Interpreting model coefficients - Revision history

Bosmana fem: /* FEM PAGE CONTRIBUTORS 2007 */

2023-04-10T21:24:29Z

FEM PAGE CONTRIBUTORS 2007

← Older revision		Revision as of 21:24, 10 April 2023
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	However, suppose the logit is not linear. In that case, we should choose another way of analysis, e.g. creating categories of age (age groups) and developing the related design (dummy) variables or using other regression analysis techniques.		However, suppose the logit is not linear. In that case, we should choose another way of analysis, e.g. creating categories of age (age groups) and developing the related design (dummy) variables or using other regression analysis techniques.

	==FEM PAGE CONTRIBUTORS 2007==		<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
			'''FEM PAGE CONTRIBUTORS 2007'''
	; Editor		; Editor
	: Fernando Simon		: Fernando Simon
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	: Lisa Lazareck		: Lisa Lazareck
	: Fernando Simon		: Fernando Simon
			</div>

	[[Category:Fitting logistic regression models]]		[[Category:Fitting logistic regression models]]

Bosmana fem at 20:24, 25 March 2023

2023-03-25T20:24:24Z

← Older revision		Revision as of 20:24, 25 March 2023
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	[[Category:~~Logistic Regression~~]]		[[Category:Fitting logistic regression models]]

Bosmana fem at 20:07, 25 March 2023

2023-03-25T20:07:49Z

← Older revision		Revision as of 20:07, 25 March 2023
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	The logit, including age as a continuous variable, is:		The logit, including age as a continuous variable, is:

	'''g(age) = - 0,6164 + ( 0,0001 x AGE)'''		'''g(age) = - 0,6164 + ( 0,0001 x AGE)'''

	For one year increase in age, the OR is multiplied by 1,0001, assuming linearity between age and log odds of gastroenteritis. For 10 years, an increase in the associated OR would be:		For one year increase in age, the OR is multiplied by 1,0001, assuming linearity between age and log odds of gastroenteritis. For 10 years, an increase in the associated OR would be:

	'''OR (10years) = exp (10 x 0,0001) = 2,72'''		'''OR (10years) = exp (10 x 0,0001) = 2,72'''

Bosmana fem: /* Continuous independent variable */

2023-03-25T20:06:28Z

Continuous independent variable

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	The logit can be expressed as:		The logit can be expressed as:

	g(x) = β0+ β1x1		'''g(x) = β0+ β1x1'''

	Here also the coefficient β1 gives the amount of change in the log odds (logit) for each unit of change in the independent variable.		Here also, the coefficient β1 gives the amount of change in the log odds (logit) for each unit of change in the independent variable.

	The following table illustrates the relationship between age and occurrence of gastroenteritis due to Salmonella.		The following table illustrates the relationship between age and the occurrence of gastroenteritis due to Salmonella.
	{\| class="wikitable" style="background-color:#FFF;"		{\| class="wikitable" style="background-color:#FFF;"
	\|- style="vertical-align:middle;"		\|- style="vertical-align:middle;"
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	\|}		\|}

			Parameter Estimates------------------------------------------95% C.I
	Parameter Estimates---------------------------------95% C.I
	{\| class="wikitable" style="text-align:center;"		{\| class="wikitable" style="text-align:center;"
	\|- style="font-weight:bold;"		\|- style="font-weight:bold;"
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	\| style="font-weight:bold;" \| 1,0199		\| style="font-weight:bold;" \| 1,0199
	\|}		\|}


	The logit, including age as a continuous variable, is:		The logit, including age as a continuous variable, is:
			'''g(age) = - 0,6164 + ( 0,0001 x AGE)'''

	~~g(age) = - 0,6164 + ( 0,0001 x AGE)~~		For one year increase in age, the OR is multiplied by 1,0001, assuming linearity between age and log odds of gastroenteritis. For 10 years, an increase in the associated OR would be:
			'''OR (10years) = exp (10 x 0,0001) = 2,72'''
	For one year increase in age, the OR is multiplied by 1,0001, assuming linearity between age and log odds of gastroenteritis. For 10 years, an increase the associated OR would be:

	OR (10years) = exp (10 x 0,0001) = 2,72

	In the above example, age seems to increase the risk of illness ~~slightly~~.		In the above example, age seems to slightly increase the risk of illness.

	However, suppose the logit is not linear. In that case, we should choose another way of analysis, e.g. creating categories of age (age groups) and developing the related design (dummy) variables or using other regression analysis techniques.		However, suppose the logit is not linear. In that case, we should choose another way of analysis, e.g. creating categories of age (age groups) and developing the related design (dummy) variables or using other regression analysis techniques.

Bosmana fem at 20:05, 25 March 2023

2023-03-25T20:05:25Z

@@ Line 279: / Line 279: @@
 * Dummy variables can be created to indicate different levels of exposure (dose-response analysis).
 * Dummy variables can be created to indicate different levels of a quantitative variable (especially when doubting about linearity, see below).
 ===Continuous independent variable===

Bosmana fem: /* Polytomous variable */

2023-03-25T20:04:31Z

Polytomous variable

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	\|}		\|}

	Parameter Estimates-----------------------------------95% C.I		Parameter Estimates------------------------------------------------95% C.I
	{\| class="wikitable" style="text-align:center;"		{\| class="wikitable" style="text-align:center;"
	\|- style="font-weight:bold;"		\|- style="font-weight:bold;"
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	* Dummy variables can be created to indicate different levels of exposure (dose-response analysis).		* Dummy variables can be created to indicate different levels of exposure (dose-response analysis).
	* Dummy variables can be created to indicate different levels of a quantitative variable (especially when doubting about linearity, see below).		* Dummy variables can be created to indicate different levels of a quantitative variable (especially when doubting about linearity, see below).

			==FEM PAGE CONTRIBUTORS 2007==
			; Editor
			: Fernando Simon
			; Original Author
			: Alain Moren
			; Contributors
			: Arnold Bosman
			: Lisa Lazareck
			: Fernando Simon

	===Continuous independent variable===		===Continuous independent variable===

Bosmana fem: /* Polytomous variable */

2023-03-25T20:02:53Z

Polytomous variable

← Older revision		Revision as of 20:02, 25 March 2023
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	\|}		\|}

	Parameter Estimates------------------------------------------95% C.I		Parameter Estimates---------------------------------------------------95% C.I
	{\| class="wikitable" style="text-align:center; background-color:#FFF;"		{\| class="wikitable" style="text-align:center; background-color:#FFF;"
	\|- style="font-weight:bold;"		\|- style="font-weight:bold;"

Bosmana fem at 20:02, 25 March 2023

2023-03-25T20:02:23Z

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	\|}		\|}

	Parameter Estimates----------------------------------95% C.I		Parameter Estimates------------------------------------------95% C.I
	{\| class="wikitable" style="text-align:center; background-color:#FFF;"		{\| class="wikitable" style="text-align:center; background-color:#FFF;"
	\|- style="font-weight:bold;"		\|- style="font-weight:bold;"

Bosmana fem: /* Dichotomous variable */

2023-03-25T20:01:20Z

Dichotomous variable

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	\|-		\|-
	! colspan="2" \| Number of terms		! colspan="2" \| Number of terms
	!		! 2
	!		!
	\|-		\|-
	\| colspan="2" \| Total Number of Observations		\| colspan="2" \| Total Number of Observations
	\|		\| 245
	\|		\|
	\|-		\|-
	\| colspan="2" \| Rejected as Invalid		\| colspan="2" \| Rejected as Invalid
	\|		\| 0
	\|		\|
	\|-		\|-

Bosmana fem at 19:59, 25 March 2023

2023-03-25T19:59:30Z

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