Generalized linear model - Revision history

Conservative at 07:43, February 9, 2019

2019-02-09T07:43:53Z

Conservative: Reverted edits by Conservative (talk) to last revision by DavidB4-bot

2019-02-09T07:40:37Z

Reverted edits by Conservative (talk) to last revision by DavidB4-bot

Conservative at 07:40, February 9, 2019

2019-02-09T07:40:18Z

DavidB4-bot: /* Notes */clean up & uniformity

2016-07-13T13:20:41Z

Notes: clean up & uniformity

Karajou: Reverted edits by Dolphin11 (talk) to last revision by FrederickT3

2012-01-03T19:29:42Z

Reverted edits by Dolphin11 (talk) to last revision by FrederickT3

Dolphin11 at 19:28, January 3, 2012

2012-01-03T19:28:37Z

FrederickT3: The definition of "generalized" appears further down on statsoft.

2011-08-17T07:13:55Z

The definition of "generalized" appears further down on statsoft.

Conservative at 06:13, August 10, 2011

2011-08-10T06:13:51Z

Toadaron: The equation is clearer with subscripts

2011-03-27T16:37:49Z

The equation is clearer with subscripts

Conservative: Created page with "The website statsoft.com offers this explanation of the '''generalized linear model''': "The Generalized Linear Model (GLZ) is a generalization of the general linear model (see,..."

2011-02-02T02:21:01Z

Created page with "The website statsoft.com offers this explanation of the '''generalized linear model''': "The Generalized Linear Model (GLZ) is a generalization of the general linear model (see,..."

New page

The website statsoft.com offers this explanation of the '''generalized linear model''':

"The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that

Y = b0 + b1X1 + b2X2 + ... + bkXk

In this [[equation]] b0 is the [[regression analysis|regression]] [[coefficient]] for the intercept and the bi values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>http://www.statsoft.com/textbook/generalized-linear-models/</ref>
== See also ==

*[[Statistics]]

== Notes ==
{{reflist|2}}

[[Category: Statistics]]

@@ Line 1: / Line 1: @@
 The website statsoft.com offers this explanation of the '''generalized linear model''':
-"The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that
+"The Generalized Linear Model (GLZ) is a generalization of the [[General linear model|general linear model]] (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that
 Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub>

@@ Line 1: / Line 1: @@
 The website statsoft.com offers this explanation of the '''generalized linear model''':
-{{Cquote|The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that
 Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub>
-In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>}}
+In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
 Whereas in classical linear models the variable Y is assumed to follow a normal distribution, generalized linear models consider more general distributions, such as a Poisson or a multinomial distribution, and are therefore applicable to a wider field of problems.

@@ Line 1: / Line 1: @@
 The website statsoft.com offers this explanation of the '''generalized linear model''':
+{{Cquote|The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that
 Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub>
-In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
+In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>}}
 Whereas in classical linear models the variable Y is assumed to follow a normal distribution, generalized linear models consider more general distributions, such as a Poisson or a multinomial distribution, and are therefore applicable to a wider field of problems.

← Older revision		Revision as of 13:20, July 13, 2016
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	{{reflist\|2}}		{{reflist\|2}}

−	[[Category: Statistics]]	+	[[Category:Statistics]]

@@ Line 5: / Line 5: @@
 Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub>
-In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficien#REDIRECT [[Insert redirect]]ts (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
+In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
 Whereas in classical linear models the variable Y is assumed to follow a normal distribution, generalized linear models consider more general distributions, such as a Poisson or a multinomial distribution, and are therefore applicable to a wider field of problems.

← Older revision		Revision as of 07:13, August 17, 2011
Line 6:		Line 6:

	In this [[equation]] b<sub>0</sub> is the [[regression analysis\|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>		In this [[equation]] b<sub>0</sub> is the [[regression analysis\|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
		+
		+	Whereas in classical linear models the variable Y is assumed to follow a normal distribution, generalized linear models consider more general distributions, such as a Poisson or a multinomial distribution, and are therefore applicable to a wider field of problems.
		+
	== See also ==		== See also ==

@@ Line 3: / Line 3: @@
 "The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|MANOVA]] topics). In its simplest form, a [[linear model]] specifies the (linear) relationship between a dependent (or response) [[variable]] Y, and a set of predictor variables, the X's, so that
-Y = b0 + b1X1 + b2X2 + ... + bkXk
+Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub>
-In this [[equation]] b0 is the [[regression analysis|regression]] [[coefficient]] for the intercept and the bi values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>http://www.statsoft.com/textbook/generalized-linear-models/</ref>
+In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>http://www.statsoft.com/textbook/generalized-linear-models/</ref>
 == See also ==