusty with statistics and I believe I might need to look this up in a table... but I did google for &quot;F distribution tables&quot; and I cannot see any figures anywhere even close to 0.124027063 ...<br />
<br />
Can someone please help me understand this?<br />
<br />
Thanks a lot in advance.</div>

 ]]></content:encoded>
			<category domain="http://www.talkstats.com/forumdisplay.php/25-Regression-Analysis">Regression Analysis</category>
			<dc:creator>spearchew</dc:creator>
			<guid isPermaLink="true">http://www.talkstats.com/showthread.php/43798-Help-understanding-Microsoft-Excel-ANOVA-output-(Significance-F-)</guid>
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		<item>
			<title>Asasociation between two series of measurements</title>
			<link>http://www.talkstats.com/showthread.php/43788-Asasociation-between-two-series-of-measurements?goto=newpost</link>
			<pubDate>Wed, 22 May 2013 09:36:22 GMT</pubDate>
			<description>I have series of (metric) lab variables measured in a group of patients, with once daily measurements over 20 days, and I would like to investigate...</description>
			<content:encoded><![CDATA[<div>I have series of (metric) lab variables measured in a group of patients, with once daily measurements over 20 days, and I would like to investigate the association between the time courses of two of these variables. What is the preferred statistical procedure for analyzing such associations?</div>

 ]]></content:encoded>
			<category domain="http://www.talkstats.com/forumdisplay.php/25-Regression-Analysis">Regression Analysis</category>
			<dc:creator>AVoelp</dc:creator>
			<guid isPermaLink="true">http://www.talkstats.com/showthread.php/43788-Asasociation-between-two-series-of-measurements</guid>
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		<item>
			<title>RMA regression</title>
			<link>http://www.talkstats.com/showthread.php/43773-RMA-regression?goto=newpost</link>
			<pubDate>Tue, 21 May 2013 20:18:33 GMT</pubDate>
			<description><![CDATA[Hello! 
 
I'm trying to fit regression as a modeling approach between predictor variables (radiation, temperature, relative humidity, etc.) and...]]></description>
			<content:encoded><![CDATA[<div>Hello!<br />
<br />
I'm trying to fit regression as a modeling approach between predictor variables (radiation, temperature, relative humidity, etc.) and response variables (physiological parameters).<br />
Since the predictor variables influence each other I cannot use a multiple regression due to collinarity. So, I had to use simple linear regression for each predictor variable. Since both types of variables are random (not controlled by me) I have to use a Model II regression, and specifically the reduced or range major axis (RMA) regression. <br />
What about if I divide the the range of these predictor variable into three levels, i.e. high radiation or temperature, medium, and low with the respective values of the response variable. Should I still use RMA regression as the measurements are prone to error or since I have categorised them it can be considered as a &quot;controlled&quot; and use OLS regression instead?<br />
Quick advice is highly appreciated!<br />
Thank you in advance,<br />
Endrit</div>

 ]]></content:encoded>
			<category domain="http://www.talkstats.com/forumdisplay.php/25-Regression-Analysis">Regression Analysis</category>
			<dc:creator>ekullaj</dc:creator>
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