scholarly journals Analysis of the Influencing Factors of College Students’ Willingness to Entrance Examination for Postgraduate Based on Binomial Logistic Regression Model

2020 ◽  
Author(s):  
Yazhen Tan ◽  
Xiyu Zhou ◽  
Junlin Chen ◽  
Qihao Zhuang ◽  
Yuhong Sun
Keyword(s):  
College Students ◽  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Entrance Examination ◽  
Binomial Logistic Regression

Die Flexion der Indefinita jemand und niemand

2021 ◽  
Vol 49 (2) ◽  
pp. 209-243
Author(s):  
Linnéa Weitkamp
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Corpus Study ◽  
Frequent Use ◽  
Current Variation ◽  
Reference Corpus

Abstract This article investigates the inflection of the German indefinite pronouns jemand and niemand in the accusative and dative. The pronouns are used both with inflectional suffix (jemanden/jemandem, niemanden/niemandem) and without (jemand, niemand) and are thus an example of current variation in contemporary German. The grammars take an unusually liberal stance and describe both forms as correct, partially even with preference to the uninflected form. A corpus study which examines conceptually written data of the DeReKo (German reference corpus) and conceptually oral data of the DECOW16B (German web corpus), shows that over 90 % of occurrences are inflected. But almost 10 % of uninflected forms show that these formations are no arbitrary errors either. To find out what influences the presence or absence of the inflectional ending, a binary logistic regression model was calculated. The following factors proved to be significant influencing factors for inflection: the degree of formality (DeReKo vs. DECOW16B), the lexeme (jemand vs. niemand), the case (acc vs. dat), government by preposition vs. government by verb and the following nominalized adjective (jemand anderen). With regard to the different inflectional suffixes, the frequent use of -en in the dative stood out in particular. Although this form is classified as erroneous in all grammars, almost 30 % of the dative occurrences in informal DECOW16B data are formed in this way.

scholarly journals The Association of Demographic Characteristics and Food Choice Motives with the Consumption of Functional Foods in Emerging Adults

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2582 ◽  
Author(s):  
Lieke Vorage ◽  
Nicola Wiseman ◽  
Joana Graca ◽  
Neil Harris
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Emerging Adults ◽  
Food Choice ◽  
Functional Food ◽  
Logistic Regression Model ◽  
Functional Foods ◽  
Demographic Characteristics ◽  
Binomial Logistic Regression ◽  
Food Choice Motives

The functional food market is one of the fastest growing segments of the global food industry. The aims of this study were to understand the association of demographic characteristics and food choice motives (FCMs) with (a) attitudes toward functional foods and (b) consumption of functional foods in Australian emerging adults. Data were collected through a paper-based and online questionnaire completed by 370 young adults aged between 17 and 29 years. A binomial logistic regression was used to determine the association between demographic characteristics and FCMs with attitudes towards functional foods. The logistic regression model was statistically significant at χ2(11) = 48.310 (p < 0.001) and explained 18.1% of the variance in attitude towards functional food. Of the several predictors, only the FCMs natural content and weight control were statistically significant. A binomial logistic regression was also used to determine the association between demographic characteristics and FCMs with the consumption of functional foods. The logistic regression model was statistically significant at χ2(9) = 37.499 (p < 0.001) and explained 14.1% of the variance in functional food consumption. Of the eight predictors, three were statistically significant: living situation, natural content and health. Findings highlight that when targeting emerging adults, functional food companies could benefit from promoting the natural and health properties of their products. Furthermore, consumption can be increased by targeting the parents of emerging adults and by designing functional foods that attract emerging adults interested in controlling weight.

scholarly journals Analysis of Influencing Factors of Community Management of Diabetes based on Adaptive-Lasso Logistic Regression Model

2020 ◽  
Author(s):  
Wei Lin ◽  
Zejuan Huang ◽  
Zhiqing Cao ◽  
Jing Zhou ◽  
Yang Tian ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Blood Glucose ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Diabetes Management ◽  
Adaptive Lasso ◽  
Community Management ◽  
Diabetic Patients

Abstract Background: Influencing factors of community management of diabetes are complex and controversial, and how to select the most effective multiple influencing factors requires in-depth research. This study aims to analyse multiple influencing factors by adaptive-lasso logistic regression model for the effectiveness of diabetes management of the community to improve the efficiency and reduce the burden of diabetes. Methods: A cross-sectional survey (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management of diabetic patients based on cluster sampling data of diabetic patients in Chengdu city, China. By comparing with the full-variable logistic model and the ridge logistic model to find the advantages of the adaptive lasso-logistic regression model in community diabetes management. Results: A total of 1,127 diabetic patients were included in the cross-sectional survey. The latest fasting blood glucose was included in the analysis. Among the included population, 90.6% of them had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the Adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The AIC and BIC criteria of adaptive lasso-logistic regression model were 2062 and 1981, which were lower than the full-variable logistic model (2349, 2023) and the ridge logistic model (2312, 2013). From the perspective of time cost, the adaptive-lasso logistic regression model was better than the other two models. Conclusions: The adaptive-lasso logistic regression model can be used to analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

scholarly journals Incidence and predictive factors of problems after fixation of trochanteric hip fractures with sliding hip screw or intramedullary devices

2020 ◽  
pp. 112070002095933
Author(s):  
Piers R J Page ◽  
Michael H Field ◽  
Niraj Vetharajan ◽  
Adam Smith ◽  
Luke Duggleby ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Hip Fractures ◽  
Logistic Regression Model ◽  
Trochanteric Fractures ◽  
Sliding Hip Screw ◽  
Binomial Logistic Regression ◽  
Hip Screw ◽  
Tip Apex Distance ◽  
Postoperative Problems

Introduction: Hip fractures are common and disabling injuries, usually managed surgically. The most common type outside the joint capsule are trochanteric fractures, usually fixed with either sliding hip screw or intramedullary nail. Data are available in the National Hip Fracture Database (NHFD) on early failure and other major complications, but late or subtler complications may escape recording. This study sought to quantify such problems after fixation performed at 3different sites and identify their predictors. Methods: Patients with a trochanteric fracture treated at 1 of 3 sites were identified from the NHFD over a 3-year period. Any with further, related episodes of care were identified, and reasons recorded, then age- and sex-matched with those with no such episodes. Data was collected on Arbeitsgemeinschaft für Osteosynthesefragen classification, tip-apex distance, American Society of Anesthesiologists (ASA) grade, Abbreviated Mental Test Score and pre-injury mobility. The cohorts were compared, and a binomial logistic regression model used to identify predictors of problems. Results: A total of 4010 patients were entered in the NHFD across 3 sites between January 2013 and December 2015. Of these, 1260 sustained trochanteric fractures and 57 (4.5%) subsequently experienced problems leading to re-presentation. The most common was failure of fixation, occurring in 22 patients (1.7%). The binomial logistic regression model explained 47.6% of the variance in incidence of postoperative problems with ASA grade and tip-apex distance being predictive. Discussion: The incidence of re-presentation with problems was around of 5%. A failure rate of less than 2% was seen, in keeping with existing data. This study has quantified the incidence of subtler postoperative problems and identified their predictors. The type of implant used was not amongst them and patients with both implants experienced problems. Fixation continues to yield imperfect results, but patient health and robust surgical technique remain important factors in a good outcome.

scholarly journals Analysis of Influencing Factors of Community Management of Diabetes Based on Adaptive-Lasso Logistic Regression Model: A Community-based Study

2021 ◽  
Author(s):  
Wei Lin ◽  
Yang Tian ◽  
Adeel Khoja ◽  
Xuan Zhao ◽  
Peng Hu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Blood Glucose ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Information Criterion ◽  
Adaptive Lasso ◽  
Community Management ◽  
Patients With Diabetes

Abstract Background This study aimed to analyse which influencing factors may be more effective to achieve diabetes management targets in the community by the adaptive-lasso logistic regression model. Methods A cross-sectional study (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management based on multi-stage cluster sampling data among patients with diabetes in China. Patient’s fasting blood glucose level, blood pressure, and triglycerides was collected. Results Overall, 90.6% of included people had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The Akaike Information Criterion and Bayesian Information Criterion of adaptive lasso-logistic regression model were 1980 and 2021, which were lower than the full-variable logistic model (2041, 2245) and the ridge logistic model (2043, 2348). The adaptive-lasso logistic regression model was better than the other two models regarding time cost.Conclusions The adaptive-lasso logistic regression model can analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

Sign in / Sign up

Export Citation Format

Share Document