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Journal articles on the topic 'Forward Stepwise Regression'

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Published: 4 June 2021

Last updated: 9 February 2022

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Henderson, Douglas A., and Daniel R. Denison. "Stepwise Regression in Social and Psychological Research." Psychological Reports 64, no. 1 (1989): 251–57. http://dx.doi.org/10.2466/pr0.1989.64.1.251.

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Researchers often invoke stepwise ordinary least squares regression to explain, predict or classify practical problems or theoretical constructs in psychological and social research. Unfortunately, this statistical technique is used without proper consideration for its inherent theoretical and practical limitations, a problem expected to grow even more serious with the proliferation of statistical packages for use on personal computers. Use of stepwise regression in social and psychological research is reconsidered here. Explanations of forward selection, backward elimination and combination stepwise procedures are provided; limitations of the technique, statistical and practical, are then addressed. Analysis shows that most of the current applications of stepwise regression should be rejected, or at least tempered with strong qualification to inference.

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In Lee, Kang, and John J. Koval. "Determination of the best significance level in forward stepwise logistic regression." Communications in Statistics - Simulation and Computation 26, no. 2 (1997): 559–75. http://dx.doi.org/10.1080/03610919708813397.

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Freedman, L. S., D. Pee, and D. N. Midthune. "The Problem of Underestimating the Residual Error Variance in Forward Stepwise Regression." Statistician 41, no. 4 (1992): 405. http://dx.doi.org/10.2307/2349005.

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Afiqah Muhamad Jamil, Siti, Mohd Asrul Affendi Abdullah, Kek Sie Long, Nur Fazilla Mohd Jupri, and Mustafa Mamat. "A Stepwise Logistic Regression Analysis: An application toward Poultry Farm Data in Johor." International Journal of Engineering & Technology 7, no. 3.28 (2018): 68. http://dx.doi.org/10.14419/ijet.v7i3.28.20968.

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The aims of this study are to fit a logistic regression model towards the fly problem in a farm and to identify the variables that are associated with the fly problem in a poultry farm. By using SPSS software, this study used ‘FORWARD STEPWISE’ and ‘BACKWARD STEPWISE’ methods to perform the analysis. Compared to linear regression analysis, logistic regression does not require rigorous assumptions to be met. This study used Likelihood Ratio test, Omnibus test and Hosmer and Lemeshow test to validate and to test the fit of poultry farm data. Akaike Information Criterion (AIC) is calculated to observe the difference between the methods of stepwise used by SPSS software in this study. As a result, logistic regression is fit towards poultry farm data by a stepwise procedure. BACKWARD STEPWISE seems to be more suitable for conducting the stepwise method of analysis. Besides, variables that influence the problem of fly in a poultry are the wasps, distance and number of flies.

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Soroush, AliReza, Ardeshir Bahreininejad, and Jan van den Berg. "A hybrid customer prediction system based on multiple forward stepwise logistic regression mode." Intelligent Data Analysis 16, no. 2 (2012): 265–78. http://dx.doi.org/10.3233/ida-2012-0523.

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Ma, Nan, Patrick Wang, Qin He, Wenjia Li, Ying Zheng, and Zhang Huan. "Prediction of Television Audience Rating Based on Fuzzy Cognitive Maps with Forward Stepwise Regression." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 07 (2017): 1750020. http://dx.doi.org/10.1142/s0218001417500203.

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The television audience rating is an important indicator of the quality of television programs and important reference for decision-television operator. As many factors that affect the ratings and the trends are complex, the article proposes a television rating mining predictive model based on fuzzy cognitive maps (FCMs) with forward stepwise regression. The FCMs use the causal relationship among various concept nodes to simulate the fuzzy reasoning, and enhance the dynamic behavior of the simulation system with its feedback mechanism, which is suitable for system to predict the trend of television audience rating. A FCM-based model for predicting television audience rating is proposed in this paper. The forward stepwise regression algorithm is used to obtain concept nodes of coarse weight matrix for FCMs, and then a training weight algorithm is used to refine the coarse weight matrix model. The FCM model is applied to mine the television audience rating, realizing to predict the television playback volume. The experimental result shows that the modeling method is effective.

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Doder, Dragan, and Radoslava Doder. "The effect of anthropological characteristics on the efficiency of execution of forward kick." Zbornik Matice srpske za prirodne nauke, no. 111 (2006): 45–54. http://dx.doi.org/10.2298/zmspn0611045d.

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A sample of eighty-two karatists at the ages from ten to fourteen has been analyzed for a system of 25 variables (12 morphological, 12 basic motoric variables and 1 specific motoric variable) with the aim of establishing the effect of prediction system of morphological variables and a system of basic motoric variables on the criterion variable, i.e., the direct forward kick - mae geri. The obtained results showed that the system of morphological variables had a statistically significant effect on the execution of direct forward kick. Among the individual variables in the regression analysis body weight had the largest effect. The stepwise method showed that body height and weight had highest prediction values. Young karatists of high body height, with long extremities and increased weight, had better results in the execution of direct forward kick. The investigation of basic motoric variables used in the regression and stepwise analyses indicated that the endurance in half-squat with weight and standing jump had statistically significant effects on the efficiency of direct forward kick. Thus it was concluded that the speed of forward kick depends on the explosive and static strength of legs.

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Jang, Changsoo, Byeng Dong Youn, Ping F. Wang, Bongtae Han, and Suk-Jin Ham. "Forward-stepwise regression analysis for fine leak batch testing of wafer-level hermetic MEMS packages." Microelectronics Reliability 50, no. 4 (2010): 507–13. http://dx.doi.org/10.1016/j.microrel.2009.11.014.

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Laborda, Juan, and Seyong Ryoo. "Feature Selection in a Credit Scoring Model." Mathematics 9, no. 7 (2021): 746. http://dx.doi.org/10.3390/math9070746.

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This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.

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Safitri, Ani, Rahma Anisa, and Bagus Sartono. "Seleksi Peubah menggunakan Algoritme Genetika pada Data Rancangan Faktorial Pecahan Lewat Jenuh Dua Taraf." Xplore: Journal of Statistics 10, no. 1 (2020): 55–69. http://dx.doi.org/10.29244/xplore.v10i1.473.

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In certain fields, experiments involve many factors and are constrained by costs. Reducing runs is one of the solutions to reduce experiment costs. But that can cause the number of runs to become less than the number of factors. This case of experimental design also is known as a supersaturated design. The important factors in this design are generally estimated by involving variable selection such as forward selection, stepwise regression, and penalized regression. Genetic algorithm is one of the methods that can be used for variable selection, especially for high dimensional data or supersaturated design. This study aims to use a genetic algorithm for variable selection in the supersaturated design and compare the genetic algorithm results with a stepwise regression which is generally used for a simple design. This study also involved fractional factorial design principles. The result showed that the main factors and interactions of the genetic algorithm and stepwise regression were quite different. But the principle was the same because the variables correlated. The genetic algorithm model had a smaller AIC and BIC and all of the main factors and interactions which had chosen were significant on the 0.1%. Therefore genetic algorithm model was chosen although computation time was much longer than stepwise regression.

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Sapkota, Kamal Raj. "Study on QSPR Method for Theoretical Calculation of Boiling Point of Some organic Compounds." Himalayan Physics 3 (January 1, 2013): 93–95. http://dx.doi.org/10.3126/hj.v3i0.7316.

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Quantitative structure-property relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of boiling point using a set of 25 organic compounds. The molecular descriptors used to represent molecular structure include topological indices and constitutional descriptors. Forward stepwise regression was used to construct the QSPR models. Multiple linear regressions is utilized to construct the linear prediction model. The prediction result agrees well with the experimental value of these properties.The Himalayan PhysicsVol. 3, No. 3, July 2012Page: 93-95

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Intano, Gabriel P., and William R. Howse. "Predicting Performance in Army Aviation Flight Training." Proceedings of the Human Factors Society Annual Meeting 36, no. 12 (1992): 907–10. http://dx.doi.org/10.1518/107118192786750304.

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The Army Research Institute Aviation Research and Development Activity successfully implemented the Multi-Track Test Battery (MTTB) and associated classification functions in 1988. The battery and functions have been used to assign flight students to their combat skills aircraft. The present program determined the applicability of the battery to prediction of student performance in flight training. Performance evaluation in training consists of flight phase grades and academic phase grades. In addition to these grades, Overall Average Grade and Overall Flight Grade were also predicted using Forward Stepwise Multiple Regression procedures. Stepwise Multiple Discriminant Analysis was used to investigate two additional measures, flight deficiency training setback and flight deficiency attrition.

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Tan, H., SJ Lin, BL Lambert, and SL Sclove. "PMH38 COMPARING EXHAUSTIVE CHAID CLASSIFICATION TREE AND FORWARD STEPWISE LOGISTIC REGRESSION (LR) IN EXPLAINING THE PRESCRIBING OF ANTIDEPRESSANTS." Value in Health 8, no. 3 (2005): 396. http://dx.doi.org/10.1016/s1098-3015(10)63073-2.

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A., Ademuyiwa J., and Adetunji A. A. "Impact of Some Economic Variables on the Real Gross Domestic Product of Nigeria." Budapest International Research and Critics Institute (BIRCI-Journal) : Humanities and Social Sciences 2, no. 4 (2019): 12–19. http://dx.doi.org/10.33258/birci.v2i4.563.

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The influences of External Debt Service (EDS), External Debt Stock (EDSt), Government Expenditure (GE), Inflation Rate (InfR), Interest Rate (IntR) and Exchange Rate (ExR) of Nigeria on the Real Gross Domestic Product (RGDP) are examined. Results of the analysis using Stepwise Regression (Backward Elimination and Forward Selection) reveals that GE, EDS, and IntR have positive significant contributions to the RGDP of the country compared to other variables considered.

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Straub, Rachel K., Adam J. Barrack, Jordan Cannon, and Christopher M. Powers. "Trunk Inclination During Squatting is a Better Predictor of the Knee-Extensor Moment Than Shank Inclination." Journal of Sport Rehabilitation 30, no. 6 (2021): 899–904. http://dx.doi.org/10.1123/jsr.2020-0397.

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Context: A limitation of previous studies on squatting mechanics is that the influence of trunk and shank inclination on the knee-extensor moment (KEM) has been studied in isolation. Objective: The purpose of the current study was to determine the influence of segment orientation on the KEM during freestanding barbell squatting. Design: Repeated-measures cross sectional. Setting: University research laboratory. Participants: Sixteen healthy individuals (8 males and 8 females). Intervention: Each participant performed 8 squat conditions in which shank and trunk inclinations were manipulated. Main Outcome Measures: 3D kinematic and kinetic data were collected at 250 and 1500 Hz, respectively. Regression analysis was conducted to identify the individual relationships between the KEM and the trunk and shank inclination at 60° and 90° of knee flexion. To identify the best predictor(s) of the KEM, stepwise regression was implemented. Results: Increased shank inclination increased the KEM (P < .001, R2 = .21–.25). Conversely, increased trunk inclination decreased the KEM (P < .001, R2 = .49–.50). For the stepwise regression, trunk inclination entered first and explained the greatest variance in the KEM (all P < .001, R2 = .49–.50). Shank inclination entered second (all P < .010, R2 = .53–.54) and explained an additional 3% to 5% of the variance. Conclusions: Our results confirm that inclination of the trunk and shank have an opposing relationship with the KEM. Increased forward shank posture increases the KEM, while increased forward trunk posture decreases the KEM. However, when viewed in combination, the trunk was the superior predictor of the KEM, highlighting the fact that increased quadriceps demand created by a forward shank can be offset by trunk inclination.

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Wang, Xu, Biyu Shen, Xun Zhuang, Xueqin Wang, and Weiqun Weng. "Investigating Factors Associated with Depressive Symptoms of Chronic Kidney Diseases in China with Type 2 Diabetes." Journal of Diabetes Research 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/1769897.

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Aim.To assess the depressive symptoms status of chronic kidney diseases in Nantong, China, with type 2 diabetes and to identify factors associated with depressive symptoms.Methods.In this cross-sectional analytic study, 210 type 2 diabetic patients were recruited from the Second Affiliated Hospital of Nantong University. Depressive symptoms were assessed with the depression subscale of the Hospital Anxiety and Depression Scale (HAD-D). The quality of life was measured with the RAND 36-Item Health Survey (SF-36). And the independent risk factors of depressive symptoms were assessed by using a stepwise forward model of logistic regression analysis.Results.The mean age of the study subjects was 57.66 years (SD: 11.68). Approximately 21.4% of subjects reported depressive symptoms (n=45). Forward stepwise logistic regression analysis showed that female gender (P=0.010), hypertension (P=0.022), Stage IV (P=0.003), and Stage V (P<0.001) were significant risk factors for depressive symptoms. The quality of life of individuals with HAD-D score <11 was significantly better compared with individuals with HAD-D score ≥ 11.Conclusions.These results indicate that clinicians should be aware that female patients with chronic kidney diseases with T2DM in their late stage with hypertension are at a marked increased risk of depressive symptoms. Providing optimal care for the psychological health of this population is vital.

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Przulj, Novo, and Vojislava Momcilovic. "Characterization of vegetative and grain filling periods of winter wheat by stepwise regression procedure: I. Vegetative period." Genetika 43, no. 2 (2011): 349–59. http://dx.doi.org/10.2298/gensr1102349p.

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Modeling plant growth by mathematical functions is important for understanding plant development and growth. Most of the models of dry matter accumulation in small cereals simulated the period of grain filling while small attention has been devoted to mathematical simulation of vegetative period till anthesis. The aim of this research was to determine the most appropriate polynomial non-linear regression for dry matter accumulation till anthesis in winter wheat. Pobeda, a medium early variety, was used as model genotype for this research. A 5-year field data were analyzed by the forward procedure of stepwise regression. Although the procedure requires the maximum power of the polynomial regression to be used, we suggest using a lower power since it is easier for understanding and explanation and it is taking into account literature sources and biological laws. It can be accepted that quadratic regression model appropriately fits the process of dry matter accumulation till anthesis in winter wheat.

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Starr, Carol, Janet Suttle, A. G. Morgan, and D. B. Smith. "A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near infrared spectroscopy." Journal of Agricultural Science 104, no. 2 (1985): 317–23. http://dx.doi.org/10.1017/s0021859600043987.

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SummaryPredictions of nitrogen, oil and glucosinolate concentration in rapeseed samples were made by near infrared reflectance analysis after various grinding treatments. Also examined were the effects of normalizing reflectance data and the possible advantage of using all combinations of two and three wavelengths in the calibration regression analysis over forward stepwise regression. The main conclusion was that drying the samples prior to a controlled grinding treatment gave the best results, although acceptable results for selection purposes could be obtained using whole seeds to predict nitrogen and oil. None of the treatments of the seed or reflectance data allowed acceptable prediction of glucosinolate content.

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Hrynkiewicz, Monika, Anna Iwaniak, Justyna Bucholska, Piotr Minkiewicz, and Małgorzata Darewicz. "Structure–Activity Prediction of ACE Inhibitory/Bitter Dipeptides—A Chemometric Approach Based on Stepwise Regression." Molecules 24, no. 5 (2019): 950. http://dx.doi.org/10.3390/molecules24050950.

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Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure–bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides’ physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA®13.1. FR/BR models differed in R2 (0.91/0.76, respectively). The impact of C-atC(−) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (−) correlations with log IC50 are presented in parens. Moreover, C-Bur(+), N-atH(+), and N-Pol(−) were also found to be important in the FR model. The additional statistical significance of N-bul(−), N-Bur(−), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the “ideal” bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log Rcaf. = f (observed log IC50) and log Rcaf. = f (predicted log IC50) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure–bioactivity prediction of other bioactivities peptides.

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Johnson, Suzanne, and Stanford E. Rubin. "Prediction of Starting Weekly Salaries of Rehabilitated Severely Disabled Women." Journal of Applied Rehabilitation Counseling 17, no. 4 (1986): 39–41. http://dx.doi.org/10.1891/0047-2220.17.4.39.

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The relationship of race, age, marital status, years of education, number of months the case has been open, and number of services with starting weekly salary of severely physically disabled female rehabilitants was examined via forward stepwise multiple regression analyses. Years of education and number of months the case had been open were positively correlated with salary for visually impaired women and for the total sample. Years of education alone provided the best prediction model for salary among orthopedically impaired women.

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Villordon, Arthur, Christopher Clark, Tara Smith, Don Ferrin, and Don LaBonte. "Combining Linear Regression and Machine Learning Approaches to Identify Consensus Variables Related to Optimum Sweetpotato Transplanting Date." HortScience 45, no. 4 (2010): 684–86. http://dx.doi.org/10.21273/hortsci.45.4.684.

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Forward and stepwise regression methods identified variables related to the influence of transplanting date on yield of U.S. #1 sweetpotatoes. The variables were mean minimum soil temperature 5 days after transplanting (DAT), wind direction at transplanting, and accumulated heat units (growing degree-days) 5 DAT. Machine learning techniques identified the same variables using leave-one-out and k-fold cross-validation methods. Growers and crop consultants, in collaboration with knowledge workers, can use this information in conjunction with public and subscription-based weather forecasts to further optimize transplanting date determination and for making risk-averse decisions. These results help to underscore the importance of consistent transplant establishment as one of the determinants of storage root yield in sweetpotatoes.

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Saishu, Hiroki, Kota Kudo, and Yuichi Takano. "Sparse Poisson regression via mixed-integer optimization." PLOS ONE 16, no. 4 (2021): e0249916. http://dx.doi.org/10.1371/journal.pone.0249916.

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We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and computer hardware. In contrast to many sparse estimation algorithms, the MIO approach has the advantage of finding the best subset of explanatory variables with respect to various criterion functions. In this paper, we focus on a sparse Poisson regression that maximizes the weighted sum of the log-likelihood function and the L2-regularization term. For this problem, we derive a mixed-integer quadratic optimization (MIQO) formulation by applying a piecewise-linear approximation to the log-likelihood function. Optimization software can solve this MIQO problem to optimality. Moreover, we propose two methods for selecting a limited number of tangent lines effective for piecewise-linear approximations. We assess the efficacy of our method through computational experiments using synthetic and real-world datasets. Our methods provide better log-likelihood values than do conventional greedy algorithms in selecting tangent lines. In addition, our MIQO formulation delivers better out-of-sample prediction performance than do forward stepwise selection and L1-regularized estimation, especially in low-noise situations.

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Hou, Jia-Sian, Chih-Hsien Wang, Yu-Hsien Lai, et al. "Negative Correlation of Serum Adiponectin Levels With Carotid–Femoral Pulse Wave Velocity in Patients Treated With Hemodialysis." Biological Research For Nursing 20, no. 4 (2018): 462–68. http://dx.doi.org/10.1177/1099800418768887.

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Purpose: The aim of this study was to evaluate the relationship between serum adiponectin levels and carotid–femoral pulse wave velocity (cfPWV) in hemodialysis (HD) patients. Method: Blood samples were obtained from 120 HD patients. cfPWV was measured with a validated tonometry system (SphygmoCor; AtCor Medical, West Ryde, Australia). Serum adiponectin levels were measured using a commercially available enzyme-linked immunosorbent assay kit. Results: By univariate linear analysis of cfPWV in HD patients, we found that diabetes ( r = .281, p = .002), pre-HD body weight ( r = .194, p = .033), post-HD body weight ( r = .192, p = .036), waist circumference ( r =.210, p = .022), and body fat mass ( r = .194, p = .034) were positively correlated, whereas adiponectin level ( r = −.254, p = .005) was negatively correlated with cfPWV in HD patients. Multivariate forward stepwise linear regression analysis showed that diabetes (β = .274, p = .006) and adiponectin level (β = −.215, p = .016) were independent predictors of cfPWV in HD patients. Moreover, post-HD body weight (β = −.274, p = .041), waist circumference (β = −.311, p < .001), logarithmically transformed triglyceride level (log-TG; β = −.186, p = .031), and log-glucose (β = −.225, p = .008) were negatively associated with adiponectin levels in HD patients after multivariable forward stepwise linear regression analysis. Conclusions: Among HD patients, serum adiponectin level was inversely associated with cfPWV level, and post-HD body weight, waist circumference, log-TG, and log-glucose were negatively associated with adiponectin level.

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Gavril, Mihaela, Peter V. Hodson, and Jim McLellan. "Decoloration of Amaranth by the white-rot fungusTrametes versicolor. Part I. Statistical analysis." Canadian Journal of Microbiology 53, no. 2 (2007): 313–26. http://dx.doi.org/10.1139/w06-123.

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The white-rot fungus Trametes versicolor decolorized the mono-azo-substituted naphthalenic dye Amaranth. The relationship between the amount of enzymes present in the system and the efficiency of the decoloration process was investigated. The two responses used to quantify the process of decoloration (i.e., initial decoloration rate, v0, and the percent concentration of dye decolorized in 1 h, %c) were correlated with the amount of three enzymes considered for the study (lignin peroxidase, manganese peroxidase, and laccase) and analyzed through stepwise regression analysis (forward, backward, and mixed). The results of the correlation analysis and those of the regression analysis indicated that lignin peroxidase is the enzyme having the greatest influence on the two responses.

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Ece, Arslan, and Güven Sayılgan. "Macroeconomic Determinants of Financial Distress in Turkey: An Econometric Analysis." Australasian Business, Accounting & Finance Journal 14, no. 5 (2020): 86–107. http://dx.doi.org/10.14453/aabfj.v14i5.6.

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Purpose The purpose of this paper is to investigate the possible links between macroeconomic factors and financial distress in Turkey. Design/methodology/approach Based on the 2009/1-2016/2 quarterly data of macroeconomic factors and the number of filings for bankruptcy postponement, econometric models are developed using forward stepwise regression and classical regression methods to determine the factors influencing financial distress. A vector error correction model is also developed using macroeconomic factors found significant in both methods to investigate the interactions of financial distress with them. Findings In the stepwise regression implementation, performed with 16 independent variables, statistically significant variables entered into the model are industrial production index with negative sign as expected and the unemployment rate with negative sign against the expectations. In the classical regression implementation, performed with 7 independent variables, statistically significant variables are ex ante real interest rate with positive sign and gross domestic product with negative sign as expected and money supply with negative sign against the expectations. The impulse response graphics of a vector error correction model involving bankruptcy postponement, industrial production index and nominal interest rate indicates that bankruptcy postponement is influenced by the shocks both in itself and in industrial production index. Originality/value This is the first study in Turkey investigates macroeconomic determinants of financial distress.

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Jović, Ozren, and Tomislav Šmuc. "Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides." Molecules 25, no. 9 (2020): 2198. http://dx.doi.org/10.3390/molecules25092198.

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Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.

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GU, W., A. R. VIEIRA, R. M. HOEKSTRA, P. M. GRIFFIN, and D. COLE. "Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections." Epidemiology and Infection 143, no. 13 (2015): 2786–94. http://dx.doi.org/10.1017/s095026881500014x.

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SUMMARYTo design effective food safety programmes we need to estimate how many sporadic foodborne illnesses are caused by specific food sources based on case-control studies. Logistic regression has substantive limitations for analysing structured questionnaire data with numerous exposures and missing values. We adapted random forest to analyse data of a case-control study of Salmonella enterica serotype Enteritidis illness for source attribution. For estimation of summary population attributable fractions (PAFs) of exposures grouped into transmission routes, we devised a counterfactual estimator to predict reductions in illness associated with removing grouped exposures. For the purpose of comparison, we fitted the data using logistic regression models with stepwise forward and backward variable selection. Our results show that the forward and backward variable selection of logistic regression models were not consistent for parameter estimation, with different significant exposures identified. By contrast, the random forest model produced estimated PAFs of grouped exposures consistent in rank order with results obtained from outbreak data, with egg-related exposures having the highest estimated PAF (22·1%, 95% confidence interval 8·5–31·8). Random forest might be structurally more coherent and efficient than logistic regression models for attributing Salmonella illnesses to sources involving many causal pathways.

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Mohit Tiwari et al.,, Mohit Tiwari et al ,. "Using Forward Stepwise Multiple Regression to Predict Impact of “Word of Mouth (WOM)” on “Consumer Purchase Intention (CPI)” for Consumer Goods." International Journal of Mechanical and Production Engineering Research and Development 10, no. 3 (2020): 10297–308. http://dx.doi.org/10.24247/ijmperdjun2020985.

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Barmparas, Galinos, Matthew J. Martin, Douglas A. Wiegmann, Ken R. Catchpole, Bruce L. Gewertz, and Eric J. Ley. "Increased Age Predicts Failure to Rescue." American Surgeon 82, no. 11 (2016): 1073–79. http://dx.doi.org/10.1177/000313481608201122.

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Failure to rescue (FTR), defined as any death after the development of in-hospital complications, is an important quality measure, but the relationship with age after a traumatic injury, has not been well defined. We sought to examine whether older trauma patients are at higher risk for FTR. The National Trauma Databank (NTDB) research datasets 2007 to 2011 were queried for patients ≥16 years who had any reported complication. Those who survived (non-FTR) were compared with those who did not (FTR) using a forward logistic regression model. Overall, 218,986 subjects met inclusion criteria of those, 201,358 (91.2%) survived their complication (non-FTR) and 17,628 (8.8%) died (FTR). A forward logistic regression identified age 65 to 89 years as the strongest predictor of FTR [adjusted odds ratio (AOR) 95% confidence interval (CI): 6.58 (6.11, 7.08), P < 0.001]. Using age group 16 to 45 years as the reference group, the adjusted risk for FTR increased with increasing age in a stepwise fashion [AOR (95 % CI): 1.94 (1.80, 2.09) for age 46 to 65 years, 6.78 (6.19, 7.42) for age 66 to 89 years and 27.58 [21.81, 34.87] for age ≥90 years]. The adjusted risk of FTR also increased in a stepwise fashion with increasing number of complications, reaching AOR (95 per cent CI) of 2.25 (2.07, 2.45), P < 0.001 for ≥4 complications. The risk of failure to rescue increases with age and number of complications. Strategies which track this quality measure to encourage early recognition and treatment of complications in the elderly are necessary.

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Koyama, Teruhide, Nagato Kuriyama, and Ritei Uehara. "Midregional Proadrenomedullin Can Reflect the Accumulation of Visceral Adipose Tissue—A Key to Explaining the Obesity Paradox." International Journal of Environmental Research and Public Health 17, no. 11 (2020): 3968. http://dx.doi.org/10.3390/ijerph17113968.

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Background: The aim of this study was to investigate whether plasma midregional proadrenomedullin (MR-proADM) reflected body composition, such as body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), VAT/SAT ratio, body fat mass (BFM), and skeletal muscle mass (SMM). Methods: A total of 2244 individuals (727 men and 1517 women) were included in the study. Multiple regression analysis was performed to assess the combined influence of variables: age, daily alcohol consumption, Brinkman index, sleeping time, metabolic equivalents, anamnesis for hypertension, dyslipidemia, diabetes, and body composition of MR-proADM, by using a stepwise forward selection method. Results: MR-proADM was significantly related to all anthropometric indices (BMI, VAT, SAT, VAT/SAT ratio, BFM, and SMM) in men and women. On the basis of a stepwise forward selection method, VAT (men: beta = 0.184, p < 0.001, women: beta = 0.203, p < 0.001) and BFM (beta = 0.181, p < 0.001) in women, were found to be significantly associated with MR-proADM. Conclusion: This study suggests that plasma MR-proADM concentration is a more reliable indicator of VAT for fat distribution, and thus, MR-proADM may help better understand the obesity paradox. Changes in circulating levels of MR-proADM could possibly reflect changes in body composition, endocrine, and metabolic milieu.

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Li, Dian-Jeng, Shiou-Lan Chen, and Cheng-Fang Yen. "Multi-Dimensional Factors Associated with Illegal Substance Use Among Gay and Bisexual Men in Taiwan." International Journal of Environmental Research and Public Health 16, no. 22 (2019): 4476. http://dx.doi.org/10.3390/ijerph16224476.

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Illegal substance use in sexual minorities is an important health issue worldwide. The present cross-sectional study aimed to investigate the multi-dimensional factors associated with illegal substance use among gay and bisexual men in Taiwan. This questionnaire-survey study recruited 500 gay or bisexual men aged between 20 and 25 years. Their experiences of using eight kinds of illegal substances in the preceding month were collected. Their previous experiences of homophobic bullying, satisfaction with academic performance, truancy, perceived family and peer support in childhood and adolescence, and social-demographic characteristics, were also collected. Potential factors associated with illegal substance use were identified using univariate logistic regression, and further selected into a forward stepwise logistic regression model to identify the factors most significantly related to illegal substance use. A total of 22 (4.4%) participants reported illegal substance use in the preceding month, and mean age was 22.9 ± 1.6. Forward stepwise logistic regression revealed that being victims of homophobic cyberbullying in childhood and adolescence (odds ratio (OR) = 1.26; p = 0.011), disclosure of sexual orientation at junior high school (OR = 4.67; p = 0.001), and missing classes or truancy in senior high school (OR = 2.52; p = 0.041) were significantly associated with illegal substance use in early adulthood. Multi-dimensional factors in childhood and adolescence that were significantly associated with illegal substance use in early adulthood among gay and bisexual men were identified. Besides traditional bullying, the effect of cyberbullying and school performance on illegal substance use should not be ignored. This study is limited to the cross-sectional design and possible recall bias. Mental health professionals must routinely assess these significant factors to prevent and intervene in illegal substance use among gay and bisexual men.

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Chao, Yi-Sheng, and Chao-Jung Wu. "PD25 Principal Component Approximation: Medical Expenditure Panel Survey." International Journal of Technology Assessment in Health Care 34, S1 (2018): 138. http://dx.doi.org/10.1017/s0266462318003008.

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Introduction:Principal component analysis (PCA) is important to summarize data or reduce dimensionality. However, one disadvantage of using PCA is the interpretability of the principal components (PCs), especially in a high-dimensional database. This study aims to analyze the patterns of variance accumulation according to PCA loadings and to approximate PCs with input variables from sample data sets.Methods:There were three data sets of various sizes used to understand the performance of PC approximation: Hitters; SF-12v2 subset of the 2004 to 2011 Medical Expenditure Panel Survey (MEPS); and, the full set of 1996 to 2011 MEPS data. The variables in three data sets were first centered and scaled before PCA. PCs approximation was studied with two approaches. First, the PC loadings were squared to estimate the variance contribution by variables to PCs. The other method was to use forward-stepwise regression to approximate PCs with all input variables.Results:The first few PCs represented large portions of total variances in each data set. Approximating PCs using stepwise regression could more efficiently identify the input variables that explain large portions of PC variances than approximating according to PCA loadings in three data sets. It required few numbers of variables to explain more than eighty percent of the PC variances.Conclusions:Approximating and interpreting PCs with stepwise regression is highly feasible. Approximating PCs can help i) interpret PCs with input variables, ii) understand the major sources of variances in data sets, iii) select unique sources of information and iv) search and rank input variables according to the proportions of PC variance explained. This is an approach to systematically understand databases and search for variables that are highly representative of databases.

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Pratt, Cornelius B., and E. Lincoln James. "A Factor Analysis of Advertising Practitioners' Perceptions of Advertising Ethics." Psychological Reports 73, no. 3_suppl (1993): 1307–14. http://dx.doi.org/10.2466/pr0.1993.73.3f.1307.

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This research investigated the perceptions of advertising ethics among 460 members of the American Advertising Federation. A principal components factor analysis of members' perceptions, measured by 17 statements, produced four factors of “agencies' standards,” “active public responsibility,” “advertising claims,” and “agencies' social responsibility.” These factors, then used as composite independent variables in a forward stepwise multiple-regression analysis, showed that Factor 3 (“advertising claims”) was the best predictor of the application of deontological (or nonconsequential) ethics to ethically troublesome situations in advertising. Implications of these results for advertising ethics are outlined and suggestions for further research are offered.

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Wang, Jing-Jing, Yan Liang, Jin-Tao Su, and Jia-Ming Zhu. "An Analysis of the Economic Impact of US Presidential Elections Based on Principal Component and Logical Regression." Complexity 2021 (March 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/5593967.

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Economy is one of the major issues in the United States presidential election campaign. In order to investigate the impact of the US presidential election on the economy, this paper first constructs an analysis model of the economic impact on the United States based on stepwise regression and principal component analysis to analyze the focus of different candidates’ attention on the economic issues and its possible impact on the US economy in the election year and after the election; secondly, a Chinese economic impact analysis model based on factor analysis and machine learning logistic regression was constructed to analyze the impact of the US presidential election on the Chinese economy. At the same time, the future economic development of the United States and China based on the time series prediction model is forecast and analyzed, respectively. Finally, the countermeasures and policy suggestions on China’s related economic development are put forward.

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Irving, Brian A., K. Sreekumaran Nair, and Manivannan Srinivasan. "Effects of Insulin Sensitivity, Body Composition, and Fitness on Lipoprotein Particle Sizes and Concentrations Determined by Nuclear Magnetic Resonance." Journal of Clinical Endocrinology & Metabolism 96, no. 4 (2011): E713—E718. http://dx.doi.org/10.1210/jc.2010-2170.

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Abstract Context: Insulin resistance has been reported to be associated with development of atherogenic dyslipidemia. However, the confounding effects that obesity and low levels of cardiorespiratory fitness have on the relationship between insulin resistance and the development of atherogenic dyslipidemia remain to be adequately addressed. Objective: This study sought to examine the independent and combined effects of insulin sensitivity, body composition, and cardiorespiratory fitness on lipoprotein particle sizes and concentrations. Methods: Eight-four healthy, nondiabetic men (n = 43) and women (n = 41) were studied. The participants had a wide range of ages (18–30 and 65–80 yr), body composition (7.2–52.8% fat), and cardiorespiratory fitness (VO2 peak, 13.5–66.2 ml/kg·min). Body composition, cardiorespiratory fitness, insulin sensitivity, and lipoprotein particle profiles were assessed using dual-energy x-ray absorptiometry, cardiopulmonary exercise testing, a hyperinsulinemic-euglycemic clamp, and nuclear magnetic resonance spectroscopy, respectively. Results: Low levels of insulin sensitivity and cardiorespiratory fitness and higher levels of adiposity were associated with the accumulation of small, dense, low-density lipoprotein particles; small high-density lipoprotein particles; triglycerides; and very low-density lipoprotein particles. Multivariate forward-stepwise regression revealed that higher levels of adiposity, in particular truncal fat, were the strongest predictor of the lipoprotein particle size and concentration data, followed by insulin sensitivity. Conclusions: As expected, the accumulation of atherogenic lipoprotein particles (e.g. small, dense, low-density lipoprotein particles and small, high-density lipoprotein particles) was associated with low levels of insulin sensitivity, cardiorespiratory fitness, and higher levels of adiposity. However, multivariate forward-stepwise regression revealed that triglycerides, followed by truncal fat mass, were the strongest predictors of the lipoprotein particle size and concentration data.

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Erickson, John, Wylie Lopez, Owolabi Shonuga, Anthony Azzolini, David Tyler, and James Monica. "Volar Cortical Integrity in Non-Operatively Treated Adult Distal Radius Fractures." Journal of Hand Surgery (Asian-Pacific Volume) 25, no. 02 (2020): 192–98. http://dx.doi.org/10.1142/s2424835520500228.

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Background: We sought to independently validate published data that volar cortical integrity (VCI) is an independent predictor of maintenance of closed reduction in a series of non-surgically treated distal radius fractures, while simultaneously investigating previously reported predictors of instability. Our null hypothesis was that volar cortical integrity would not affect maintenance of reduction. Methods: Four hundred thirty-three adult distal radius fractures were screened from our Orthopedic database with 112 meeting inclusion criteria. Two groups were determined on the basis of maintenance of reduction (MOR) or loss of reduction (LOR) at 5–6 weeks post-reduction. Bivariate analysis was applied to previously published instability factors along with VCI. A forward stepwise logistic regression was then used to identify instability factors that, as a group, are most predictive of outcome. Results: Results of 112 patients were collected. Reduction was maintained in 62 patients (55.35%) at 5–6 weeks Biivariate analysis showed a statistically significant difference in volar cortical integrity between the 2 groups (80.6% MOR vs 59.2% LOR). Forward stepwise logistic regression analysis of subgroups revealed that post-reduction radial height and VCI were together statistically significant in the model. The presence of VCI, post-reduction radial height greater than or equal to ulnar neutral at the time of initial reduction resulted in a 67.8% successful MOR. Conclusions: We were able to confirm the importance of volar cortical integrity as a predictor of successful maintenance of reduction in non-surgically managed distal radius fractures. When combined with post-reduction radial height greater than or equal to ulnar neutral at the time of initial reduction, it showed a 67.8% MOR with non-operative management in our data-set.

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Hautzel, Hubertus, Elisabeth Pisar, David Lindner, Matthias Schott, Rüdiger Grandt, and Hans-Wilhelm Müller. "Impact of Renal Function and Demographic/Anthropomorphic Variables on Peak Thyrotropin After Recombinant Human Thyrotropin Stimulation: A Stepwise Forward Multiple-Regression Analysis." Thyroid 23, no. 6 (2013): 662–70. http://dx.doi.org/10.1089/thy.2012.0284.

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Jiao, Jiacheng, John Rollo, Baibai Fu, and Chunlu Liu. "Exploring Effective Built Environment Factors for Evaluating Pedestrian Volume in High-Density Areas: A New Finding for the Central Business District in Melbourne, Australia." Land 10, no. 6 (2021): 655. http://dx.doi.org/10.3390/land10060655.

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Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little data that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green ratio and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

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Estrada, Alba, and Raimundo Real. "A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling." Entropy 23, no. 8 (2021): 1014. http://dx.doi.org/10.3390/e23081014.

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Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency.

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Cleland, Frances E., and David L. Gallahue. "Young Children's Divergent Movement Ability." Perceptual and Motor Skills 77, no. 2 (1993): 535–44. http://dx.doi.org/10.2466/pms.1993.77.2.535.

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To examine the relations of age, gender, movement experience, and gross motor development to young children's scores on divergent movement (i.e., ability to perform and modify fundamental movement patterns), 40 boys and girls ages 4, 6, and 8 years were tested. Ability to make divergent movements was evaluated by having subjects perform 3 different fundamental movement tasks. Movement experience was assessed by asking parents to complete a questionnaire. Ulrich's Test of Gross Motor Development was used to evaluate subjects' gross motor skill. Testing sessions were videotaped for analysis. Based on a forward stepwise regression analysis, experience and age accounted for 45% of the variance in children's gross motor skill.

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Rizzo, Terry L., and Walter P. Vispoel. "Physical Educators’ Attributes and Attitudes Toward Teaching Students with Handicaps." Adapted Physical Activity Quarterly 8, no. 1 (1991): 4–11. http://dx.doi.org/10.1123/apaq.8.1.4.

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This study examined the relationship between selected attributes of physical educators (N=94) and their attitudes toward teaching students labeled educable mentally retarded, behaviorally disordered, and learning disabled. Data were collected through the administration of the Physical Educators’ Attitude Toward Teaching the Handicapped–II (PEATH–II) instrument. A forward stepwise multiple regression analysis showed that, of the eight selected teacher variables assessed, physical educators’ perceived competence in teaching students with handicaps was the best predictor of attitudes. A repeated-measures ANOVA and subsequent post hoc comparison tests indicated that learning disabled students were viewed more favorably than educable mentally retarded and behaviorally disordered students.

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Luo, Hanyang, Sijia Cheng, Wanhua Zhou, Wugang Song, Sumin Yu, and Xudong Lin. "Research on the Impact of Online Promotions on Consumers’ Impulsive Online Shopping Intentions." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 6 (2021): 2386–404. http://dx.doi.org/10.3390/jtaer16060131.

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Online shopping has developed rapidly, but recently, the sales of some online stores have suffered due to the decrease in people’s income caused by the epidemic. How to grasp the psychology and behavior of consumers and formulate effective marketing strategies is important for increasing sales. This paper puts forward a research model and eight hypotheses based on the research on the promotion situation and the types of products promoted on consumers’ impulse shopping, and uses regression analysis, t-test, stepwise regression and analysis of variance to conduct data analysis. The results show that online promotion has a significant impact on consumers’ willingness, and the anticipated regrets in different directions have totally different effect on willingness; the type of product promoted, and the impulsive characteristics of consumers play a moderating role; online promotion affects consumers’ impulsive online shopping intentions through the intermediary effect of expected regret. The influence of anticipated regrets on impulsive online shopping intention is proposed creatively, and the results also provide e-commerce merchants and customers with new insights in managing and treating online promotions. Managerial implications like controlling the duration of promotions and the number of preferential goods are put forward based on our analysis.

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Fajman, Martin, Milan Palát, and Pavel Sedlák. "Estimation of the yield of poplars in plantations of fast-growing species within current results." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 2 (2009): 25–36. http://dx.doi.org/10.11118/actaun200957020025.

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Current results are presented of allometric yield estimates of the poplar short rotation coppice. According to a literature review it is obvious that yield estimates, based on measurable quantities of a growing stand, depend not only on the selected tree specie or its clone, but also on the site location. The Jap-105 poplar clone (P. nigra x P. maximowiczii) allometric relations were analyzed by regression methods aimed at the creation of the yield estimation methodology at a testing site in Domanínek. Altogether, the twelve polynomial dependences of particular measured quantities approved the high empirical data conformity with the tested regression model (correlation index from 0.9033 to 0.9967). Within the forward stepwise regression, factors were selected, which explain best examined estimates of the total biomass DM; i.e. d.b.h. and stem height. Furthermore, the KESTEMONT’s (1971) model was verified with a satisfying conformity as well. Approving presented yield estimation methods, the presented models will be checked in a large-scale field trial.

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Quartezani, Waylson Zancanella, Julião Soares de Souza Lima, Talita Aparecida Pletsch, et al. "Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity." June 2019, no. 13(06) 2019 (June 20, 2019): 857–62. http://dx.doi.org/10.21475/ajcs.19.13.06.p1424.

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There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.

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Böttner, Antje, Jürgen Kratzsch, Grit Müller, et al. "Gender Differences of Adiponectin Levels Develop during the Progression of Puberty and Are Related to Serum Androgen Levels." Journal of Clinical Endocrinology & Metabolism 89, no. 8 (2004): 4053–61. http://dx.doi.org/10.1210/jc.2004-0303.

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Adiponectin is an adipocytokine with profound antidiabetic and antiatherogenic effects that is decreased in obesity. With the increasing prevalence of obesity and the emergence of related disorders, including type 2 diabetes in children, the regulation of adiponectin and its relationship to childhood obesity is of great interest. In this study we aimed to elucidate the impact of gender, pubertal development, and obesity on adiponectin levels in children. We investigated two phenotypically characterized cohorts of 200 normal weight and 135 obese children and adolescents covering a wide range of age (3.4–17.9 yr) and body mass index (−2.1 to +4.8 sd score). In healthy lean boys, adiponectin levels significantly declined in parallel with physical and pubertal development, subsequently leading to significantly reduced adiponectin levels in adolescent boys compared with girls (5.6 ± 0.5 vs. 7.1 ± 0.5 mg/liter; P = 0.03). This decline was inversely related to testosterone (r = −0.42; P &lt; 0.0001) and dehydroepiandrosterone sulfate (r = −0.20; P = 0.0068) serum concentrations and may account for the gender differences seen in adults. Using a stepwise forward multiple regression model, pubertal stage was the strongest independent predictor of adiponectin (r2 = 0.206; P &lt; 0.0001), with additional influences of body mass index sd score and testosterone. Adiponectin levels were decreased in obese children and adolescents compared with lean peers of corresponding age and pubertal stage (5.18 vs. 7.13 mg/liter; P = 0.015). In obese children, adiponectin levels were closely associated with parameters related to the metabolic syndrome, such as insulin resistance, hyperinsulinemia, blood pressure, and uric acid, in univariate and multivariate analyses, with the insulin sensitivity index being the strongest independent parameter identified by stepwise forward multiple regression (r2 = 0.226; P &lt; 0.0001). Hence, there is a strong association of adiponectin serum concentrations with obesity, pubertal development, and metabolic parameters in children indicating epidemiological and pathophysiological relevance already in childhood.

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Bere, T., and JG Tundisi. "Epipsammic diatoms in streams influenced by urban pollution, São Carlos, SP, Brazil." Brazilian Journal of Biology 70, no. 4 (2010): 920–30. http://dx.doi.org/10.1590/s1519-69842010000500002.

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Epipsammic diatoms have important implications for ecosystem processes in lotic environments. Most of the studies on benthic diatoms concentrate on epilithic diatoms and very little is known about epipsammic diatoms. The objective of this study was to assess epipsammic diatom communities in streams in relation to environmental conditions. Epipsammic diatoms and water quality sampling was done at 7 sites during summer base flow period (2008). Forward stepwise multiple regression and canonical correspondence analysis (CCA) were used to determine environmental gradients along which species vary with physical and chemical variables. A total of 112 diatom species distributed among 44 genera were recorded. Altitude and the process of eutrophication played a significant role in structuring diatom communities in the study region.

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Beaumont Smith, M., and E. Begemann. "Measuring associations between working capital and return on investment." South African Journal of Business Management 28, no. 1 (1997): 1–5. http://dx.doi.org/10.4102/sajbm.v28i1.783.

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The two conflicting goals of working capital management are profitability and liquidity. This article looks at return on investment as a measure of profitability and some traditional and more recently developed working capital concepts as liquidity measures. Associations were measured between profitability and the liquidity concepts by using chi-square analysis and stepwise forward regression. The statistical test results showed that a traditional working capital leverage ratio, current liabilities divided by funds flow, displayed the greatest associations with return on investment. Well-known liquidity concepts such as the current and quick ratios registered insignificant associations whilst only one of the newer working capital concepts, the comprehensive liquidity index, indicated significant associations with return on investment.

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Hutchinson, Robert. "Culture versus Structure: A Critical Perspective on the Role of Culture in Tax Evasion." Accounting Historians Journal 46, no. 1 (2019): 79–91. http://dx.doi.org/10.2308/aahj-10659.

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ABSTRACT Culture is a somewhat nebulous term, particularly in the context of accounting scholarship. Historically, it has been offered as a factor to explain many economic phenomena, including tax evasion. Utilizing Hofstede's (1980, 2001) cultural dimensions and data from the World Bank from 2005–2010, this study takes an exploratory approach to develop a stepwise, forward selection regression model to better illuminate this phenomenon vis-à-vis culture and economic structure. The results suggest, at least within the context of tax evasion, that cultural indices may be redundant in the presence of other readily available socio-economic indicators. This brings the age-old debate of culture versus structure to the forefront of accounting scholarship and policymaking.

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Taylor, Jonathan, and Robert J. Tibshirani. "Statistical learning and selective inference." Proceedings of the National Academy of Sciences 112, no. 25 (2015): 7629–34. http://dx.doi.org/10.1073/pnas.1507583112.

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We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

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Wang, Lu Zhuang, Zheng Yuan Wang, and Zi Shu Yuan. "Exploratory Research on Earnings Forecast Model-Based on Chinese Manufacturing Companies." Advanced Materials Research 566 (September 2012): 580–83. http://dx.doi.org/10.4028/www.scientific.net/amr.566.580.

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This paper takes China's manufacturing listed companies as an example, applies matching T test to select key financial indicators, uses factor analysis to determine the main factors, and explores company earnings forecast models by comparing different kinds of stepwise regression plans. Empirical results showed that by means of properly linear combination of key financial indicators from this year, it is possible to estimate the earning, EPS, for next year. The accuracy of the forecast can reach 80% for Top 10 profitable enterprises, and 62% for Top 100, which validated the effectiveness of this exploration for earnings forecasting. Finally, based on the prediction model, the authors put forward some suggestions for improving financial management of listed companies.

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