Model-Dependent Variance Inflation Factor Cutoff Values

Tujuan dari penelitian ini adalah untuk menguji kualitas pelayanan pengelola wisata bahari daerah desa Suli dengan pendekatan uji statistika. Dengan hipotesis apakah variabel independen responsivenes, reliabilit, assurance, emphaty, tangibles berpengaruh positif (signifikan) terhadap kualitas pelayanan, dan apakah seluruh variabel independen tersebut secara simultan bersama-sama berpengaruh positif terhadap kualitas pelayanan. Saat ini kebutuhan berwisata menjadi kebutuhan penting, sebab dengan berwisata diperoleh pengalaman, informasi, dan pengetahuan baru. Semua itu bisa diperoleh, manakala layanan yang diberikan pengelola sangat berkesan, khususnya wisatwan difabel. Hasil penelitian menunjukkan bahwa uji reliabilitas dengan nilai spearman-brown adalah 0,9352 sehingga masuk kategori sangat tinggi. Uji normalitas menggunakan metode Kolmogorov-Smirnov dan Jarque-Bera memiliki nilai p-value masing-masing adalah 0,779 dan 0,809 > 0,05 maka asumsi data terpenuhi. Uji mutikoliniieritas menunjukkan nilai variance inflation factor memiliki nilai < 10 maka tidak terjadi multikolinieritas. Uji homoskedastisitas terpenuhi dengan nilai p-value (sig) seluruh variabel independen > 0,05. Uji non-autocorrelation menggunakan Durbin-Watson dengan range nilai adalah 1 – 3 yakni 2,36. Uji koefisien determinasi dihasilkan bilai koefisien determinasi sebesar 0,8042 sangat mendekati nilai atau jauh dari nilai 0. Dan pada uji F, nilai p-value memiliki tingkat signifikansi < 0,05, maka seluruh variabel independen secara bersama-sama mempengaruhi variabel dependen.

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Abstract 14101: A Correction Score for Comparison of Aortic Calcification in Post-contrast and Non-contrast Measurements From Computed Tomography Scans

Circulation ◽

10.1161/circ.142.suppl_3.14101 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Steven R Horbal ◽

Edward Brown ◽

Brian A Derstine ◽

Peng Zhang ◽

Andrea H Rossman ◽

...

Keyword(s):

Pearson Correlation ◽

Aortic Calcification ◽

University Of Michigan ◽

Variance Inflation Factor ◽

Post Contrast ◽

Abdominal Aortic ◽

Pixel Value ◽

Inflation Factor ◽

The University ◽

The Relationship

Introduction: Aortic calcification can be utilized to assess cardiovascular risk. While contrast is useful for vascular enhancement in diagnostic imaging, enhancement creates heterogeneity between post and non-contrast scans and limits their direct comparability. Hypothesis: We hypothesized that post and non-contrast aortic calcification measures will correlate, and a correction score can be developed for statistical comparability. Methods: Retrospective CT-scans were obtained from the University of Michigan. Participants (N=330) received abdominal scans with and without contrast enhancement within 120 calendar days. Analytic Morphomics was used to obtain vertebral-indexed measurements of aortic calcium area, and aortic wall obfuscation percentage. Calcification was specifically identified as regions with a given morphology and pixel value five standard deviations above the defined central lumen zone. Pearson correlation and multiple linear regression were used to explain the relationship between aortic measurements with and without contrast. Regressions include calcification percent (Model 1), and area (Model 2). Independent variables were non-contrast measurements and dependent variables were contrast measurements, age, and sex. Results: Correlations of calcification percent ranged from 0.86 at T11 and 0.94 and L2. Correlations of calcification area ranged from 0.66 at T12 to 0.84 at L3. In Model 1, for every percent increase in post-contrast calcification, non-contrast calcification percent increased by 11% (β=1.11, p <0.001, R2=0.85). In Model 2, for every mm2 increase in post-contrast calcification area, non-contrast calcification area increased by 0.45 mm2 (β=1.45, p <0.001, R2=0.69). Variance inflation factor for Model 1 was 1.08 and 1.07 for Model 2. Conclusion: In conclusion, this research proposes a correction score for comparisons of abdominal aortic calcification measurements in post-contrast and non-contrast scans.

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Variable selection in support vector regression using angular search algorithm and variance inflation factor

Journal of Chemometrics ◽

10.1002/cem.3282 ◽

2020 ◽

Vol 34 (12) ◽

Author(s):

Gabriely S. Folli ◽

Márcia H.C. Nascimento ◽

Ellisson H. Paulo ◽

Pedro H.P. Cunha ◽

Wanderson Romão ◽

...

Keyword(s):

Variable Selection ◽

Support Vector Regression ◽

Search Algorithm ◽

Support Vector ◽

Variance Inflation Factor ◽

Inflation Factor

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Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2012001200010 ◽

2012 ◽

Vol 47 (12) ◽

pp. 1743-1750 ◽

Cited By ~ 7

Author(s):

Juliana Petrini ◽

Raphael Antonio Prado Dias ◽

Simone Fernanda Nedel Pertile ◽

Joanir Pereira Eler ◽

José Bento Sterman Ferraz ◽

...

Keyword(s):

Birth Weight ◽

Linear Dependence ◽

Correlation Matrix ◽

Condition Index ◽

Additive Effects ◽

Variance Inflation Factor ◽

Explanatory Variables ◽

Scrotal Circumference ◽

Natural Connection ◽

Inflation Factor

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

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Collinearity: revisiting the variance inflation factor in ridge regression

Journal of Applied Statistics ◽

10.1080/02664763.2014.980789 ◽

2014 ◽

Vol 42 (3) ◽

pp. 648-661 ◽

Cited By ~ 54

Author(s):

C.B. García ◽

J. García ◽

M.M. López Martín ◽

R. Salmerón

Keyword(s):

Ridge Regression ◽

Variance Inflation Factor ◽

Inflation Factor

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The Relationship between the Country‘s Global Competitiveness and its National MNEs

Visegrad Journal on Bioeconomy and Sustainable Development ◽

10.2478/vjbsd-2019-0017 ◽

2019 ◽

Vol 8 (2) ◽

pp. 87-92

Author(s):

Inga Aleksandrovna Mezinova ◽

Janetta Benikovna Amirkhanyan ◽

Oleg Valerjevich Bodiagin ◽

Milena Miroslavovna Balanova

Keyword(s):

Multinational Enterprises ◽

Multinational Firms ◽

Variance Inflation Factor ◽

Global Competitiveness ◽

Global Competitiveness Index ◽

Inflation Factor ◽

Competitiveness Index ◽

Country Competitiveness ◽

The Relationship

Abstract The main purpose of this paper is to study the influence of home-multinational enterprises on country global competitiveness and to determine how this influence changes with the stage of country competitiveness. Based on the regression model, Variance Inflation Factor test and Agglomerative Hierarchical Clustering method, we analyzed the WEF Global Competitiveness Index 2017–2018 of those countries whose multinational firms were included into the Forbes Global 2000 list of 2017. The findings highlighted the important role of home-MNEs as determinants of countries‘ competitiveness, however MNE-related contribution of different pillars and components of the Global Competitiveness Index vary, depending on the stage of competitiveness of the studied 58 countries.

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Decision letter for "Variable selection in support vector regression using angular search algorithm and variance inflation factor"

10.1002/cem.3282/v1/decision1 ◽

2020 ◽

Keyword(s):

Variable Selection ◽

Support Vector Regression ◽

Search Algorithm ◽

Support Vector ◽

Variance Inflation Factor ◽

Inflation Factor

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An improved robust variance inflation factor: Reducing the negative effects of good leverage points

Kuwait Journal of Science ◽

10.48129/kjs.15533 ◽

2021 ◽

Author(s):

Osman U. Ekiz ◽

Keyword(s):

Linear Regression Analysis ◽

Real Data ◽

Multiple Linear Regression Analysis ◽

Coefficient Of Determination ◽

Negative Effects ◽

Variance Inflation Factor ◽

Explanatory Variables ◽

Robust Variance ◽

Inflation Factor ◽

Better Than

In multiple linear regression analysis, the variance inflation factor is a well-known collinearity measure. It is defined as the function of the coefficient of determination between the explanatory variables, and it is based on the maximum likelihood estimator of the regression coefficients. Nevertheless, in addition to outliers, leverage observations can have significant impact on the coefficient of determination, and thereby the variance inflation factor. This study presents an improved robust variance inflation factor estimator that is not affected by these observations. Simulation studies and a real data analysis indicate that the modified robust variance inflation factor estimator performs better than the traditional one.

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