Applied Logistic Regression

Hosmer, David W.; Lemeshow, Stanley; Sturdivant, Rodney X.

doi:10.1002/9781118548387

Cited by 11,443 publications

(10,218 citation statements)

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“…Probability analysis of labors to undertake the inter sector mobility is based on three categories: (1) a new sector of new type of job; (2) a new sector of the same type of job; and (3) the same sector of the same type of job, which is analyzed by using multinomial logistic regression model (Hosmer et al 2013). Factors that affect labors to undertake inter sector mobility include income, working experience, education, number of family members, and job security.…”

Section: Research Methodsologymentioning

confidence: 99%

Inter Sector Labor Mobility in Palembang, Indonesia

Miskiyah¹,

Marwa²,

Sariman³

et al. 2017

EJEF

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This study discusses the inter sector labor mobility in Palembang working in the primary, secondary, and tertiary sectors. In this study, the inter sector labor mobility is divided into three (3) categories: (1) a new sector of new type of job, (2) a new sector of same type of job, and (3) the same sector of same type of job. The data used are the cross section data obtained through field surveys. By using multinomial logistic regression model, the results indicate that the variable income, job experience, education, and the number of family members significantly influence the inter sector labor mobility on category 1 compared to category 3. Meanwhile on category 2 compared to category 3, the variables that significantly influence the mobility are income and job security.

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Section: Research Methodsologymentioning

confidence: 99%

Inter Sector Labor Mobility in Palembang, Indonesia

Miskiyah¹,

Marwa²,

Sariman³

et al. 2017

EJEF

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“…We carried out the chi-square. This test is widely used to assesses the adequacy of a model; the test results indicate the ability of the model to reflect variance and covariance of the data (Byrne, 2013;Hosmer et al 2013). To avoid the bias of "sample size" we carried out other fit indices such as goodness of fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), normed fit index (NFI), relative fit index (RFI), average block VIF (AVIF), average full collinearity VIF (AFVIF), average path coefficient (APC) and average adjusted R-square.…”

Section: Resultsmentioning

confidence: 99%

The Effect of Perceived Corruption on Entrepreneurial Intention: Evidence from Italy

Allini¹,

Ferri²,

Maffei³

et al. 2017

IBR

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This paper aims to examine the entrepreneurial intention of undergraduate students, using a modified version of Ajzen's theory of planned behavior (TPB), considering the perception of corruption. We conducted a questionnaire survey with Italian students. There were a total of 350 student participants. In order to analyze the data collected with the questionnaire, structural equation modeling is provided. Our results indicate that the majority of students have strong entrepreneurial intention but due to the effect of corruption students are dissuaded from engaging in entrepreneurship. Corruption has a negative effect on students' entrepreneurial behavior. This paper provides a new model that helps to understand the students' entrepreneurial intentions considering the corruption perception.

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“…An F-test (also known as Wald test) was performed to evaluate the overall significance of the model. A logistic regression is a regression-based statistical classification model (16) that is able to predict binary outcomes from ≥2 continuous predictor variables, where the binary outcomes indicate which class the observations belong to. The formula of basic logistic regression can be written as:…”

Section: Methodsmentioning

confidence: 99%

Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients

et al. 2016

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Abstract. The present study aims to quantitatively measure adiposity-related image features and to test the feasibility of applying multivariate statistical data analysis-based prediction models to generate a novel clinical marker and predict the benefit of epithelial ovarian cancer (EOC) patients with and without maintenance bevacizumab-based chemotherapy. A dataset involving computed tomography (CT) images acquired from 59 patients diagnosed with advanced EOC was retrospectively collected. Among them, 32 patients received maintenance bevacizumab following primary chemotherapy, while 27 did not. A computer-aided detection scheme was developed to automatically segment visceral and subcutaneous fat areas depicted on CT images of abdominal sections, and 7 adiposity-related image features were computed. Upon combining these features with the measured body mass index, multivariate data analyses were performed using three statistical models (multiple linear, logistic and Cox proportional hazards regressions) to analyze the association between the model-generated prediction results and the treatment outcome, including progression-free survival (PFS) and overall survival (OS) of the patients. The results demonstrated that applying all three prediction models yielded a significant association between the adiposity-related image features and patients' PFS or OS in the group of the patients who received maintenance bevacizumab (P<0.010), while there was no significant difference when these prediction models were applied to predict both PFS and OS in the group of patients that did not receive maintenance bevacizumab. Therefore, the present study demonstrated that the use of a quantitative adiposity-related image feature-based statistical model may generate a novel clinical marker to predict who will benefit among EOC patients receiving maintenance bevacizumab-based chemotherapy.

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Applied Logistic Regression

Cited by 11,443 publications

References 0 publications

Inter Sector Labor Mobility in Palembang, Indonesia

Inter Sector Labor Mobility in Palembang, Indonesia

The Effect of Perceived Corruption on Entrepreneurial Intention: Evidence from Italy

Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients

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