**Logistic Models**: Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.

**Risk Factors**: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.

**Odds Ratio**: The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.

**Multivariate Analysis**: A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.

**Cross-Sectional Studies**: Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.

**Case-Control Studies**: Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.

**Biostatistics**: The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.

**Prevalence**: The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.

**Socioeconomic Factors**: Social and economic factors that characterize the individual or group within the social structure.

**United States**

**ROC Curve**: A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.

**Models, Statistical**: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

**Retrospective Studies**: Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.

**Age Factors**: Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.

**Cohort Studies**: Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.

**Predictive Value of Tests**: In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

**Sex Factors**: Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.

**Risk Assessment**: The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)

**Prospective Studies**: Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.

**Smoking**: Inhaling and exhaling the smoke of burning TOBACCO.

**Questionnaires**: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.

**Time Factors**: Elements of limited time intervals, contributing to particular results or situations.

**Japan**

**Health Surveys**: A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.

**Longitudinal Studies**: Studies in which variables relating to an individual or group of individuals are assessed over a period of time.

**Pregnancy**: The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.

**Probability**: The study of chance processes or the relative frequency characterizing a chance process.

**France**: A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.

**Residence Characteristics**: Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.

**Regression Analysis**: Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.

**Confidence Intervals**: A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.

**Infant, Newborn**: An infant during the first month after birth.

**Epidemiologic Methods**: Research techniques that focus on study designs and data gathering methods in human and animal populations.

**Incidence**: The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.

**Sensitivity and Specificity**: Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)

**Follow-Up Studies**: Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.

**Linear Models**: Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.

**Reproducibility of Results**: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.

**Hospital Mortality**: A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.

**Italy**

**Food Microbiology**: The presence of bacteria, viruses, and fungi in food and food products. This term is not restricted to pathogenic organisms: the presence of various non-pathogenic bacteria and fungi in cheeses and wines, for example, is included in this concept.

**Severity of Illness Index**: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.

**Brazil**

**Educational Status**: Educational attainment or level of education of individuals.

**Prognosis**: A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.

**European Continental Ancestry Group**: Individuals whose ancestral origins are in the continent of Europe.

**Genetic Predisposition to Disease**: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.

**China**: A country spanning from central Asia to the Pacific Ocean.

**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**Treatment Outcome**: Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.

**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Comorbidity**: The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.

**Health Status**: The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.

**Ethnic Groups**: A group of people with a common cultural heritage that sets them apart from others in a variety of social relationships.

**Genotype**: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.

**Models, Biological**: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

**Alcohol Drinking**: Behaviors associated with the ingesting of alcoholic beverages, including social drinking.

**Risk**: The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.

**Models, Theoretical**: Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**African Americans**: Persons living in the United States having origins in any of the black groups of Africa.

**Chi-Square Distribution**: A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.

**Body Mass Index**: An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)

**Obesity**: A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).

**Occupational Exposure**: The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.

**Statistics as Topic**: The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.

**Life Style**: Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)

**Data Collection**: Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.

**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Polymorphism, Single Nucleotide**: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.

**HIV Infections**: Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).

**Breast Neoplasms**: Tumors or cancer of the human BREAST.

**Hypertension**: Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.

**Biological Markers**: Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.

**Polymorphism, Genetic**: The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Survival Analysis**: A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.

**Analysis of Variance**: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

## Analysis of the effect of conversion from open to closed surgical intensive care unit. (1/24374)

OBJECTIVE: To compare the effect on clinical outcome of changing a surgical intensive care unit from an open to a closed unit. DESIGN: The study was carried out at a surgical intensive care unit in a large tertiary care hospital, which was changed on January 1, 1996, from an open unit, where private attending physicians contributed and controlled the care of their patients, to a closed unit, where patients' medical care was provided only by the surgical critical care team (ABS or ABA board-certified intensivists). A retrospective review was undertaken over 6 consecutive months in each system, encompassing 274 patients (125 in the open-unit period, 149 in the closed-unit period). Morbidity and mortality were compared between the two periods, along with length-of-stay (LOS) and number of consults obtained. A set of independent variables was also evaluated, including age, gender, APACHE III scores, the presence of preexisting medical conditions, the use of invasive monitoring (Swan-Ganz catheters, central and arterial lines), and the use of antibiotics, low-dose dopamine (LDD) for renal protection, vasopressors, TPN, and enteral feeding. RESULTS: Mortality (14.4% vs. 6.04%, p = 0.012) and the overall complication rate (55.84% vs. 44.14%, p = 0.002) were higher in the open-unit group versus the closed-unit group, respectively. The number of consults obtained was decreased (0.6 vs. 0.4 per patient, p = 0.036), and the rate of occurrence of renal failure was higher in the open-unit group (12.8% vs. 2.67%, p = 0.001). The mean age of the patients was similar in both groups (66.48 years vs. 66.40, p = 0.96). APACHE III scores were slightly higher in the open-unit group but did not reach statistical significance (39.02 vs. 36.16, p = 0.222). There were more men in the first group (63.2% vs. 51.3%). The use of Swan-Ganz catheters or central and arterial lines were identical, as was the use of antibiotics, TPN, and enteral feedings. The use of LDD was higher in the first group, but the LOS was identical. CONCLUSIONS: Conversion of a tertiary care surgical intensive care unit from an open to closed environment reduced dopamine usage and overall complication and mortality rates. These results support the concept that, when possible, patients in surgical intensive care units should be managed by board-certified intensivists in a closed environment. (+info)## Antiphospholipid, anti-beta 2-glycoprotein-I and anti-oxidized-low-density-lipoprotein antibodies in antiphospholipid syndrome. (2/24374)

Antiphospholipid antibodies (aPL), anti-beta 2-glycoprotein I (anti-beta 2-GPI) and anti-oxidized-low-density lipoprotein (LDL) antibodies are all implicated in the pathogenesis of antiphospholipid syndrome. To investigate whether different autoantibodies or combinations thereof produced distinct effects related to their antigenic specificities, we examined the frequencies of antiphospholipid syndrome (APS)-related features in the presence of different antibodies [aPL, beta 2-GPI, anti-oxidized low density lipoprotein (LDL)] in 125 patients with APS. Median follow-up was 72 months: 58 patients were diagnosed as primary APS and 67 as APS plus systemic lupus erythematosus (SLE). Anticardiolipin antibodies (aCL), anti-beta 2-GPI and anti-oxidized LDL antibodies were determined by ELISA; lupus anticoagulant (LA) by standard coagulometric methods. Univariate analysis showed that patients positive for anti-beta 2-GPI had a higher risk of recurrent thrombotic events (OR = 3.64, 95% CI, p = 0.01) and pregnancy loss (OR = 2.99, 95% CI, p = 0.004). Patients positive for anti-oxidized LDL antibodies had a 2.24-fold increase in the risk of arterial thrombosis (2.24, 95% CI, p = 0.03) and lower risk of thrombocytopenia (OR = 0.41 95% CI, p = 0.04). Patients positive for aCL antibodies had a higher risk of pregnancy loss (OR = 4.62 95% CI, p = 0.001). When these data were tested by multivariate logistic regression, the association between anti-beta 2-GPI and pregnancy loss and the negative association between anti-oxidized LDL antibodies and thrombocytopenia disappeared. (+info)## Capture-recapture models including covariate effects. (3/24374)

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries. (+info)## Risk factors for injuries and other health problems sustained in a marathon. (4/24374)

OBJECTIVES: To identify risk factors for injuries and other health problems occurring during or immediately after participation in a marathon. METHODS: A prospective cohort study was undertaken of participants in the 1993 Auckland Citibank marathon. Demographic data, information on running experience, training and injuries, and information on other lifestyle factors were obtained from participants before the race using an interviewer-administered questionnaire. Information on injuries and other health problems sustained during or immediately after the marathon were obtained by a self administered questionnaire. Logistic regression analyses were undertaken to identify significant risk factors for health problems. RESULTS: This study, one of only a few controlled epidemiological studies that have been undertaken of running injuries, has identified a number of risk factors for injuries and other health problems sustained in a marathon. Men were at increased risk of hamstring and calf problems, whereas women were at increased risk of hip problems. Participation in a marathon for the first time, participation in other sports, illness in the two weeks before the marathon, current use of medication, and drinking alcohol once a month or more, were associated with increased self reported risks of problems. While increased training seemed to increase the risk of front thigh and hamstring problems, it may decrease the risk of knee problems. There are significant but complex relations between age and risk of injury or health problem. CONCLUSIONS: This study has identified certain high risk subjects and risk factors for injuries and other health problems sustained in a marathon. In particular, subjects who have recently been unwell or are taking medication should weigh up carefully the pros and cons of participating. (+info)## Early mycological treatment failure in AIDS-associated cryptococcal meningitis. (5/24374)

Cryptococcal meningitis causes significant morbidity and mortality in persons with AIDS. Of 236 AIDS patients treated with amphotericin B plus flucytosine, 29 (12%) died within 2 weeks and 62 (26%) died before 10 weeks. Just 129 (55%) of 236 patients were alive with negative cerebrospinal fluid (CSF) cultures at 10 weeks. Multivariate analyses identified that titer of cryptococcal antigen in CSF, serum albumin level, and CD4 cell count, together with dose of amphotericin B, had the strongest joint association with failure to achieve negative CSF cultures by day 14. Among patients with similar CSF cryptococcal antigen titers, CD4 cell counts, and serum albumin levels, the odds of failure at week 10 for those without negative CSF cultures by day 14 was five times that for those with negative CSF cultures by day 14 (odds ratio, 5.0; 95% confidence interval, 2.2-10.9). Prognosis is dismal for patients with AIDS-related cryptococcal meningitis. Multivariate analyses identified three components that, along with initial treatment, have the strongest joint association with early outcome. Clearly, more effective initial therapy and patient management strategies that address immune function and nutritional status are needed to improve outcomes of this disease. (+info)## The Sock Test for evaluating activity limitation in patients with musculoskeletal pain. (6/24374)

BACKGROUND AND PURPOSE: Assessment within rehabilitation often must reflect patients' perceived functional problems and provide information on whether these problems are caused by impairments of the musculoskeletal system. Such capabilities were examined in a new functional test, the Sock Test, simulating the activity of putting on a sock. SUBJECTS AND METHODS: Intertester reliability was examined in 21 patients. Concurrent validity, responsiveness, and predictive validity were examined in a sample of 337 patients and in subgroups of this sample. RESULTS: Intertester reliability was acceptable. Sock Test scores were related to concurrent reports of activity limitation in dressing activities. Scores also reflected questionnaire-derived reports of problems in a broad range of activities of daily living and pain and were responsive to change over time. Increases in age and body mass index increased the likelihood of Sock Test scores indicating activity limitation. Pretest scores were predictive of perceived difficulties in dressing activities after 1 year. CONCLUSION AND DISCUSSION: Sock Test scores reflect perceived activity limitations and restrictions of the musculoskeletal system. (+info)## Modified cuspal relationships of mandibular molar teeth in children with Down's syndrome. (7/24374)

A total of 50 permanent mandibular 1st molars of 26 children with Down's syndrome (DS) were examined from dental casts and 59 permanent mandibular 1st molars of normal children were examined from 33 individuals. The following measurements were performed on both right and left molars (teeth 46 and 36 respectively): (a) the intercusp distances (mb-db, mb-d, mb-dl, db-ml, db-d, db-dl, db-ml, d-dl, d-ml, dl-ml); (b) the db-mb-ml, mb-db-ml, mb-ml-db, d-mb-dl, mb-d-dl, mb-dl-d angles; (c) the area of the pentagon formed by connecting the cusp tips. All intercusp distances were significantly smaller in the DS group. Stepwise logistic regression, applied to all the intercusp distances, was used to design a multivariate probability model for DS and normals. A model based on 2 distances only, mb-dl and mb-db, proved sufficient to discriminate between the teeth of DS and the normal population. The model for tooth 36 for example was as follows: p(DS) = (e(30.6-5.6(mb-dl)+25(mb-db)))/(1 + e(30.6 5.6(mb-dl)+25(mb db))). A similar model for tooth 46 was also created, as well as a model which incorporated both teeth. With respect to the angles, significant differences between DS and normals were found in 3 out of the 6 angles which were measured: the d-mb-dl angle was smaller than in normals, the mb-d-dl angle was higher, and the mb-dl-d angle was smaller. The dl cusp was located closer to the centre of the tooth. The change in size occurs at an early stage, while the change in shape occurs in a later stage of tooth formation in the DS population. (+info)## Organizational and environmental factors associated with nursing home participation in managed care. (8/24374)

OBJECTIVE: To develop and test a model, based on resource dependence theory, that identifies the organizational and environmental characteristics associated with nursing home participation in managed care. DATA SOURCES AND STUDY SETTING: Data for statistical analysis derived from a survey of Directors of Nursing in a sample of nursing homes in eight states (n = 308). These data were merged with data from the On-line Survey Certification and Reporting System, the Medicare Managed Care State/County Data File, and the 1995 Area Resource File. STUDY DESIGN: Since the dependent variable is dichotomous, the logistic procedure was used to fit the regression. The analysis was weighted using SUDAAN. FINDINGS: Participation in a provider network, higher proportions of resident care covered by Medicare, providing IV therapy, greater availability of RNs and physical therapists, and Medicare HMO market penetration are associated with a greater likelihood of having a managed care contract. CONCLUSION: As more Medicare recipients enroll in HMOs, nursing home involvement in managed care is likely to increase. Interorganizational linkages enhance the likelihood of managed care participation. Nursing homes interested in managed care should consider upgrading staffing and providing at least some subacute services. (+info)###### Some categories never predicted in ordinal logistic regression model - Cross Validated

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###### Archive ouverte HAL - **MODEL** SELECTION IN **LOGISTIC** REGRESSION

###### Chapter 2: The Multiple **Logistic** Regression **Model** - Applied **Logistic** Regression, 3rd Edition [Book]

###### SAS Training in Switzerland -- Predictive **Modeling** Using **Logistic** Regression

###### SAS Training in Magyarország -- Predictive **Modeling** Using **Logistic** Regression

**Logistic** **modeling** - help integrating/solving for P | Physics Forums

###### Worked example: **Logistic** **model** word problem (video) | Khan Academy

###### The Performance of Robust Methods in **Logistic** Regression **Model**

###### SAP **Model** Company for **Logistics** Execution

###### Production Forecasting with **Logistic** Growth **Models** - OnePetro

###### 7. The **Logistic** Regression **Model** - R Statistical Application Development by Example Beginner's Guide [Book]

###### Axioms | Free Full-Text | Applications of Skew **Models** Using Generalized **Logistic** Distribution

**Logistics** technology: WeWork debacle highlights unhealthy VC investment **models**

###### Standard **Model** of **Logistics** Information System | United Nations ESCAP

###### R] **logistic** **model** diagnostics residuals.lrm {design}, residuals()

**Logistic** **Model** Trees - Best Student Paper - VideoLectures.NET

###### Introduction to Linear and **Logistic** Regression **Models** | Bristol Medical School | University of Bristol

Applied Logistic RegressionBased on the logistic regressionRegressionsNonlinearDiscretePredictiveMultiple regressionBinomialMultivariablePROC LOGISTICStatisticalGrowthCoefficientsLogitCitations2001Neural networksMaximum likelihoodStochasticStepwise2018EstimationEstimatePredictionDifferentialDataMethodsClassificationParameterCategoricalFrameworkPapersSimulationParameters2017GaussianGeneralized logistic disDichotomousLinearConvergenceProcedureDemonstrateCalibration2000MathematicalBusinessAlgorithmGeographic InformaComparisonsAnalyticalThird-party logParametricBinary responseThesisWeights

###### Applied Logistic Regression1

- Hosmer DW, Lemeshow S (2000) Applied logistic regression. (springer.com)

###### Based on the logistic regression2

- Hi, I m trying to build a credit risk model (an application scorecard) based on the logistic regression for my master thesis. (mathhelpforum.com)
- Odds ratios based on the logistic-regression results were calculated for these variables. (aappublications.org)

###### Regressions3

- In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. (coursera.org)
- Optimal response modeling is studied using logistic regression, random forests, and I* algorithm of building tuned regressions. (ssrn.com)
- In the framework of conditional density estimation, we use candidates taking the form of mixtures of Gaussian regressions with logistic weights and means depending on the covariate. (inria.fr)

###### Nonlinear7

- Identifiability of nonlinear logistic test models. (springer.com)
- It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. (springer.com)
- Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity ," Journal of Applied Econometrics , John Wiley & Sons, Ltd., vol. 20(1), pages 39-54. (repec.org)
- Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity ," CeMMAP working papers CWP18/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. (repec.org)
- We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. (mdpi.com)
- The aim of this work was to improve the forecasting performance of business failure prediction with all sample sizes by constructing a novel nonlinear integrated forecasting model (ANIFM) of individual linear forecasting models and individual nonlinear forecasting models. (astm.org)
- We considered logistic regression (LR) as the individual linear forecasting method to deal with each linear variable, the support vector machine (SVM) as the individual nonlinear forecasting method to deal with each nonlinear variable, and the residual SVM as the integration method to integrate the forecasts of LRs and SVMs. (astm.org)

###### Discrete6

- K. Matsuya and M. Kanai, Exact solution of a delay difference equation modeling traffic flow and their ultra-discrete limit , https://arxiv.org/abs/1509.07861 . (hindawi.com)
- R. Willox, "Modelling natural phenomena with discrete and ultradiscrete systems," in Proceedings of the RIAM Symposium Held at Chikushi Campus, Kyushu Universiy 22AO-S8 , pp. 13-22. (hindawi.com)
- An additional modeling consideration, which is introduced in this chapter, is using design variables for modeling discrete, nominal scale, independent variables. (oreilly.com)
- Panel Data Discrete Choice Models with Lagged Dependent Variables ," Econometrica , Econometric Society, vol. 68(4), pages 839-874, July. (repec.org)
- Traditional predictions of radioactive waste transport using discrete fracture network (DFN) models often consider one particular realization of the fracture distribution based on fracture statistic features. (environmental-expert.com)
- With a successful model in hand, and continuing focus on the total landed cost, you can put on your tactical hat, and fuss over discrete tactical functions. (inboundlogistics.com)

###### Predictive6

- Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. (coursera.org)
- This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. (sas.com)
- This data shows tuning logistic regression using random forest variable importance results in an optimal predictive model even with data without interaction effects. (ssrn.com)
- Multivariate logistic regression is a statistical method commonly used in several fields to build predictive models. (umn.edu)
- Logistic regression has been applied in many machine learning applications to build building predictive models. (igi-global.com)
- The Hosmer-Lemeshow test revealed a good fit for the model, and the Nagelkerke R 2 effect size demonstrated good predictive efficacy. (aappublications.org)

###### Multiple regression3

- EDITOR,-The application of multiple regression models in medical research has greatly increased during the past years. (bmj.com)
- You'll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. (coursera.org)
- Professor Harrell has produced a book that offers many new and imaginative insights into multiple regression, logistic regression and survival analysis, topics that form the core of much of the statistical analysis carried out in a variety of disciplines, particularly in medicine. (springer.com)

###### Binomial5

- proposed a natural class of robust estimator and testing procedures for binomial models and Poisson models, which are based on a concept of quasi-likelihood estimator proposed by . (scirp.org)
- Logistic regression is based on binary (binomial distribution) data, not continuous data. (ethz.ch)
- 1989). Haseman and Soares (1976) concluded that, when analyzing experiments that look at dichotomous fetal responses, binomial or Poisson models provide poor fits, as there is similarity between responses from the same litter (Kupper et al. (jyi.org)
- The beta-binomial model, considered by Williams (1975), is commonly used to account for littermate correlation when analyzing dose response data (Kupper et al. (jyi.org)
- Multinomial and binomial logistic regression models are used, and different versions of the models are compared and assessed with cross validation. (lu.se)

###### Multivariable3

- In this chapter, we generalize the model to one with more than one independent variable (i.e., the multivariable or multiple logistic regression model). (oreilly.com)
- To provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research. (bristol.ac.uk)
- Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. (springer.com)

###### PROC LOGISTIC3

- Presumably you are simulating the data so that you can call PROC LOGISTIC and obtain parameter estimates and other statistics for the simulated data. (sas.com)
- So, optionally, Step 5 is to write the data to a SAS data set so that PROC LOGISTIC can read it. (sas.com)
- In this example, 150 observations were generated so that you can run PROC LOGISTIC against the simulated data and see that the parameter estimates are close to the parameter values. (sas.com)

###### Statistical8

- Have experience building statistical models using SAS software. (sas.com)
- Revision of basic methods in a statistical modelling framework. (bristol.ac.uk)
- Statistical models based on the scale-invariance (or scaling) concept has increasingly become an essential tool for modeling extreme rainfall processes over a wide range of time scales. (easychair.org)
- Results of this assessment based on different statistical criteria have indicated the comparable performance of the proposed scaling GLO model as compared to other popular models in practice. (easychair.org)
- Different statistical models have been created to include litter effect, with many undergoing constant improvement (Yamamoto and Yanagimoto, 1994). (jyi.org)
- An often overlooked problem in building statistical models is that of endogeneity, a term arising from econometric analysis, in which the value of one independent variable is dependent on the value of other predictor variables. (cdc.gov)
- To create a statistical model to predict LM versus AM in children based on history, physical, and laboratory findings during the initial presentation of meningitis. (aappublications.org)
- A total of 175 children with meningitis were included in the final statistical model. (aappublications.org)

###### Growth19

- This is a model of a logistic growth curve using the System Dynamics Modeler. (northwestern.edu)
- NetLogo Logistic Growth model. (northwestern.edu)
- Two extensions of stochastic logistic model for fish growth have been examined. (scirp.org)
- The basic features of a logistic growth rate are deeply influenced by the carrying capacity of the system and the changes are periodical with time. (scirp.org)
- We will look at a logistic model to predict whether a school met the target for school-wide growth in the API score. (coursera.org)
- This paper will propose a new empirical model for production forecasting in extremely low permeability oil and gas reservoirs based on logistic growth models. (onepetro.org)
- The logistic growth model does not extrapolate to non-physical values. (onepetro.org)
- Venture capital firms are pouring an unprecedented amount of funding into logistics-focused startups, but a focus on rapid revenue growth over sustainable profitability may not be leading these companies away from products that would provide the most value to shippers. (joc.com)
- A sufficient condition is established for globally asymptotic stability of the positive equilibrium of a regulated logistic growth model with a delay in the state feedback. (aimsciences.org)
- Global stability in a regulated logistic growth model. (aimsciences.org)
- Two-dimensional stability analysis in a HIV model with quadratic logistic growth term. (aimsciences.org)
- There are two main parameters, $N$, the total number of virions produced by one infected cell, and $r$, the logistic parameter which controls the growth rate. (aimsciences.org)
- More customers are using logistics to gain a competitive advantage, opening up opportunities for providers that choose the best path to growth. (bain.com)
- This strategic shift opens up significant growth opportunities for logistics providers, with winners using different paths and business models to foster growth. (bain.com)
- is, in contrast to the model without cellular growth, no longer critical to the global existence or collapse of this system. (aimsciences.org)
- Last month's Reliability Basics article introduced success/failure data in the context of developmental testing reliability growth analysis and listed the models that can be used to analyze such data. (weibull.com)
- In this month's article, we continue the discussion by highlighting the logistic model, available in RGA , and show its application to model success/failure data as well as another type of developmental growth data that consists of reliability values at different times or stages. (weibull.com)
- b ≤ 1 then Ti ≤ 0 and the logistic reliability growth model will not be described by an S-shaped curve. (weibull.com)
- This article explained a process for analyzing failure/success and reliability data from developmental reliability growth tests using the logistic growth model. (weibull.com)

###### Coefficients4

- And then we will see how to test whether subset of coefficients is zero, the same way we did in the linear model. (coursera.org)
- Central to the consideration of the multiple logistic models is estimating the coefficients and testing for their significance. (oreilly.com)
- Interpretation of model coefficients as differences in means or odds ratios. (bristol.ac.uk)
- The analysis of the various coefficients was done across all models. (umn.edu)

###### Logit3

- The script works after arbitrary logit or logistic commands. (umn.edu)
- Transform the linear predictor by the logistic (inverse logit) function. (sas.com)
- A logistic model formulates the model in terms of the log odds ratio (the logit) of the probability of the outcome of interest as a function of the explanatory variables. (analyse-it.com)

###### Citations1

- If you mention this model or the NetLogo software in a publication, we ask that you include the citations below. (northwestern.edu)

###### 20012

- We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. (archives-ouvertes.fr)
- Everitt B., Rabe-Hesketh S. (2001) Generalized Linear Models I: Logistic Regression. (springer.com)

###### Neural networks5

- In this study, a model of drivers' behavior during a severe braking is created using both neural networks and logistic regression methods to determine the BAS threshold activation. (sae.org)
- Samples of brake pedal speed, Brake pedal displacement, and vehicle acceleration measured from panic and normal situations, will be fed for training neural networks and acquiring logistic regression equation. (sae.org)
- Solaymani Roody, S., "Modeling Drivers' Behavior During Panic Braking for Brake Assist Application, Using Neural Networks and Logistic Regression and a Comparison," SAE Technical Paper 2011-01-2384, 2011, https://doi.org/10.4271/2011-01-2384 . (sae.org)
- The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. (usda.gov)
- For performance comparison, single LR, SVM methods, integration forecasting models based on equal weights and on neural networks, and one based on rough set and Dempster-Shafer evidence theory (D-S theory) were also included in the empirical experiment as benchmarks. (astm.org)

###### Maximum likelihood5

- The maximum likelihood estimator is a common technique of parameter estimation in the binary regression model. (scirp.org)
- studied the breakdown of the maximum likelihood estimator in the logistic model. (scirp.org)
- In this article we investigate the use of weight functions introduced by as a weight function for Mallows type (weighted maximum likelihood estimator) to obtain a robust estimation for logistic regression, in addition, to compare their performance with classical maximum likelihood estimator and some existing robust methods by means of simulation study and real data sets. (scirp.org)
- The maximum likelihood estimator for the logistic regression model is given in Section 2. (scirp.org)
- The asymptotic normality of maximum likelihood estimators (MLEs) is obtained even though the support of this non-regular regression model depends on unknown parameters. (umsystem.edu)

###### Stochastic2

- M. A. Shah and U. Sharma, "Optimal Harvesting Policies for a Generalized Gordon-Schaefer Model in Randomly Varying Environment," Applied Stochastic Models in Business and Industry, John Wiley & Sons, Ltd., 2002. (scirp.org)
- In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. (mdpi.com)

###### Stepwise2

- This video reviews the variables to be used in stepwise selection logistic regression modeling in this demonstration. (lynda.com)
- The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences. (cdc.gov)

###### 20181

- inproceedings{HIC2018:Scale_Invariance_Generalized_Logistic_GLO, author = {Truong-Huy Nguyen and Van-Thanh-Van Nguyen}, title = {Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change}, booktitle = {HIC 2018. (easychair.org)

###### Estimation4

- Practical guidelines for the estimation and inference of a dynamic logistic model with fixed-effects ," Economics Letters , Elsevier, vol. 115(2), pages 300-304. (repec.org)
- Practical Guidelines for the Estimation and Inference of a Dynamic Logistic Model with Fixed-Effects ," Working Papers 2011-08, Center for Research in Economics and Statistics. (repec.org)
- After model estimation, marginal effects for each of the models were obtained. (umn.edu)
- This study compared the small-sample performance of an optimized Bayesian hierarchical 2PL (H2PL) model to its standard inverse Wishart specification, its nonhierarchical counterpart, and both unweighted and weighted least squares estimators (ULSMV and WLSMV) in terms of sampling efficiency and accuracy of estimation of the item parameters and their variance components. (uio.no)

###### Estimate8

- See here, I put the estimate for diabetes and next to it … I put the confidence interval and since we just ran … this model in our 600 code we know that diabflag … is statistically significant, here are the numbers. (lynda.com)
- Module 2 covers how to estimate linear and logistic model parameters using survey data. (coursera.org)
- You'll also see how logistic regression will allow you to estimate probabilities of success. (coursera.org)
- The new model incorporates known physical volumetric quantities of oil and gas into the forecast to constrain the reserve estimate to a reasonable quantity. (onepetro.org)
- estimate such a model? (ethz.ch)
- The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response, and the second when models are compared between groups which have different distributions of other causes of the binary response. (lse.ac.uk)
- The model can be used to estimate when the reliability goal of 99% will be achieved if testing and improvements continue. (weibull.com)
- The model was used to estimate the reliability throughout the test and estimate additional trials needed to demonstrate a certain reliability goal. (weibull.com)

###### Prediction6

- Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. (psu.edu)
- However, logistic training regularly requires a long time to adapt an accurate prediction model. (igi-global.com)
- No large studies have compared patients with LM to all patients presenting with AM and attempted to define a clinical prediction model. (aappublications.org)
- The final model was transformed into a clinical prediction model that allows practitioners to calculate the probability of a child having LM. (aappublications.org)
- The clinical prediction model can help guide the clinician about the need for parenteral antibiotics while awaiting serology results. (aappublications.org)
- The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. (nih.gov)

###### Differential7

- Narrator] The population P of T of bacteria in a petry dish satisfies the logistic differential equation. (khanacademy.org)
- Let's just remind ourselves what we're talking about or what they're talking about with the logistic differential equation and the carrying capacity. (khanacademy.org)
- So in general a logistic differential equation is one where we seeing the rate of change of, and it's often referring to population, so let's just stick with population. (khanacademy.org)
- And so let me just draw a little graph here to show the typical solution to a logistic differential equation. (khanacademy.org)
- one way is we can actually put our logistic differential equation in this form and then we can recognize what the carrying capacity is. (khanacademy.org)
- As T approaches infinity, this thing approaches zero and so we can think from this logistic differential equation what P values would make this thing be zero based on this differential equation or when this thing approaches zero, what P values would this approach. (khanacademy.org)
- T. Cieslak and C. Stinner , New critical exponents in a fully parabolic quasilinear Keller-Segel system and applications to volume filling models, J. Differential Equations , 258 (2015), 2080-2113. (aimsciences.org)

###### Data31

- We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. (psu.edu)
- To illustrate this phenomenon, we analyze a real data set using a Lagrange multiplier test for the specification of the model. (springer.com)
- We also cover the features of survey data sets that need to be accounted for when estimating standard errors of estimated model parameters. (coursera.org)
- In this video we will illustrate how to fit a logistic model in R. So I'm going to use the same data set that we've seen before, this academic performance data set that comes bundled with the R survey package. (coursera.org)
- 1 Nevertheless, assessing the accuracy of regression models in describing the data (goodness of fit) is almost unknown in medical research. (bmj.com)
- This module explores regression models, which allow you to start with data and discover an underlying process. (coursera.org)
- This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. (sas.com)
- Logistic regression is the most important tool for data analysis in various fields. (scirp.org)
- Logistic regression is a proper analysis method to model the data and explain the relationship between the binary response variable and explanatory variables. (scirp.org)
- The new model is easy to use, and it is very capable of trending existing production data and providing reasonable forecasts of future production. (onepetro.org)
- His research interests lie in the areas of epidemic logistics, data analysis and medical operations management. (springer.com)
- Working through the case studies in the book will demonstrate what can be achieved with a little imagination, when modelling complex and challenging data sets. (springer.com)
- In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. (uni-muenchen.de)
- To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. (springer.com)
- Using the UML modelling tools such as component and class diagrams, the model addresses the highest level quality business processes and data structure as well as all identified providing and depending interfaces and the messaging system in a module-based framework design. (diva-portal.org)
- The methodology and adopted procedures are explained in details of which provide a better understanding of the modelling and the possibility for the lower-levels quality data extensions following the same framework. (diva-portal.org)
- The model is able to manipulate all quality control data for purchasing, production and remedy operation in a lot-based make-to-order production system within a defined module. (diva-portal.org)
- The feasibility and accuracy of this model were assessed using ER data from a network of 21 raingages located in Ontario, Canada. (easychair.org)
- As Bieler and Williams (1975) state, littermate correlation can have a huge impact on how toxicology data is modeled and analyzed. (jyi.org)
- From generating consumer insights to understanding the product flows comprising driver behaviour, shortest routes, and other valuable information, big data has resulted in tremendous cost saves and optimized delivery models. (prnewswire.com)
- This paper describes the socioeconomic determinants of primary school dropout in Uganda with the aid of a logistic model analysis using the 2004 National Service Delivery Survey data. (umn.edu)
- Logistics looks like a very complex area, but for a Data Modeller, it seems to have just a few Entities. (databaseanswers.org)
- In my book Simulating Data with SAS , I show how to use the SAS DATA step to simulate data from a logistic regression model. (sas.com)
- Recently there have been discussions on the SAS/IML Support Community about simulating logistic data by using the SAS/IML language. (sas.com)
- This article describes how to efficiently simulate logistic data in SAS/IML, and is based on the DATA step example in my book. (sas.com)
- The SAS/IML statements for simulating logistic data are concise. (sas.com)
- In addition to this, an approach based upon a hierarchical process model is shown that supports acquisition of input data necessary for configuring a logistics process and setting its parameters. (fraunhofer.de)
- Sales Datasheet for the Teradata Transportation and Logistics Data Model (TLDM), which is a licensed customer offering which provides the map showing the pieces of information required to support Use Cases that challenge your business. (teradata.com)
- The TLDM models the enterprise business data, data relationships, business rules governing these data relationships, and Transportation and Logistics industry-specific topic areas. (teradata.com)
- Reliability data can be analyzed with various models, such as Lloyd Lipow (which was covered in the Reliability Basics article in Issue 52), Gompertz, modified Gompertz and the logistic model (which is the subject of this article). (weibull.com)
- The above data set is entered in RGA (using the Reliability data type) and the model parameters are calculated, as shown next. (weibull.com)

###### Methods4

- We compare the performance of our algorithm against that of decision trees and logistic regression on 32 benchmark UCI datasets, and show that it achieves a higher classification accuracy on average than the other two methods. (psu.edu)
- Ahmed, I. and Cheng, W. (2020) The Performance of Robust Methods in Logistic Regression Model. (scirp.org)
- Many different types of models and methods are discussed. (springer.com)
- Models Methods Appl. (aimsciences.org)

###### Classification2

- In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. (psu.edu)
- In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. (wikipedia.org)

###### Parameter6

- model evaluates the four-parameter logistic function and its gradient. (psu.edu)
- For small samples, there is a lot of uncertainty in the parameter estimates for a logistic regression. (sas.com)
- Under the BBL model, expected responses follow a logistic function which can be made equal to that of the Four Parameter Logistic (4PL) model. (umsystem.edu)
- OPLM: One Parameter Logistic Model. (springer.com)
- For instance, [Formula: see text] respondents are typically recommended for the two-parameter logistic (2PL) model. (uio.no)
- To alleviate shortcomings of hierarchical models, the optimized H2PL (a) was reparametrized to simplify the sampling process, (b) a strategy was used to separate item parameter covariances and their variance components, and (c) the variance components were given Cauchy and exponential hyperprior distributions. (uio.no)

###### Categorical2

- Transforming categorical variables into either WOE (weight of evidence) or probability of response coupled with equal frequency binning of size 10 results in improved models. (ssrn.com)
- In this chapter, we introduce generalized linear models , which include the regression and ANOVA models of previous chapters, but can also be used for modeling non-normally distributed response variables, in particular categorical variables. (springer.com)

###### Framework4

- A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. (springer.com)
- The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley. (springer.com)
- This paper deepens the understanding regarding the practical usability of CM models for purchasing decisions, and provides a framework for determining a desired complexity of cost management in different purchasing environments. (diva-portal.org)
- In their study, the authors consider the high computation capabilities of GPU and easy development onto Open Computing Language (OpenCL) framework to execute logistic training process. (igi-global.com)

###### Papers3

- Hence, medical journals may be publishing papers in which regression models are misused or results are misinterpreted. (bmj.com)
- We investigated the use of logistic regression in papers published in the BMJ, JAMA, the Lancet, and the New England Journal of Medicine during 1991-4. (bmj.com)
- The previous papers were considering the problem as a single source logistic network problem while in real world the people face a multi source logistic network problem. (techrepublic.com)

###### Simulation4

- City logistics long-term planning: simulation of shopping mobility and goods restocking and related support systems Agostino Nuzzolo, Antonio Comi and Luca Rosati 8. (bookdepository.com)
- From small businesses to multinational companies, organizations apply simulation models to analyze logistics networks, reduce costs and improve customer service. (eventbrite.com)
- At this seminar in Munich, we will showcase our flagship products for managing logistics challenges - AnyLogic simulation platform and anyLogistix supply chain simulation and analytics software - along with their adoption case studies. (eventbrite.com)
- Our European partners will also demo, with hands-on examples, how they leverage simulation to model and optimize complex logistics systems. (eventbrite.com)

###### Parameters2

- Its parameters can be related to covariates such as dose and gender through a regression model. (umsystem.edu)
- The Ballooned Beta-logistic (BBL) model expands the response boundaries from (0,1) to (L,U), where L and U are unknown parameters. (umsystem.edu)

###### 20171

###### Gaussian2

- If the Gaussian or other model is more consistent, the dots should deviate from the diagonal. (talkchess.com)
- Gaussian model seems ruled out, and Logistic ELO model for computer chess engines seems to stand well on this try. (talkchess.com)

###### Generalized logistic dis4

- These models make use of normal, student- t and generalized logistic distribution, see Rathie and Swamee [Technical Research Report No. 07/2006. (mdpi.com)
- Olinto, G. Applications of Skew Models Using Generalized Logistic Distribution. (mdpi.com)
- Rathie PN, Silva P, Olinto G. Applications of Skew Models Using Generalized Logistic Distribution. (mdpi.com)
- Furthermore, the Generalized Logistic distribution (GLO) has been recommended in UK for modeling of extreme hydrologic variables. (easychair.org)

###### Dichotomous1

- In this chapter we will consider regression models when the regressand is dichotomous or binary in nature. (oreilly.com)

###### Linear14

- For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves. (psu.edu)
- Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). (wikipedia.org)
- and how to present and interpret your linear and logistic regression models. (lynda.com)
- This paper is about the Linear Logistic Test Model (LLTM). (springer.com)
- The linear logistic test model. (springer.com)
- This is the same function we used for the linear model. (coursera.org)
- As in the case of linear regression, the strength of the logistic regression model is its ability to handle many variables, some of which may be on different measurement scales. (oreilly.com)
- Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. (sas.com)
- introduced a fast algorithm based on breakdown points of the trimmed likelihood for the generalized linear model. (scirp.org)
- generalized optimally bounded score functions studied by for linear models to the logistic model. (scirp.org)
- In the previous chapter we considered the linear regression model where the regressand was assumed to be continuous along with the assumption of normality for the error distribution. (oreilly.com)
- Common features of linear and logistic regression models. (bristol.ac.uk)
- The linear model does not contain an error term, as would be the case for linear regression. (sas.com)
- The engines were distanced between themselves by an order of 200 ELO points each, so that each individual ELO interval between them is almost linear in ELO-score and independent of the ELO model. (talkchess.com)

###### Convergence1

- Convergence of global and bounded solutions of a two-species chemotaxis model with a logistic source. (aimsciences.org)

###### Procedure2

- Please find Alko Online Store logistic model and Listing procedure and retail sale of alcoholic beverages 1st May 2016 document in attachments below. (alko.fi)
- The SAS documentation for the LOGISTIC procedure includes a brief discussion of the mathematics of logistic regression . (sas.com)

###### Demonstrate2

- We demonstrate that there are infinitely many equivalent ways to specify a model. (springer.com)
- Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. (mdpi.com)

###### Calibration1

- Accurate item calibration in models of item response theory (IRT) requires rather large samples. (uio.no)

###### 20001

- This paper shows how to simply compute one of the estimators proposed by Honoré and Kyriazidou (2000), as well as its variance, through a reshaping of the original dataset that is then used in a weighted logistic regression with clustering. (repec.org)

###### Mathematical1

###### Business24

- The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. (coursera.org)
- Through a series of short lectures, demonstrations, and assignments, you'll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. (coursera.org)
- Many companies now outsource all or part of their supply chain to logistics specialists when it's not a core business. (bain.com)
- The three services are built on different business models. (bain.com)
- Even so, contract logistics remains a local and fragmented business. (bain.com)
- The Future of Logistics: What Technology-Enabled New Business Models Will Transform the Supply Chain By 2025? (prnewswire.com)
- Key new business models relate to app-based and market place models, and online brokerage services that provide aggregated end-to-end visibility across the supply chain. (prnewswire.com)
- The logistics industry is undergoing a significant business transformation, comparable to when Expedia revolutionized the travel industry with online platforms and achieved colossal improvements in the quoting and procurement of services. (prnewswire.com)
- New logistics players are now implementing similar business models, providing the much-needed price transparency and seamless service for the freight supply chain. (prnewswire.com)
- also spot how they are being used for last-mile connectivity and enabling new agile business models in the supply chain. (prnewswire.com)
- In the UK, here's a consulting organisation which indicates the complexity of Logistics Projects in real world - The Logistics Business . (databaseanswers.org)
- The aim of this paper consists in presenting a new approach in studying the evolution of the business models of the categories of business actors belonging to a business Ecosystem focused on co-modal logistics networks. (inria.fr)
- The focus of the business models is on the key elements that characterize the value-creation links between the involved actors in the Value Chain. (inria.fr)
- The paper focuses on broadening and exploring the traditional concepts of business models showing their evolution when considering the future business environment. (inria.fr)
- To perform this analysis, a template, recalling the well-known approach by Osterwalder, is proposed, discussed and applied to systematically consider alternative approaches to value creation in terms of reference business models. (inria.fr)
- Globalization, complexity, costs and ecommerce are creating key challenges and forging business-to-business and direct-to-consumer models into a single omni-channel. (technicolor.com)
- To hear more about the market forces, challenges, new business models and Technicolor's logistics and supply chain outlook, listen to the full interview with Sullivan in the podcast. (technicolor.com)
- On June 17th, during a series of discussions about E-Commerce, Consumer Delivery, and the New Logistics Economy, Phil Guindi, VP of Product at Shipwire, will join CEOs and GMs from Uber, LoadDelivered, Transfix, and FLEXE on a panel to address New Innovative Business Models Challenging the Logistics Norm. (shipwire.com)
- To be successful in today's active business competition, enterprises need to design and build a productive and flexible logistics network. (techrepublic.com)
- It can be argued that beside product-, production-, or market-oriented companies, there are also flow-oriented companies, in which the business models are based on superior logistics performance. (diva-portal.org)
- The purpose of this study is to explore the characteristics of logistics-based competition, i.e. how a logistics-based business model is designed. (diva-portal.org)
- Findings - Logistics-related characteristics of the three business model components - external environment, internal factors and offering - are elaborated. (diva-portal.org)
- The strategic role of logistics is described through a business model approach. (diva-portal.org)
- In this report business principles for the liquid logistics shipping concept are elaborated upon and a rationale on how this business model can be rolled out further is provided. (globalccsinstitute.com)

###### Algorithm2

- The I* algorithm is enhanced using a 0way interaction option to tune logistic regression without interaction effects. (ssrn.com)
- This paper, tried to find the minimum cost of fMLN using Imperialist Competitive Algorithm (ICA) with considering a multi source flexible multistage logistics network. (techrepublic.com)

###### Geographic Informa1

###### Comparisons1

- We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). (nih.gov)

###### Analytical1

- know the basis on which analytical strategy and model choice is made, and how the results should be interpreted. (bristol.ac.uk)

###### Third-party log2

- To help companies navigate these changes, third-party logistics and supply chain management providers like Technicolor are evolving, and information and the management of that information are core to their services. (technicolor.com)
- Moreover, not all links within most global supply chains are digital, says Matt Castle, vice president of global forwarding products and services with C.H. Robinson, a provider of multimodal transportation services and third-party logistics based in Eden Prairie, Minnesota. (inboundlogistics.com)

###### Parametric2

- The book covers, very completely, the nuances of regression modeling with particular emphasis on binary and ordinal logistic regression and parametric and nonparametric survival analysis. (springer.com)
- Several logistics models have been developed for use in parametric studies, contingency planning, and management of transportation networks. (unt.edu)

###### Binary response1

- Fit a model to a binary response variable. (analyse-it.com)

###### Thesis1

- The thesis propose an empirically supported model for studying positions or for positioning of TPL providers, based on both served markets and internal industry variables. (diva-portal.org)

###### Weights1

- Would it be correct to use weighting by the variable of weights and to use logistic regression afterwards? (mathhelpforum.com)