CONTENTS -- 1 Introduction to the Logistic Regression Model -- 1.1 Introduction, 1 -- 1.2 Fitting the Logistic Regression Model, 7 -- 1.3 Testing for the Significance of the Coefficients, 11 -- 1.4 Confidence Logistic Regression -- 2.1 Introduction, 31 -- 2.2 The Multiple Logistic Regression Model, 31 -- 2.3 Fitting the Multiple Logistic Regression Model, 33 -- 2.4 Testing for the Significance of the Model, Methods for Logistic Regression Models, 330 -- 8.5 Sample Size Issues When Fitting Logistic Regression
Wiley series in probability and statistics
2nd ed. xii, 375 s.
- Conspectus
- Veřejné zdraví a hygiena
- NML Fields
- management, organizace a řízení zdravotnictví
Contents -- Preface xi -- 1 Introduction to Regression Modeling -- of Survival Data 1 -- 1.1 Introduction Regression Models for Survival Data 67 -- 3.1 Introduction, 67 -- 3.2 Semi-Parametric Regression Models Parametric Regression Models 244 -- 8.1 Introduction, 244 -- 8.2 The Exponential Regression Model, 246 -- 8.3 The Weibull Regression Model, 260 -- 8.4 The Log-Logistic Regression Model, 273 -- 8.5 Other Parametric Regression Models, 283 Exercises, 283 -- 9.
Wiley series in probability and statistics
Second edition xiii, 392 stran : ilustrace, tabulky, grafy ; 24 cm.
- Conspectus
- Kombinatorika. Teorie grafů. Matematická statistika. Operační výzkum. Matematické modelování
- NML Fields
- přírodní vědy
- NML Publication type
- kolektivní monografie
This research proposes an assessment and decision support model to use when a driver should be examined about their propensity for traffic accidents, based on an estimation of the driver's psychological traits. The proposed model was tested on a sample of 305 drivers. Each participant completed four psychological tests: the Barratt Impulsiveness Scale (BIS-11), the Aggressive Driving Behaviour Questionnaire (ADBQ), the Manchester Driver Attitude Questionnaire (DAQ) and the Questionnaire for Self-assessment of Driving Ability. In addition, participants completed an extensive demographic and driving survey. Various fuzzy inference systems were tested and each was defined using the well-known Wang-Mendel method for rule-base definition based on empirical data. For this purpose, a programming code was designed and utilized. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver's propensity for traffic accidents. The best of the considered fuzzy inference systems might be used as a decision support tool in various situations, such as in recruitment procedures for professional drivers. The validity of the proposed fuzzy approach was confirmed as its implementation provided better results than from statistics, in this case multiple regression analysis.
- MeSH
- Aggression MeSH
- Safety MeSH
- Accidents, Traffic * MeSH
- Adult MeSH
- Fuzzy Logic MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Attitude MeSH
- Surveys and Questionnaires MeSH
- Models, Psychological * MeSH
- Regression Analysis MeSH
- Automobile Driving psychology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
The increasing prevalence of autism spectrum disorders (ASD) has led to worldwide interest in factors influencing the age of ASD diagnosis. Parents or caregivers of 237 ASD children (193 boys, 44 girls) diagnosed using the Autism Diagnostic Observation Schedule (ADOS) completed a simple descriptive questionnaire. The data were analyzed using the variable-centered multiple regression analysis and the person-centered classification tree method. We believed that the concurrent use of these two methods could produce robust results. The mean age at diagnosis was 5.8 ± 2.2 years (median 5.3 years). Younger ages for ASD diagnosis were predicted (using multiple regression analysis) by higher scores in the ADOS social domain, higher scores in ADOS restrictive and repetitive behaviors and interest domain, higher maternal education, and the shared household of parents. Using the classification tree method, the subgroup with the lowest mean age at diagnosis were children, in whom the summation of ADOS communication and social domain scores was ≥ 17, and paternal age at the delivery was ≥ 29 years. In contrast, the subgroup with the oldest mean age at diagnosis included children with summed ADOS communication and social domain scores < 17 and maternal education at the elementary school level. The severity of autism and maternal education played a significant role in both types of data analysis focused on age at diagnosis.
- MeSH
- Autistic Disorder * MeSH
- Child MeSH
- Adult MeSH
- Communication MeSH
- Humans MeSH
- Child Development Disorders, Pervasive * MeSH
- Autism Spectrum Disorder * diagnosis epidemiology MeSH
- Child, Preschool MeSH
- Regression Analysis MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Experiments were carried out in the genetically hypertensive obese rats of Koletsky type (SHR/N-cp) and in their lean siblings. Regression analysis was performed when plasma triglycerides was used as a dependent variable and plasma insulin, insulin binding to erythrocytes, basal plasma glucose tolerance data were used as independent variables. Coefficient determination (R2) as well as the tests of hypotheses of regression coefficients being zero were used to indicate which independent variables contributed the least in the explanation of dependent variable. This way we reduced the list of variables to give a simpler regression equation. In the control animals insulinemia was found to be dominant independent variable in all groups except SHR/N-cp obese females where the dominant independent variable was represented by the basal plasma glycaemia. Under the terguride treatment only in SHR/N-cp female rats the dominant independent variable remained the same as in controls. In the other groups the dominant independent variable was different in relation to the control animals. Long lasting terguride treatment normalized hypertriglyceridemia only in SHR/N-cp obese females. Thus the data obtained by multiple regression analysis of parameters of lipide and glycide metabolism show the close relationship to alleviating effect of terguride in hypertriglyceridemia.
- MeSH
- Dopamine Agonists * pharmacology MeSH
- Glucose * metabolism MeSH
- Glucose Tolerance Test MeSH
- Hypertension complications metabolism MeSH
- Insulin blood MeSH
- Rats MeSH
- Lisuride * analogs & derivatives pharmacology MeSH
- Obesity complications metabolism MeSH
- Rats, Inbred SHR MeSH
- Regression Analysis MeSH
- Triglycerides * blood MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Male MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
Wiley series in probability and statistics
1st ed. xxvi, 311 s.
- MeSH
- Medicine MeSH
- Population MeSH
- Regression Analysis MeSH
- Statistics as Topic MeSH
- Health Surveys MeSH
- Conspectus
- Statistika
- NML Fields
- statistika, zdravotnická statistika
- veřejné zdravotnictví
Decision support systems represent very complicated systems offering assistance with the decision making process. Learning the classification rule of a decision support system requires to solve complex statistical task, most commonly by means of classification analysis. However, the regression methodology may be useful in this context as well. This paper has the aim to overview various regression methods, discuss their properties and show examples within clinical decision making.
- MeSH
- Data Interpretation, Statistical MeSH
- Clinical Decision-Making methods MeSH
- Linear Models MeSH
- Logistic Models MeSH
- Least-Squares Analysis MeSH
- Neural Networks, Computer MeSH
- Regression Analysis * MeSH
- Models, Statistical * MeSH
- Statistics as Topic MeSH
- Support Vector Machine MeSH
- Decision Support Systems, Clinical MeSH
