scholarly journals Analysis of Traffic Accidents Caused by Drivers by Using Log-Linear Models

1970 ◽  
Vol 24 (6) ◽  
pp. 495-504 ◽  
Author(s):  
Hülya Olmuş ◽  
Semra Erbaş
Keyword(s):  
Odds Ratio ◽  
Relative Frequency ◽  
Traffic Accidents ◽  
Linear Models ◽  
Linear Modelling ◽  
Accident Severity ◽  
Modelling Effort ◽  
Log Linear ◽  
Best Fit ◽  
Multiple Interactions

Log-linear modelling is advanced as a procedure to identify factors that underlie the relative frequency of occurrence of various characteristics. The purpose of this study is to present a modelling effort using log-linear models to estimate the relationships between driver’s fault and carelessness and the traffic variables such as gender, accident severity, and accident time. The study was conducted in four different districts in Ankara, the capital of Turkey. There were 1,325 people selected for the study; and they were asked whether they had been in an accident. Four hundred and forty-eight of them answered that they had been involved in an accident. As drivers, 276 out of 448 people, namely 61.6%, had traffic accidents. The data on the variables, namely gender, driver’s fault and carelessness, accident severity and accident time, were collected through a questionnaire survey. Detailed information has been created based on this information. The analysis showed that the best-fit model regarding these variables was the log-linear model. Furthermore, the odds ratio between these variables, the associations of the factors with the accident severity and the contributions of various factors, and the multiple interactions between these variables were assessed. The obtained results provide valuable information in regard to preventing undesired consequences of traffic accidents.

Relationship between plasma osmolality and plasma vasopressin in human subjects

1980 ◽  
Vol 238 (4) ◽  
pp. E313-E317 ◽  
Author(s):  
M. Hammer ◽  
J. Ladefoged ◽  
K. Olgaard
Keyword(s):  
Linear Models ◽  
Human Subjects ◽  
Plasma Osmolality ◽  
Metabolic Clearance ◽  
Parabolic Model ◽  
Pronounced Increase ◽  
Plasma Vasopressin ◽  
Log Linear ◽  
The Relationship ◽  
Best Fit

The relationship between plasma osmolality (pOsm) and plasma vasopressin (pAVP) was studied in 13 human subjects during dehydration. The fit of linear, log-linear, parabolic, and exponential models was tested. For all of the data, the nonlinear models had the best fit. However, when individual differences in either gain or threshold were allowed for, the linear models were better than log-linear models. Finally, analyses were made with individual data points. Linear models had the best fit in half of the subjects, whereas for the others the parabolic model gave the best fit. For those subjects investigated in the low range of the osmoregulatory curve, a linear relationship was found, whereas, for those having the most pronounced increase in pOsm, the most significant improvement was found with the parabolic model. This finding indicates that the relationship is not stable during dehydration in the whole range and that hypovolemia probably can influence the secretion rate and/or metabolic clearance rate and thereby the relationship.

Applying Log-Linear Models in Educational Research

1988 ◽  
Vol 32 (1) ◽  
pp. 3-24 ◽  
Author(s):  
John J. Kennedy
Keyword(s):  
Educational Research ◽  
Analysis Of Variance ◽  
Logit Model ◽  
Contingency Table ◽  
Goodness Of Fit ◽  
Qualitative Data ◽  
Linear Models ◽  
Linear Modelling ◽  
Acceptable Model ◽  
Log Linear

This didactic illustrates the Goodman-Kennedy approach to log-linear modelling within the context of two educational research examples. The first example lends itself to a symmetrical analysis. To qualitative data presented within a 2 × 2 × 2 contingency table, general log-linear models are specified and assessed for goodness of fit. Subsequently an acceptable model is identified and interpreted. In a second example, an asymmetrical logit-model analysis is performed on data in a 2 × 2 × 5 table. It is shown that a subset of general models can be fitted to observed data and that resultant component chi-squares can be used to assess logit response in a manner that is analogous to the analysis of variance.

scholarly journals Comparing Log-Linear and Best-Fit Models to Evaluate the Long-Term Persistence of Enteric Markers in Sewage Spiked River Water

2021 ◽  
Vol 3 ◽  
Author(s):  
Yolanda M. Brooks ◽  
Joan B. Rose
Keyword(s):  
Water Quality ◽  
River Water ◽  
Linear Models ◽  
Decay Rates ◽  
Fecal Indicator ◽  
Quality Models ◽  
Linear Pattern ◽  
Log Linear ◽  
Water Quality Models ◽  
Best Fit

Water quality models use log-linear decay to estimate the inactivation of fecal indicator bacteria (FIB). The decay of molecular measurements of FIB does not follow a log-linear pattern. This study examined the factors associated with the persistence of Escherichia coli uidA, enterococci 23S rDNA, and Bacteroides thetataiotaomicron 1,6 alpha mannanase in microcosms containing 10% (vol/vol) sewage spiked river water stored at 4°C for up to 337 days. The study estimated the markers' persistence with log-linear models (LLMs) to the best-fit models, biphasic exponential decay (BI3) and log-logistic (JM2) and compared the estimates from the models. Concentrations of B. thetataiotaomicon decreased to levels below detection after 31 days in storage and were not fit to models. BI3 and JM2 were fit to E. coli and enterococci, respectively. LLMs had larger Bayesian information criterion values than best-fit models, indicating poor fit. LLMs over-estimated the time required for 90% reduction of the indicators (T90) and did not consider dynamic rates of decay. Time in storage and indicator species were associated with the persistence of the markers (p < 0.001). Using the T90 values of the best-fit models, enterococci was the most persistent indicator. Our data supports the use of best fit models with dynamic decay rates in water quality models to evaluate the decay of enteric markers.

scholarly journals Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China

2019 ◽  
Vol 7 (6) ◽  
pp. 165 ◽  
Author(s):  
Xingning Xiao ◽  
Wen Wang ◽  
Xibin Zhang ◽  
Jianmin Zhang ◽  
Ming Liao ◽  
...  
Keyword(s):  
Supply Chain ◽  
Predictive Models ◽  
Linear Models ◽  
Weibull Model ◽  
Bacterial Reduction ◽  
Salmonella Spp ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Log Linear ◽  
Best Fit

The objective of this study was to develop predictive models for describing the inoculated Salmonella reductions on chicken during the scalding process in China. Salmonella reductions on chicken breasts at a 100 s treatment were 1.12 ± 0.07, 1.38 ± 0.01, and 2.17 ± 0.11 log CFU/g at scalding temperatures of 50, 60 and 70 °C, respectively. For chicken wingettes, 0.87 ± 0.02, 0.99 ± 0.14 and 1.11 ± 0.17 log CFU/g reductions were obtained at 50, 60 and 70 °C after the 100 s treatment, respectively. Greater bacterial reductions were observed on chicken breasts than on chicken wingettes (p < 0.05). A logistic (−1.12, 0.06) distribution could describe the bacterial reductions on chicken breasts at 50–60 °C. Weibull, exponential and log-linear models were compared for describing the bacterial reduction on chicken breasts at 70 °C and the Weibull model showed the best fit as indicated by the pseudo-R2, root mean square error (RMSE) and standard error of prediction (SEP) values. For chicken wingettes, a logistic (−0.95, 0.07) distribution could be used to describe the bacterial reduction at 50–70 °C. The developed predictive models could provide parts of the input data for microbial risk assessment of the poultry supply chain in China.

Log-Linear Modelling of Geographical Contingency Tables

1981 ◽  
Vol 13 (12) ◽  
pp. 1539-1551 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Model Selection ◽  
Minimal Model ◽  
Linear Models ◽  
Test Procedure ◽  
Alternative Methods ◽  
Categorical Variables ◽  
Simultaneous Test ◽  
Selection Strategies ◽  
Linear Modelling ◽  
Log Linear

Salient features are outlined of hierarchical log-linear models appropriate to the analysis of associations between categorical variables. Comparisons are made with alternative methods, and the rationale of the Aitkin simultaneous test procedure for minimal model selection is discussed in detail and compared with screening and stepwise selection strategies.

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear
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