multiple regression
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... 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.
Springer series in statistics
1st ed. 568 s.
... 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.
- Konspekt
- Kombinatorika. Teorie grafů. Matematická statistika. Operační výzkum. Matematické modelování
- NLK Obory
- přírodní vědy
- NLK Publikační typ
- 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
- agrese MeSH
- bezpečnost MeSH
- dopravní nehody * MeSH
- dospělí MeSH
- fuzzy logika MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- postoj MeSH
- průzkumy a dotazníky MeSH
- psychologické modely * MeSH
- regresní analýza MeSH
- řízení motorových vozidel psychologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- MeSH
- dopravní nehody MeSH
- lidé MeSH
- rány a poranění MeSH
- regresní analýza MeSH
- statistika jako téma MeSH
- Check Tag
- lidé 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
- autistická porucha * MeSH
- dítě MeSH
- dospělí MeSH
- komunikace MeSH
- lidé MeSH
- pervazivní vývojové poruchy u dětí * MeSH
- poruchy autistického spektra * diagnóza epidemiologie MeSH
- předškolní dítě MeSH
- regresní analýza MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- předškolní dítě MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Wiley series in probability and statistics
1st ed. xxvi, 311 s.
BACKGROUND: A wide variety of interventions exists in physical therapy (PT), but knowledge about their use across different geographical regions is limited. This study investigated the use of PT interventions in people with multiple sclerosis (MS) across Europe. It aimed to determine whether regions differ in applying interventions, and explore whether factors other than regions play a role in their use. METHODS: In an online cross-sectional survey, 212 respondents from 115 European workplaces providing PT services to people with MS representing 26 countries (four European regions) participated. Cluster analysis, Pearson Chi-squared test and a Poisson regression model were used to analyze the data. RESULTS: Thirteen of 45 listed PT interventions were used by more than 75% of centers, while nine interventions were used by less than 25%. For 12 interventions, regions differed markedly in their use. Cluster analysis of centers identified four clusters similar in their intervention use. Cluster assignment did not fully align with regions. While center region was important, center size, number and gender of physical therapists working in the center, and time since qualification also played a role. Cluster analysis exploring the use of the interventions provided the basis for a categorization of PT interventions in line with their primary focus: 1. Physical activity (fitness/endurance/resistance) training; 2. Neuroproprioceptive "facilitation/inhibition"; 3. Motor/skill acquisition (individualized therapy led); 4. Technology based interventions. CONCLUSIONS: To our knowledge this is the first study that has explored this topic in MS. The results broaden our understanding of the different PT interventions used in MS, as well as the context of their use.
- MeSH
- lidé MeSH
- průřezové studie MeSH
- regresní analýza MeSH
- roztroušená skleróza terapie MeSH
- shluková analýza MeSH
- techniky fyzikální terapie * MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
