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Topic: Logistic model

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Logistic map

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	Logistic Population Model
		This is a weakness in the logistic model, and various attempts at correcting this problem have given rise to many variations on the logistic model.
		Despite the weaknesses illustrated above, the logistic model is frequently used in biological modeling, either a basis for more complicated modeling, or as an approximate model when the details of a population dynamics are not known.
		Change the flow in the original logistic Stella model to be bidirectional and repeat the earlier exercises with initial populations in excess of the carrying capacity.
	www.stolaf.edu /people/mckelvey/envision.dir/logistic.html (1331 words)

	Logistic Regression Calculating Page
		To determine whether smoking confounds the catecholamine->CHD association, two odds ratios are needed, a "crude" odds ratio from a logistic regression model with just catecholamine as a predictor of CHD which was 2.8615, and a logistic regression model with two predictors in the model, catecholamine and smoking.
		Logistic regression is a variation of ordinary regression, useful when the observed outcome is restricted to two values, which usually represent the occurrence or non-occurrence of some outcome event, (usually coded as 1 or 0, respectively).
		The Null Model is used as the starting guess for the iterations -- all parameter coefficients are zero, and the intercept is the logarithm of the ratio of the number of cases with y=1 to the number with y=0.
	statpages.org /logistic.html (0 words)

	Human Population Dynamics Revisited With the Logistic Model - Marchetti, Meyer, Ausubel
		We revive the logistic model, which was tested and found wanting in early-20th-century studies of aggregate human populations, and apply it instead to life expectancy (death) and fertility (birth), the key factors totaling population.
		The same model as for fertility rates can be applied to the age of mother or father at the birth of children, but the results are not directly comparable, because in the case of the rates all women in a certain age cohort are counted.
		Logistic "pulse" of fertility during a logistic decline, Finland, 1930-1983.
	phe.rockefeller.edu /poppies (11167 words)

	Statistics Solutions: 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.
		Model chi-square thus tests the null hypothesis that all population logistic regression coefficients except the constant are zero.
		A model in which the predictions, correct or not, were mostly close to the.50 cutoff does not have as good a fit as a model where the predicted scores cluster either near 1.0 or 0.0.
	www.statisticssolutions.com /Logistic_Regression.htm (9713 words)

	1
		The logistic regression does not model the relationship between the probability of Y=1 and the explanatory variables directly, but through the logit function, that is, natural logarithm of odds of Y=1.
		By default proc logistic orders Y values in the ascending order and models the probability of the smaller value (in our case it would be 0).
		The output of proc logistic is the same as for the first program and will be discussed later, in the section on interpretation of logistic regression results.
	www.uky.edu /ComputingCenter/SSTARS/logisticregression_2.htm (1906 words)

	BBR: Bayesian Logistic Regression
		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.
		Score is the model's estimate of the probability for the case to have label 1, while label is the label assigned.
	www.stat.rutgers.edu /~madigan/BBR (3591 words)

	PA 765: SPSS Output for Logistic Regression
		If the logistic model has homoscedasticity (not a logistic regression assumption), the percent correct will be approximately the same for both rows.
		Above SPSS prints the initial test for the model in which the coefficients for all the independent variables are 0.
		The ratio of the logistic coefficient B to its standard error S.E., squared, equals the Wald statistic.
	www2.chass.ncsu.edu /garson/PA765/logispss.htm (1077 words)

	Annotated SPSS Output: Logistic Regression
		Each variable to be entered into the model, e.g., read, science, ses(1) and ses(2), has one degree of freedom, which leads to the total of four shown at the bottom of the column.
		In this example, the statistics for the Step, Model and Block are the same because we have not used stepwise logistic regression or blocking.
		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.
	www.ats.ucla.edu /stat/spss/output/logistic.htm (2981 words)

	Egwald Mathematics — Nonlinear Dynamics
		Introduction to nonlinear dynamics using the model of population dynamics.
		In the continuous time version of the model, solution trajectories of the differential equation governing the growth of employment converge to a stable fixed point.
		In the discrete time version of the model, solution trajectories may follow periodic cycles and eventual exhibit chaotic behaviour as the parameter α of the model's difference equation increase.
	www.egwald.ca /nonlineardynamics (0 words)

	Logistic map - Wikipedia, the free encyclopedia
		The logistic model was originally introduced as a demographic model by Pierre François Verhulst.
		In the case of the logistic map, the quadratic difference equation (1) describing it may be thought of as stretching-and-folding operation on the interval (0,1).
		In the case of the logistic map with parameter r = 4 and an initial state in (0,1), the attractor is also the interval (0,1) and the probability measure corresponds to the beta distribution with parameters a = 0.5 and b = 0.5.
	en.wikipedia.org /wiki/Logistic_map (1218 words)

	Exact Logistic Regression
		Exact logistic regression has become an important analytical technique, especially in the pharmaceutical industry, since the usual asymptotic methods for analyzing small, skewed, or sparse data sets are unreliable.
		Inference based on enumerating the exact distributions of sufficient statistics for parameters of interest in a logistic regression model, conditional on the remaining parameters, is computationally infeasible for many problems.
		For each parameter estimate, the procedure displays either the exact maximum conditional likelihood estimate or the median unbiased estimate, the exponential of the estimate, one- or two-sided confidence limits, and a one- or two-sided p-value for testing that the parameter is equal to zero.
	support.sas.com /rnd/app/da/new/daexactlogistic.html (874 words)

	Malthusian growth model - Wikipedia, the free encyclopedia
		The model is named after the Reverend Thomas Malthus, who authored An Essay on the Principle of Population, one of the earliest and most influential books on population.
		As noted by Professor Peter Turchin (Does population ecology have general laws?, 2001 and Complex Population Dynamics, 2003), this model is often referred to as The Exponential Law and is widely regarded in the field of population ecology as the first principle of population dynamics, with Malthus as the founder.
		The Malthusian growth model is the direct ancestor of the logistic function.
	en.wikipedia.org /wiki/Malthusian_growth_model (613 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.
		The logistic model formula computes the probability of the selected response as a function of the values of the predictor variables.
		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.
	www.dtreg.com /logistic.htm (1667 words)

	IIASA - Logistic Substitution Model II
		Model estimations are user defined in terms of model selection, data periods used for parameter estimation, or in terms of exogenously specified scenario values (used e.g.
		An extension of the model in the latter direction is planned.
		In 1994 the original model code was reprogrammed by M. Posch into two separate software packages FIT (for estimation of S-curves) and LSM (for estimating logistic substitution patterns) and ran on the DOS/Windows platform of that time.
	www.iiasa.ac.at /Research/TNT/WEB/Software/LSM2/lsm2-index.html (491 words)

	Statistical Computing Seminars: Introduction to SAS PROC LOGISTIC
		Logistic regression describes the relationship between a categorical response variable and a set of predictor variables.
		For a binary response variable, such as a response to a yes-no question, a commonly used model is the logistic regression model.
		A logistic regression model describes a linear relationship between the logit, which is the log of odds, and a set of predictors.
	www.ats.ucla.edu /stat/sas/seminars/sas_logistic/logistic1.htm (5234 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.
		If the P-value for the overall model fit statistic is less than the conventional 0.05 then there is evidence that at least one of the independent variables contributes to the prediction of the outcome.
	www.medcalc.be /manual/logistic_regression.php (1499 words)

	[No title]
		The first form, referred to as the actual MODEL syntax, is applicable to both binary response data and ordinal response data.
		The second form, referred to as the events/trials MODEL syntax, is restricted to the binary response data case.
		PROC LOGISTIC treats each observation as if it appears n times, where n is the value of the FREQ variable for the observation.
	www.uic.edu /classes/bstt/bstt511/SAScode/Proc_logistic.doc (2050 words)

	LOGISTIC
		One way to model this assumption is to let the birthrate depend linearly on the size of the population as follows:
		Since any model or simulation is valid only under limited conditions, we want to be aware of limitations of the mathematical model.
		The previous unlimited growth population model is based on the assumption that the relative growth rate k is constant, but in fact the growth rates of populations change over time.
	isolatium.uhh.hawaii.edu /m206L/lab8/Logistic/Logistic.htm (676 words)

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