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We consider testing of the homogeneity hypothesis in the one-way ANOVA model and testing for the significance of regression in the multiple linear regression model. Unlike in the classical approach, there is no alternative hypothesis to accept when the null hypothesis is rejected. When there is a substantial deviation from the null hypothesis we reject the null hypothesis and also identify the independent variables or the levels that contributed most towards the deviation from the null hypothesis.

The Theory Of Linear and Non Linear Regression Analysis involves modeling of data points (of the Independent Variables and the Dependent Variable) for finding any Trends, both of Linear and Non Linear kind in them. Some of these concepts are detailed in the works mentioned in the References section.   In this research manuscript, the author has advented the comprehensive Holistic Theoretical Model For Optimal Multiple Linear and Multiple Non Linear Regression Analysis. Also, an Exhaustive Error Modeling Scheme is detailed to perfect the advented Model.

The report is about the Marketing Perception and Consumer Behavior of the customers of Chennai and all across India towards High End Cars (Benz, BMW and Audi etc.). The purpose of this projectis to enable the reader, to gather important information quickly about the WHY, WHAT, WHEN, WHO and HOW should a consumer purchase a High End Car and also study about the perception of the consumers in different levels of our society. Today's generation want to purchase cars which will showcase their class or standard of living and this does not means that the consumers who are rich or upper middle class can only dream of purchasing a High End car everyone wants and has the right to dream. The dream can be fulfilled now in this modern age where consumers have the chance of getting a car loan which will in turn help them purchase a high end car and lead a life of a king. It is all because of the modernizing concepts and themes.

The report is about the Marketing Perception and Consumer Behavior of the customers of Chennai and all across India towards High End Cars (Benz, BMW and Audi etc.). The purpose of this projectis to enable the reader, to gather important information quickly about the WHY, WHAT, WHEN, WHO and HOW should a consumer purchase a High End Car and also study about the perception of the consumers in different levels of our society. Today's generation want to purchase cars which will showcase their class or standard of living and this does not means that the consumers who are rich or upper middle class can only dream of purchasing a High End car everyone wants and has the right to dream. The dream can be fulfilled now in this modern age where consumers have the chance of getting a car loan which will in turn help them purchase a high end car and lead a life of a king. It is all because of the modernizing concepts and themes.

The report is about the Marketing Perception and Consumer Behavior of the customers of Chennai and all across India towards High End Cars (Benz, BMW and Audi etc.). The purpose of this projectis to enable the reader, to gather important information quickly about the WHY, WHAT, WHEN, WHO and HOW should a consumer purchase a High End Car and also study about the perception of the consumers in different levels of our society. Today's generation want to purchase cars which will showcase their class or standard of living and this does not means that the consumers who are rich or upper middle class can only dream of purchasing a High End car everyone wants and has the right to dream. The dream can be fulfilled now in this modern age where consumers have the chance of getting a car loan which will in turn help them purchase a high end car and lead a life of a king. It is all because of the modernizing concepts and themes.

The report is about the Marketing Perception and Consumer Behavior of the customers of Chennai and all across India towards High End Cars (Benz, BMW and Audi etc.). The purpose of this projectis to enable the reader, to gather important information quickly about the WHY, WHAT, WHEN, WHO and HOW should a consumer purchase a High End Car and also study about the perception of the consumers in different levels of our society. Today's generation want to purchase cars which will showcase their class or standard of living and this does not means that the consumers who are rich or upper middle class can only dream of purchasing a High End car everyone wants and has the right to dream. The dream can be fulfilled now in this modern age where consumers have the chance of getting a car loan which will in turn help them purchase a high end car and lead a life of a king. It is all because of the modernizing concepts and themes.

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

The Theory Of Linear and Non Linear Regression Analysis involves modeling of data points (of the Independent Variables and the Dependent Variable) for finding any Trends, both of Linear and Non Linear kind in them. Some of these concepts are detailed in the works mentioned in the References section. In this research manuscript, the author has advented the comprehensive Holistic Theoretical Model For Optimal Multiple Linear and Multiple Non Linear Regression Analysis. Also, an Exhaustive Error Modeling Scheme is detailed to perfect the advented Model.

We propose a new method of analysing a variable component for type 1 active galactic nuclei (AGNs) in the near-infrared wavelength region. This analysis uses a multiple regression technique and divides the variable component into two components originating in the accretion disk at the center of AGNs and from the dust torus that far surrounds the disk. Applying this analysis to the long-term $VHK$ monitoring data of MCG+08-11-011 that were obtained by the MAGNUM project, we found that the $(H-K)$-color temperature of the dust component is $T = 1635$K $\pm20$K, which agrees with the sublimation temperature of dust grains, and that the time delay of $K$ to $H$ variations is $\Delta t\approx 6$ days, which indicates the existence of a radial temperature gradient in the dust torus. As for the disk component, we found that the power-law spectrum of $f_\nu \propto \nu^\alpha$ in the $V$ to near-infrared $HK$ bands varies with a fixed index of $\alpha\approx -0.1$ -- +0.4, which is broadly consistent with the irradiated standard disk model. The outer part of the disk therefore extends out to a radial distance where the temperature decreases to radiate the light in the near-infrared.

This article is from BMJ Open , volume 2 . Abstract Objectives: This study investigated what clinical and sociodemographic factors affected Wisconsin Card Sorting Test (WCST) factor scores of patients with schizophrenia to evaluate parameters or items of the WCST. Design: Cross-sectional study. Setting: Patients with schizophrenia from three hospitals participated. Participants: Participants were recruited from July 2009 to August 2011. 131 Japanese patients with schizophrenia (84 men and 47 women, 43.5±13.8 years (mean±SD)) entered and completed the study. Participants were recruited in the study if they (1) met DSM-IV criteria for schizophrenia; (2) were physically healthy and (3) had no mood disorders, substance abuse, neurodevelopmental disorders, epilepsy or mental retardation. We examined their basic clinical and sociodemographic factors (sex, age, education years, age of onset, duration of illness, chlorpromazine equivalent doses and the positive and negative syndrome scale (PANSS) scores). Primary and secondary outcome measures: All patients carried out the WCST Keio version. Five indicators were calculated, including categories achieved (CA), perseverative errors in Milner (PEM) and Nelson (PEN), total errors (TE) and difficulties of maintaining set (DMS). From the principal component analysis, we identified two factors (1 and 2). We assessed the relationship between these factor scores and clinical and sociodemographic factors, using multiple logistic regression analysis. Results: Factor 1 was mainly composed of CA, PEM, PEN and TE. Factor 2 was mainly composed of DMS. The factor 1 score was affected by age, education years and the PANSS negative scale score. The factor 2 score was affected by duration of illness. Conclusions: Age, education years, PANSS negative scale score and duration of illness affected WCST factor scores in patients with schizophrenia. Using WCST factor scores may reduce the possibility of type I errors due to multiple comparisons.

In this paper, we propose the model for predicting radon concentration after 24 hours using multiple linear regression analysis. If the proposed predictive model predicts the concentration of radon after 24 hours, simple methods such as using a ventilator or opening a window can greatly reduce the concentration of radon that can be exposed. Deog-Gyeong Yoon | Jai-Won Chung | Tae-In Hyon | Gyu-Sik Kim ""Prediction of Radon Concentration after 24 Hours using Multiple Linear Regression Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30092.pdf Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/30092/prediction-of-radon-concentration-after-24-hours-using-multiple-linear-regression-analysis/deog-gyeong-yoon

In this paper, we propose the model for predicting radon concentration after 24 hours using multiple linear regression analysis. If the proposed predictive model predicts the concentration of radon after 24 hours, simple methods such as using a ventilator or opening a window can greatly reduce the concentration of radon that can be exposed. Deog-Gyeong Yoon | Jai-Won Chung | Tae-In Hyon | Gyu-Sik Kim ""Prediction of Radon Concentration after 24 Hours using Multiple Linear Regression Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30092.pdf Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/30092/prediction-of-radon-concentration-after-24-hours-using-multiple-linear-regression-analysis/deog-gyeong-yoon

In this paper, we propose the model for predicting radon concentration after 24 hours using multiple linear regression analysis. If the proposed predictive model predicts the concentration of radon after 24 hours, simple methods such as using a ventilator or opening a window can greatly reduce the concentration of radon that can be exposed. Deog-Gyeong Yoon | Jai-Won Chung | Tae-In Hyon | Gyu-Sik Kim ""Prediction of Radon Concentration after 24 Hours using Multiple Linear Regression Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30092.pdf Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/30092/prediction-of-radon-concentration-after-24-hours-using-multiple-linear-regression-analysis/deog-gyeong-yoon

In this paper, we propose the model for predicting radon concentration after 24 hours using multiple linear regression analysis. If the proposed predictive model predicts the concentration of radon after 24 hours, simple methods such as using a ventilator or opening a window can greatly reduce the concentration of radon that can be exposed. Deog-Gyeong Yoon | Jai-Won Chung | Tae-In Hyon | Gyu-Sik Kim ""Prediction of Radon Concentration after 24 Hours using Multiple Linear Regression Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30092.pdf Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/30092/prediction-of-radon-concentration-after-24-hours-using-multiple-linear-regression-analysis/deog-gyeong-yoon

Software development organization is under threats to improve software productivity and reducing expenditure through minimization of wastage of software information’s. This article presents a software development company to execute lean six sigma software project developments 

[4], 116 p. 24 cm

In order to determine more scientifically the value of property assisted by the Coast Guard in search and rescue incidents, regression analysis was conducted on various characteristics of vessels in order to estimate their fair market values. Data for this research were collected from the U.S. Maritime Administration, the U.S. Coast Guard, and numerous oil and steel companies. Mathematical models were developed for merchant ships, tugs, fishing vessels, petroleum-carrying ships, and petroleum-carrying barges. Little correlation could be found in the analysis of yachts. To estimate the value of yachts as well as numerous other varieties of boats, it is prudent to utilize a commercially developed data base. Use of the models along with the commercial data base should provide value estimates for approximately 90 percent of the future Coast Guard search and rescue incidents. The search and rescue data base for previous years cannot be corrected because of the precision required in the measurement of vessel attributes and the categorization of characteristics in the Coast Guard assistance reports.

Contains all of the files on the accompanying compact disc for  Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences ( 3rd Edition). ISBN: 0-8058-2223-2

The study aims to examine the main effects of step count, high activity minutes, and sleep duration predictors in relationship to positive and negative affect outcomes by fitting a multiple linear regression model.

In order to determine more scientifically the value of property assisted by the Coast Guard in search and rescue incidents, regression analysis was conducted on various characteristics of vessels in order to estimate their fair market values. Data for this research were collected from the U.S. Maritime Administration, the U.S. Coast Guard, and numerous oil and steel companies. Mathematical models were developed for merchant ships, tugs, fishing vessels, petroleum-carrying ships, and petroleum-carrying barges. Little correlation could be found in the analysis of yachts. To estimate the value of yachts as well as numerous other varieties of boats, it is prudent to utilize a commercially developed data base. Use of the models along with the commercial data base should provide value estimates for approximately 90% of the future Coast Guard search and rescue incidents. The search and rescue data base for previous years cannot be corrected because of the precision required in the measurement of vessel attributes and the categorization of characteristics in the Coast Guard assistance reports. (Author)

Introduction: Sleep quality and secondhand smoke are two different, complex health problems that can be life-threatening for humans. The purpose of this study was to determine the linkage between secondhand smoke exposure and poor sleep quality among non-smoker university students in Saudi Arabia.

In this paper, a proposed form of dependent regression models are introduced to study symbolic data. The estimation of the proposed linear regression models are based on interval valued data, for which we have lower and upper bounds or center and range values. The least squares method is used to estimate the models. A real example data are used to illustrate the usefulness of the proposed regression models for handling the interval valued data. The estimation results are evaluated using the predicted mean squared errors. The results support the proposed dependent regression models. 

In this work, we emphasize on the analysis of the effect of the black market in the Mining sector in the Central African Republic’s economy (GDP), using the relationship between mining quantity exported and the price divided into legal and illegal price. The data collected were analyzed and tested using Ordinary Least Square (OLS). The result of this work shown that legal price coefficient indicated for each increase of 1 percent in the legal Price, holding illegal price constant, the expected change in Product is predicted to be about 52%, this mean change in the legal price has a positive effecton the official production and statistically significant (t-value is 2.745165).However, change in illegal price has a negative effect on production (about 53% with t-value equal to 2.792635 of statistical significance). Regarding this work, in order to make mining sector more efficient, the Central African Republic’s government should try to develop other sectors such as agriculture and manufacture for diversifying sources of state revenue purpose. It should also increase the export earnings of the country throughout income from mining by strengthening the country's integration into the global economy but also by ensuring the socio-economic development of mining workers.

From the bitsavers.org collection, a scanned-in computer-related document. autonetics :: recomp :: software :: Recomp Users Library :: 1093 Stepwise Multiple Linear Regression and Correlation Analysis Sep61

Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DOSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.

See attached documents.

We aim at conducting a quantitative synthesis (meta-analysis) of the currently available real world evidence on discontinuation of disease-modifying treatments (DMTs) in patients with multiple sclerosis (MS), focusing on platform self-injectable and oral DMTs, i.e. interferon beta, glatiramer acetate, dimethyl fumarate, teriflunomide. We will explore: (1) the event rates for relapses and disability accrual after stopping platform DMTs; (2) the main determinants (moderators) for increased rates of relapse and disability accrual after stopping platform DMTs. We do not include studies about discontinuation of high-efficacy DMTs, i.e. monoclonal antibodies and sphingosine-1-phospate (S1P) receptors modulators whose discontinuation is commonly driven by safety issues and is freqeuntly associated with huge reactivation of disease activity (rebound).

http://uf.catalog.fcla.edu/uf.jsp?st=UF024487681&ix=pm&I=0&V=D&pm=1

http://uf.catalog.fcla.edu/uf.jsp?st=UF024487681&ix=pm&I=0&V=D&pm=1

Thesis (M.S. in Management)--Naval Postgraduate School, 1982

The Air Force Logistics Command currently uses a simple linear regression model to forecast overseas Second Destination Transportation (SDT) general cargo tonnage requirements for specific geographical areas. The independent variable for the model is the total flying hours for each geographical area while the dependent variable is the general cargo SDT tonnage requirement. This thesis explored the use of a multivariable approach for developing multiple regression and neural network models which was based on the breakout of the total flying hour variable into separate aircraft flying hours and the addition of military population variables. The purpose of this research was to develop multiple regression and neural network models for predicting Pacific and European Military Airlift Command and Military Sealift Command general cargo tonnage requirements that were more accurate forecasting models than the simple regression forecasting models presently used by AFLC/DSXR. Once the models were developed, the multiple regression and neural network models were compared to determine which type of model was statistically more accurate. Neural networks are an adaptive information processing system loosely based on the information processing capability of the human brain that mathematically develops associations between particular independent and dependent variables. Recent research indicates neural network models are an alternative to conventional mathematical techniques for solving problems that do not have a well defined model or theory. (KR)

The Air Force Logistics Command currently uses a simple linear regression model to forecast overseas Second Destination Transportation (SDT) general cargo tonnage requirements for specific geographical areas. The independent variable for the model is the total flying hours for each geographical area while the dependent variable is the general cargo SDT tonnage requirement. This thesis explored the use of a multivariable approach for developing multiple regression and neural network models which was based on the breakout of the total flying hour variable into separate aircraft flying hours and the addition of military population variables. The purpose of this research was to develop multiple regression and neural network models for predicting Pacific and European Military Airlift Command and Military Sealift Command general cargo tonnage requirements that were more accurate forecasting models than the simple regression forecasting models presently used by AFLC/DSXR. Once the models were developed, the multiple regression and neural network models were compared to determine which type of model was statistically more accurate. Neural networks are an adaptive information processing system loosely based on the information processing capability of the human brain that mathematically develops associations between particular independent and dependent variables. Recent research indicates neural network models are an alternative to conventional mathematical techniques for solving problems that do not have a well defined model or theory. (KR)

Bibliography: p. 5

Bibliography: p. 5

The purpose of the present study is to examine the impact of parental socioeconomic status on pupils' achievement in secondary school Mathematics. The population of the study consists of class nine students studying in different secondary and higher secondary schools of Karbi Anglong district of Assam state in India. 900 students of 30 different schools and their parents participated in the survey and responded to the questionnaires. Stepwise Multiple Regression Analysis technique is adopted to analyze collected data. The main objective of the study is to determine the relationship between parents' socioeconomic status and its impact on the scholastic attainment of secondary school students in mathematics. The result of the analysis reveals that parents' socioeconomic status has significant relationship with school learners' academic achievement in mathematics.

The purpose of the present study is to examine the impact of parental socioeconomic status on pupils' achievement in secondary school Mathematics. The population of the study consists of class nine students studying in different secondary and higher secondary schools of Karbi Anglong district of Assam state in India. 900 students of 30 different schools and their parents participated in the survey and responded to the questionnaires. Stepwise Multiple Regression Analysis technique is adopted to analyze collected data. The main objective of the study is to determine the relationship between parents' socioeconomic status and its impact on the scholastic attainment of secondary school students in mathematics. The result of the analysis reveals that parents' socioeconomic status has significant relationship with school learners' academic achievement in mathematics.

Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

FORTRAN 4 program for multiple linear regression analysis

This article is from PLoS ONE , volume 9 . Abstract Background: Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods: Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results: Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P 5 years: OR = 27.226, P

This article is from PLoS ONE , volume 9 . Abstract Background: Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods: Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results: Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P 5 years: OR = 27.226, P

This article is from PLoS ONE , volume 9 . Abstract Background: Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods: Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results: Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P 5 years: OR = 27.226, P

This article is from PLoS ONE , volume 9 . Abstract Background: Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods: Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results: Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P 5 years: OR = 27.226, P

This article is from PLoS ONE , volume 9 . Abstract Background: Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods: Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results: Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P 5 years: OR = 27.226, P

Grouping is a statistical procedure through which members of the same group are considered as a single unit of observation. There are various ways to assign group membership and various ways to assign values of variables to groups. There are methodological problems associated with grouping in general and with particular methods of grouping. This paper argues that a wide variety of complex analytical problems concerning inferences from grouped observations can be understood from the use of a few simple principles. The paper focuses on multiple regression models which use grouping and shows that the effects of grouping depend centrally on the quality of the specification of the regression model used. Simulated examples as well as examples from the literature are presented and discussed. The principles developed are then extended to more complex cases. In particular, estimation from grouped observations in systems of simultaneous equations and in dynamic models for panel analysis are examined. (Author/JKS)

Artificial neural networks (ANN) and multiple regression analysis (MRA) were used to predict the rheological properties of oil well cement slurries. The slurries were prepared using class G oil well cement with a water-cement mass ratio (w/c) of 0.44, and incorporating a new generation polycarboxylate-based high-range water reducing admixture (PCH), polycarboxlate-based mid-range water reducing admixture (PCM), and lignosulphonate-based mid-range water reducing admixture (LSM). The rheological properties were investigated at different temperatures in the range of 23 to 60ºC using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop predictive models based on back-propagation artificial neural networks and multiple regression analysis. It was found that both ANN and MRA depicted good agreement with the experimental data, with ANN achieving more accurate predictions. The developed models could effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database with an absolute error of 3.43, 3.17, and 2.82%, in the case of ANN and 4.83, 6.32, and 5.05%, in the case of MRA, for slurries incorporating PCH, PCM, and LSM, respectively. The flow curves developed using ANN and MRA allowed predicting the Bingham parameters (yield stress and plastic viscosity) of the oil well slurries with adequate accuracy.

Researchers are becoming increasingly aware of the advantages of using multiple regression as opposed to analysis of variance (ANOVA) or analysis of covariance (ANCOVA). Multiple regression is more versatile and does not force the researcher to throw away variance by categorizing intervally scaled data. Polynomial regression analysis offers the investigator a method of analyzing curvilinear relationships with relative ease through commonly available statistical packages such as the Statistical Package for the Social Sciences (SPSS). This paper explores polynomial regression as an alternative to ANOVA for exploring nonlinear relationships. Challenges in interpreting the individual coefficients produced by polynomial regression are examined. Nine tables and four figures illustrate the discussion. Appendixes A and B illustrate SPSS output. (Contains 8 references.) (Author/SLD)

This study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple-regression and supervised artificial neural network (ANN) models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success. (Contains 6 tables and 2 figures.)

The purpose of this study was to simultaneously examine relationships between teacher quality and instructional time and mathematics and science achievement of 8th grade cohorts in 18 advanced and developing economies. In addition, the study examined changes in mathematics and science performance across the two groups of economies over time using data from the TIMSS 1995-2007 assessments. While economy did not account for variation in mathematics and science achievement, findings from regression analyses indicated significant relationships between school inputs and achievement in both groups of countries across the years. Teaching experience was a strong indicator of mathematics performance in developing countries, while instructional time was mildly related to achievement in both subjects in advanced economies. (Contains 5 tables.)