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. Tuchband T., Rozsa Sz. Modeling tropospheric zenith delays using regression models based on surface meteorology data (submitted for publication) in the Proceedings of IAG Scientific Assembly 2009 , Buenos Aires, 31 August–4 September, 2009

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343 352 Nicholls, S. J., Tuzcu, E. M., Sipahi, I. és mtsai: Statins, high-density lipoprotein cholesterol and regression of coronary atherosclerosis. JAMA, 2007

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our perspective, also plays a crucial role in the phenomenon, therefore, participation rate in the last parliamentary election was considered a proxy. Our method – using country-specific regression models with municipalities as observations – is novel

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content as binder gives dense structure to AAC by minimizing the voids and pores in micro-structure. Thus, the electrical resistivity of concrete enhances. Fig. 6. Electrical resistivity test results 4 Regression analysis Regression analysis is a

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correlations between IA, childhood ADHD symptoms, adult ADHD symptoms, depression, anxiety, impulsiveness, alcohol use, and related symptoms, we used the Pearson’s correlations. Later, hierarchical multiple regression analysis was used to examine the individual

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multivariate nonlinear regression analysis. The activation energy was also determined using two equations based on the variable heating rate method, by differential Kissinger [ 11 ] and integral Ozawa [ 12 ] isoconversional methods. Investigation of the

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component analysis. Finally, multiple regression analyses were carried out by using a step-wise approach to estimate the relationship among the motivational scales ( Kormos & Csizér, 2008 ). Results

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Journal of Behavioral Addictions
Authors:
Kristian Krause
,
Anja Bischof
,
Silvia Lewin
,
Diana Guertler
,
Hans-Jürgen Rumpf
,
Ulrich John
, and
Christian Meyer

subfactors (independent variables), we ran quantile regressions (QRs) using Stata’s qreg procedure. QR allows to model the conditional median or any other quantile, whereas ordinary least squares (OLS) regression provides estimates of conditional means

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regression was used to identify the risk factors of problem gambling. The following independent variables entered the analysis: age, gender, school type, family structure (both parents = 1/different family structure = 0), three smoking status variables

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analyzed by generalized least squares method and multiple regression analysis. Pareto variance analysis (ANOVA) was used to determine the statistical parameters. Minitab 16 software was used for surface response analysis. Results are expressed as mean

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