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Topic: Logistic regression

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Logit

Logistic function

Conditional probability

Sigmoid curve

Odds ratio

Linear regression

Regression analysis

Neural networks

Generalised logistic curve

Information entropy

Logarithm

Preference regression (in marketing)

Bayesian probability

Bathtub curve

Multivariate statistics

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	Logistic Regression
		Logistic regression is used to determine whether other measurements are related to the presence of some characteristic--for example, whether certain blood measures are predictive of having a disease.
		While the response variable in a logistic regression is a 0/1 variable, the logistic regression equation, which is a linear equation, does not predict the 0/1 variable itself.
		To be precise, logistic regression equation does not directly predict the probability that the indicator is equal to 1.
	www.tufts.edu /~gdallal/logistic.htm (992 words)

	PlanetMath: logistic regression
		Logistic regression is a particular type of generalized linear model.
		Comparing model equation for the logistic regression to that of the normal or Gaussian linear regression model, we see that the difference is in the choice of link function.
		This is version 9 of logistic regression, born on 2004-11-04, modified 2006-09-23.
	planetmath.org /encyclopedia/LogisticRegression.html (456 words)

	Logistic Regression
		Logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these.
		For example, logistic regression is often used in epidemiological studies where the result of the analysis is the probability of developing cancer after controlling for other associated risks.
		Logistic regression also provides knowledge of the relationships and strengths among the variables (e.g., smoking 10 packs a day puts you at a higher risk for developing cancer than working in an asbestos mine).
	userwww.sfsu.edu /~efc/classes/biol710/logistic/logisticreg.htm (1020 words)

	Statistics Solutions : Binomial Logistic Regression
		Logistic reqression can be used to predict a dependent variable on the basis of continuous and/or categorical independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables.
		Logistic regression applies maximum likelihood estimation after transforming the dependent into a logit variable (the natural log of the odds of the dependent occurring or not).
		The logistic regression model is run against the dependent for the full model with independents and covariates, then is run again with the block of independents dropped.
	www.statisticssolutions.com /Binomial-Logistic-Regression.htm (3474 words)

	Statistics Solutions : Logistic Regression (Site not responding. Last check: 2007-11-04)
		Parameter estimates (b coefficients) are logits of explanatory variables used in the logistic regression equation to estimate the log odds that the dependent equals 1 (binomial logistic regression) or that the dependent equals its highest/last value (multinomial logistic regression [though the researcher may select any value as the reference value, overriding the default]).
		The convention for binomial logistic regression is to code the dependent class of greatest interest as 1 and the other class as 0, and to code its expected correlates also as +1 to assure positive correlation.
		Logistic regression does not require linear relationships between the independent factor or covariates and the dependent, as does OLS regression, but it does assume a linear relationship between the independents and the log odds (logit) of the dependent.
	www.statisticssolutions.com /Logistic_Regression.htm (9721 words)

	Logistic regression
		Another situation that calls for logistic regression, rather than an anova or t-test, is when the values of the measurement variable are set by the experimenter, while the values of the attribute variable are free to vary.
		What makes logistic regression different is that the Y variable is not directly measured; it is instead the probability of obtaining a particular value of an attribute variable.
		Choosing variables in logistic regression, based on which ones contribute significantly to the relationship between Y and the X variables, is an important part of logistic regression.
	udel.edu /~mcdonald/statlogistic.html (1437 words)

	Logistic Regression - Introduction
		Logistic regression is a variation of ordinary regression which is used when the dependent (response) variable is a dichotomous variable (i.
		Unlike ordinary linear regression, logistic regression does not assume that the relationship between the independent variables and the dependent variable is a linear one.
		Logistic regression thus forms a predictor variable (log (p/(1-p)) which is a linear combination of the explanatory variables.
	www.resample.com /xlminer/help/Lreg/lreg_intro.htm (307 words)

	Logistic regression (Site not responding. Last check: 2007-11-04)
		Hosmer and Lemeshow, 1989; Armitage and Berry, 1994; Altman 1991; McCullagh and Nelder, 1989; Cox and Snell, 1989; Pregibon, 1981
		are fed back into the logistic regression and bootstrap' estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process.
		The statistic is used to detect observations that have a strong influence upon the regression estimates.
	www.statsdirect.com /help/regression_and_correlation/logi.htm (1438 words)

	Test Versus Control, Part 3
		Logistic regression is a variation of ordinary regression.
		The main difference between the two is in ordinary regression the dependent variable is an amount (e.g., sales) or score, while in logistical regression the dependent variable is an event occurrence.
		The nice aspect of logistic regression is it allows the analyst to measure the impact of multiple variables at the same time, something that is not possible in the testing methodology presented in part two of this series.
	clickz.com /showPage.html?page=1430641 (1019 words)

	PSY6003: Logistic regression and discriminant analysis
		In SPSS at least, logistic regression is easier to use than discriminant analysis when we have a mixture of numerical and categorical regressors, because it includes procedures for generating the necessary dummy variables automatically.
		But it is now being replaced with logistic regression, as this approach requires fewer assumptions in theory, is more statistically robust in practice, and is easier to use and understand than discriminant analysis.
		In thinking about logistic regression, two hypotheses are likely to be of interest: the null hypothesis, which is that all the coefficients in the regression equation take the value zero, and the hypothesis that the model currently under consideration is accurate.
	www.people.ex.ac.uk /SEGLea/multvar2/disclogi.html (3323 words)

	PA 765: Logistic Regression
		In a logistic regression context, the Box-Tidwell transformation and orthogonal polynomial contrasts are ways of testing linearity among the independents.
		In general, nonparametric regression as discussed in the section on OLS regression can be extended to the case of GLM regression models like logistic regression.
		The authors make the case for the superiority of logistic regression for situations where the assumptions of multivariate normality are not met (ex., when dummy variables are used), though discriminant analysis is held to be better when they are.
	www2.chass.ncsu.edu /garson/PA765/logistic.htm (12398 words)

	Logistic Regression
		As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered.
		In summary, the logistic formula has each continuous predictor variable, each dichotomous predictor variable with a value of 0 or 1, and a dummy variable for every category of predictor variables with more than two categories less one category.
		Most logistic regression analyses converge to a solution in a dozen or so iterations, but you may occasionally run into one that does not converge.
	www.dtreg.com /logistic.htm (1667 words)

	Categorical Data: Part 6: Logistic Regression
		Logistic regression describes the relationship between a dichotomous response variable and a set of explanatory variables.
		The logistic regression model fits the log odds by a linear function of the explanatory variables (as is multiple regression).
		Comparison of the regression coefficients in the two sub-models (in relation to the size of their standard errors) indicates why the proportional odds model was not tenable.
	www.math.yorku.ca /SCS/Courses/grcat/grc6.html (5061 words)

	Logistic Regression
		One of the assumptions of regression is that the variance of Y is constant across values of X (homoscedasticity).
		In logistic regression, the dependent variable is a logit, which is the natural log of the odds, that is,
		The main interpretation of logistic regression results is to find the significant predictors of Y. However, other things can sometimes be done with the results.
	luna.cas.usf.edu /~mbrannic/files/regression/Logistic.html (2682 words)

	Annotated SPSS Output: Logistic Regression
		Logistic regression does not have an equivalent to the R-squared that is found in OLS regression; however, many people have tried to come up with one.
		Because this statistic does not mean what R-squared means in OLS regression (the proportion of variance explained by the predictors), we suggest interpreting this statistic with great caution.
		B - These are the values for the logistic regression equation for predicting the dependent variable from the independent variable.
	www.ats.ucla.edu /stat/spss/output/logistic.htm (2981 words)

	Logistic regression - MedCalc manual
		Logistic regression is a technique for analyzing problems in which there are one or more independent variables that determine an outcome.
		The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables.
		Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest:
	www.medcalc.be /manual/logistic_regression.php (1499 words)

	Logistic Regression
		Logistic regression can be used with any combination of explanatory variables, numerical, categorical, or some of each.
		is the constant and B is the vector of logistic regression parameters, to be estimated.
		Thus this family of transformations of P includes the logistic regression model as a special case.
	www.uic.edu /classes/idsc/ids570/logistic_regr.htm (997 words)

	Conditional logistic regression (Site not responding. Last check: 2007-11-04)
		are used for handling the errors associated with regression models for binary/dichotomous responses (i.e.
		The regression is fitted by maximisation of the natural logarithm of the conditional likelihood function using Newton-Raphson iteration as desc
		Note that the selection of predictors for regression models such as this can be complex and is best done with the help of a Statistician.
	www.statsdirect.com /help/regression_and_correlation/conditional_logistic_regression.htm (571 words)

	BBR: Bayesian Logistic Regression (Site not responding. Last check: 2007-11-04)
		Logistic regression models estimate the probability that a data vector belongs to the class with label 1.
		Classification with a logistic regression model typically uses a threshold: we assign a case to class 1 iff the probability estimate is greater or equal to the threshold value.
		The constant feature 1 that corresponds to the intercept terms of the logistic regression model does not participate in, and is not affected by either centering and scaling or cosine normalization.
	www.stat.rutgers.edu /~madigan/BBR (3613 words)

	3.3 Logistic Regression
		Logistic regression is used to model the relationship between a binary response variable and one or more predictor variables, which may be either discrete or continuous.
		To determine the overall significance for a model using the G statistic, the deviance for the model and the deviance for the intercept-only model are subtracted.
		In other words, the data values for each predictor variable are replaced with integer values, the logistic regression parameters are recalculated, and the statistic is obtained from the resulting model.
	www.roguewave.com /support/docs/hppdocs/anaug/3-3.html (815 words)

	Amazon.com: Applied Logistic Regression (Wiley Series in Probability and Statistics - Applied Probability and ... (Site not responding. Last check: 2007-11-04)
		Hosmer and Lemeshow point to the massive growth in applications of logistic regression over a ten year period from the time of publication of the first edition of their text.
		Also included is the use of logistic regression in the analysis of complex survey sampling data and for the modeling of matched studies.
		The book is intended for a graduate course in logistic regression requiring the student to be familiar with linear regression and contingency tables.
	www.amazon.com /Applied-Logistic-Regression-Probability-Statistics/dp/0471356328 (1722 words)

	MMU - Multivariate Statistics, Biol Sci:Logistic regression (Site not responding. Last check: 2007-11-04)
		Logistic regression is a technique that assumes the errors are drawn from a binomial distribution.
		In logistic regression the dependent variable is the probability that an event will occur, hence y is constrained between 0 and 1.
		Logistic regression has the additional advantage that all of the predcitors can be binary, a mixture of categorical and continuous or just continuous.
	obelia.jde.aca.mmu.ac.uk /multivar/lr.htm (697 words)

	Logistic Regression with SAS
		Then you merge the new data with the original data and run the logistic regression using the merged data set.
		Logistic regression can be modeled as a class of generalized linear model where the response probability distribution function is binomial and the link function is logit.
		A logistic regression for these data is a generalized linear model with response equal to the binomial proportion R/N. PROC GENMOD can be used as follows:
	www.indiana.edu /~statmath/stat/all/cat/1b1.html (2140 words)

	SPSS Regression Models \| Data Analysis (Site not responding. Last check: 2007-11-04)
		Using SPSS Regression Models with SPSS Base gives you an even wider range of statistics so you can get the most accurate response for specific data types.
		The multinomial logistic regression procedure predicts a categorical outcome such as "primary reason for Web use." The categories in this example are: a) work only, b) shopping only, c) both working and shopping, and d) neither (reference category).
		With binary logistic regression, you can select variables using six types of stepwise methods, including forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) and backward (at each step, the procedure removes the least significant predictor in the dataset) methods.
	www.spss.com /regression/data_analysis.htm (627 words)

	Logistic Regression
		To get around this problem, we use logistic regression, as opposed to ordinary linear regression.
		We can look at the interactions between variables in the same way, by including them in the "model" section of the binary logistic regression interface in (variable_A)*(variable_B) form.
		For further information on logistic regression, a recommended text is David Hosmer and Stanley Lemeshow's Applied Logistic Regression.
	www.maths.tcd.ie /~deetoher/logisticregression.html (452 words)

	Logistic Regression with SPSS
		Unlike in SAS, the SPSS procedure LOGISTIC REGRESSION models the probability of Y=1 or Y's higher sorted value.
		This is the same result as with the use of the DESCENDING option in SAS PROC LOGISTIC.
		Permission to use this document is granted so long as the author is acknowledged and notified.
	www.indiana.edu /~statmath/stat/all/cat/1b2.html (380 words)

	Categorical Data Analysis Using Logistic Regression
		The topics include performing stratified data analysis, using model-building strategies, assessing the fit of a binary logistic regression model, and detecting interactions and nonlinear effects.
		You will also learn how to fit ordinal logistic regression models, GEE models, exact logistic regression models, conditional logistic regression models, and nominal logistic regression models.
		have completed a course in statistics that covers linear regression and logistic regression.
	support.sas.com /training/us/crs/cdalr.html (566 words)

	SPSS Regression Models \| Logistic Regression Analysis (Site not responding. Last check: 2007-11-04)
		SPSS Regression Models enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models.
		SPSS Regression Models is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian.
		SPSS Regression Models 15.0 is now available in North America.
	www.spss.com /regression (246 words)

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