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The purpose of this article was to introduce the basic concepts, calculation methods, two examples and SAS implementation related to the multivariate multiple linear regression analysis. Basic concepts included multivariate multiple regression analysis, ordinary least squares, partial least squares, principal component analysis and canonical correlation analysis. The calculation methods involved data preparation and implementation steps. The data in the two examples were "measurement results of body morphology indicators and functional indicators of Han male students" and "measurement results of physical shape and health status indicators of two groups of subjects". With the help of SAS software, the multivariate multiple linear regression analysis was carried out on the data in the two examples, and an explanation was made for the output results of SAS. [ABSTRACT FROM AUTHOR]

Several generalizations of the logistic distribution and certain related models are proposed by many authors for modeling various random phenomena such as those encountered in data engineering, pattern recognition, and reliability assessment studies. In this paper, we study generalized q-logistic (GqL) distribution, in which the additional parameters offered increase flexibility of the distribution for modeling purposes. Since the parameter estimation of GqL model is not explored yet, in the present study, we propose different methods for estimating the GqL parameters. In particular, we have made a comprehensive comparison through simulation study, the performance of the maximum likelihood estimation (both complete and censored), maximum product spacing estimation and ordinary least squares estimation methods. Some characterization theorems are obtained and some properties of GqL are established. Finally, we analyze a real data to illustrate the potentiality of the model and also present some graphical illustrations. [ABSTRACT FROM AUTHOR]

Background. The problem of finding new structural specifications of regression models with interesting interpretive properties is currently relevant. The purpose of the study is to formalize a new structural specification,based on a symbiosis of previously proposed non-elementary and modular linear regressions, to develop an algorithm for its approximate estimation using the ordinary least squares method and to demonstrate its effectiveness using the example of modeling the consumer price index in the Altai Republic. Materials and methods. To estimate the regression models, the ordinary least squares method was used in combination with the «all possible regressions» method. Results. A new structural specification of regression models is formulated – non-elementary modular linear regression, which generalizes many well-known models. An algorithm for its approximate estimation is proposed. The non-elementary modular linear regression constructed using it turned out to be 39.3 % better in terms of the approximation quality of non-elementary regression without modules, and 63.7 % better than linear regression. Conclusions. Using the proposed models of non-elementary modular linear regression, one can successfully solve forecasting problems, as well as the problem of identifying new knowledge about the object of study.

This research aims to analyze the spillover effect of Mataram City's growth center on the economic growth rate of the hinterland region, specifically Lombok Barat Regency. The independent variables include domestic investment in Mataram City (X1) and Mataram City's economic growth rate (X2), while the dependent variable is the economic growth rate of Lombok Barat Regency (Y). The study utilizes time-series secondary data from publications by the Central Statistics Agency (BPS) and other relevant agencies in Nusa Tenggara Barat, covering the period from 2010 to 2022. The Ordinary Least Squares (OLS) method is employed for analysis. The findings reveal that the independent variables in this study account for 98.50 percent of the variance in the dependent variable, while the remaining 1.50 percent is influenced by factors outside the estimation model. On a partial basis, domestic investment in Mataram City does not have a significant effect on the economic growth rate of Lombok Barat Regency, indicating that this variable does not contribute to a positive economic spillover effect. However, the economic growth rate of Mataram City has a positive and significant impact on the economic growth of Lombok Barat Regency, suggesting that it contributes a positive spillover effect. Simultaneously, the combined influence of domestic investment and Mataram City's economic growth rate has a positive spillover effect on Lombok Barat Regency's economy. [ABSTRACT FROM AUTHOR]

The Covid-19 outbreak, which caused the world to go out of the usual order and deeply affected the world, caused many people to contract the Sars-CoV-2 virus and even lost their lives due to this virus. The fact that a virus that threatens human life spreads faster in densely populated areas such as cities has brought along many measures. Measures such as imposing a person limit on public transportation systems, which have an essential share in enabling urbanites to participate in socio-cultural activities on their compulsory journeys, abandoning the current working system in all public/private institutions and switching to flexible, remote or rotational working systems have been implemented. These measures, implemented to break the spread of the virus, have moved the daily routine of the urbanites between four walls and restricted their mobility. Within the scope of this study, the attitude of the city dwellers towards the public transportation system and vaccination efforts against the pandemic during the pandemic period was examined with a case study. In this study conducted in the context of Süleymanpaşa, the central district of Tekirdağ, public transit passenger data, Covid-19 patient count data and vaccination data between January 2021 and December 2021 were used. In this framework, a prediction model was developed with the Least Squares Method for a medium-sized city. In the model developed against future pandemics, the impact of the number of patients and vaccination on public transportation use was revealed. In addition to the effect of the number of patients on the public transportation system, an original contribution to the existing literature is presented by including vaccination as an input to the model. [ABSTRACT FROM AUTHOR]

Boreal forest and wetland have important influences on the development and protection of the ecosystem‐dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi‐period land use/land cover (LULC) data and long‐time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025°C per yr) and extents of permafrost showed a decreasing trend (−3668 km2yr−1) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 × 104 and 3.8754 × 104 km2, respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km2 from 1980s to 2010s. Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China. [ABSTRACT FROM AUTHOR]

Random set theory is concerned with the development of a coherent mathematical framework for studying random objects whose realizations are sets, where the object of economic and statistical interest is a set rather than a point. In this paper we discuss the random intervals which are random sets, and to use Karush-Kuhn-Tucker conditions and maximum likelihood Function for estimating the parameters of a multiple linear regression model, whose dependant variable is unobserved and belongs to a random interval. [ABSTRACT FROM AUTHOR]

This study investigates the interrelation between energy and economic growth for USA covering the time period from 1975 to 2017. Ordinary least squares method and the essential diagnostic tests are applied in order to examine the possible existence of autocorrelation, specification, heteroscedasticity and normality tests for residuals of estimated equation model. The empirical results of this study indicated that there is a linear and positive relationship between energy and economic growth for USA.

This paper aims to propose estimation methods for one-inflated positive Poisson (OIPP) distribution and compare their properties in terms of unbiasedness, consistency, efficiency and deficiency. All estimators considered in the study are asymptotically unbiased and consistent. The maximum likelihood estimator (MLE) for the OIPP distribution is asymptotically normal. When compared to the MLE, the ordinary least square estimator (OLSE) is the most efficient, followed by the method of moments estimator (MME) and the ratio of probability estimator (RPE). A novel one-inflation index was also proposed to assess the presence of excess ones in the dataset for the positive Poisson distribution to determine whether a one-inflated distribution is required for model fitting. A real dataset with a large number of ones, as identified by the proposed one-inflation index, was used for model fitting. It is found that the OLSE and MLE are the best estimators for an OIPP distribution.

In this article, the problem of estimating the parameter of one of the recently introduced lifetime distributions known as the Pranav distribution is studied. The estimation of the scale parameter was addressed using the moments method, maximum likelihood method and the proposal to use the ordinary least squares method for the purpose of estimating the scale parameter of this distribution. The simulation results showed for different sample sizes and default values for the parameter, the proposed estimator was much better than the performance of other estimators, as in the most expensive cases the mean square error value of the proposed parameter using ordinary least squares method was less than its value for the parameters obtained using other methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Finance is the property of Academic Finance Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to propose estimation methods for one-inflated positive Poisson (OIPP) distribution and compare their properties in terms of unbiasedness, consistency, efficiency and deficiency. All estimators considered in the study are asymptotically unbiased and consistent. The maximum likelihood estimator (MLE) for the OIPP distribution is asymptotically normal. When compared to the MLE, the ordinary least square estimator (OLSE) is the most efficient, followed by the method of moments estimator (MME) and the ratio of probability estimator (RPE). A novel one-inflation index was also proposed to assess the presence of excess ones in the dataset for the positive Poisson distribution to determine whether a one-inflated distribution is required for model fitting. A real dataset with a large number of ones, as identified by the proposed one-inflation index, was used for model fitting. It is found that the OLSE and MLE are the best estimators for an OIPP distribution. [ABSTRACT FROM AUTHOR]

Copyright of Science-Based Technologies is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Administration & Economics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Effect of Underground Economy on Importing Medicine in Iran Jalaee Seyed Abdolmajid1,Dowlatzadeh Anita2*, Sattari Omid 3,Nadjafi Behzad4 Received: 15. 11. 2015 Revised: 03. 02. 2016 Accepted: 21. 02. 2016 1.Professor, Department of Economics, School of Management and Economics, Shahid Bahonar University, Kerman, Iran 2.MSc, School of Management and Economics, Shahid Bahonar University,Kerman, Iran. 3.PhD Student, Department of Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran. 4. Assistant Professor, Expert Office Budget Ministry of Health and Medical Education, Tehran, Iran. *Correspondence: Building 4,Alley 15, Eastern 4, Jumhori Blv., Infront of EMS,Kerman. Tel: 09137134187 Email: anitadolatzadeh@yahoo.com Abstract Introduction:Pharmaceutical Products as strategic commodities have direct relationship with public health status. Policy making in this sector strongly depends on determination of different factors that affect medicine production, consumption and imports. According to economic literature, like other commodities, medicine imports are affected by exchange rate and grass national product (GNP). One of the most important factors affecting medicine import is related underground economic activities like medicine smuggling and informal markets. Method:In this study, through benefiting from fuzzy logic approach and using Matlab 2012 software, the size of underground economy in Iran was estimated. Then, by specifying an import model for pharmaceutical products, the effect of underground economy on medicine import demand in Iran during 1971-2011 was determined using ordinary least squares (OLS) method in Eviews6. Results:Share of underground economy on grass domestic product (GDP) during this period has been swingy and the effect of underground economy on medicine imports has been negative but non-significant. Conclusion:Despite the necessity of considering medicine smuggling and informal markets in health sector, this important factor has been neglected in Iran policymaking processes and decision makings for medicine import. Key­words:Underground economy, Medicine import demand, Fuzzy logic approach, Ordinary least squares method Citation: Jalaee SA, Dowlatzadeh A, Sattari O, Nadjafi B.The Effect of Underground Economy on Importing Medicine in Iran. Journal of Health Based Research 2016; 1(3): 255-265.

Constraints related to the technical and socioeconomic aspects of adopting tomato varieties are well documented. However, preferences relating to the demand for this fruit are rarely studied. In fact, demand for the different varieties of tomatoes might be related to consumers' preferences for the extrinsic (color, size) and intrinsic (taste, nutritive value, water content) characteristics specific to each variety. It is therefore indispensable to determine the characteristics of tomatoes preferred by consumers. We examine consumers' preferences for tomato attributes among a sample of 600 consumers in four representative markets in the main cities in southern Benin. The study also identifies potential segments of consumers and the market shares of each profile for each segment. The study identifies four potential tomato market segments, two of which have a strong preference for local tomatoes, namely, those that can be conserved for long periods and those with a firm consistency. [ABSTRACT FROM AUTHOR]

Copyright of Educación y Humanismo is the property of Universidad Simon Bolivar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this report we examine predictive control of the systems with incompletely observable control coordinate. The scientific and methodical approach to algorithm selection for the predictive model is represented. The article contains the analysis of prediction methods based on the static processing of object state data. We present research results concerning the precision of methods of algorithm specification based on interpolation polynomials and ordinary least squares approximation of data. [ABSTRACT FROM AUTHOR]

The study aims to explain of stock prices the behavior in the New York Stock Exchange during the period from 1961 to 2014, by studying the effect of a series of Bourses variables on prices by building standard models to explain price behavior. The study found out that there is a strong positive correlation between the Bourses variables and stock prices during the study period, except for the turnover rate, which is linked to the average strength of stock prices compared to other variables. The study also concluded that the model which includes both volume and number of institutions As explanatory variables is the best explanation for the behavior of stock prices on the New York Stock Exchange during the study period. [ABSTRACT FROM AUTHOR]

A new approach is proposed to comparing ordinary least squares and least absolute value methods by estimating the relative efficiency of sample mean and median. The results confirm the previous conclusions concerning the relative efficiency of these methods for estimating the location parameters, which have been obtained by other authors using more time-consuming techniques. The proposed approach is applied to the common cases of normal and Laplace error distributions, but it can be extended also to other distributions. [ABSTRACT FROM AUTHOR]

The estimation of the informal economy is a complex and difficult task, especially for countries during transition. Various methods applied to assess it give, not for the few times, quite different values. The methods applied in this paper emphasize the differences between groups of countries in transition in relation to aspects of the informal economy. [ABSTRACT FROM AUTHOR]

Abstract: A detailed procedure for testing linearity of calibration curves in method validation by the ordinary least squares method (OLSM), including experimental design, estimation of the parameters, outlier treatment and evaluation of the assumptions, was proposed. The theoretical background was discussed and the assumptions considered were: (i) normality, homoscedasticity and independency of residuals and (ii) adjustment to the correct model. The procedure involved precise statistical techniques that were easily carried out by any commercial spreadsheet software. The suitability of the procedure for assessing linearity was demonstrated by the application in food analysis. [Copyright &y& Elsevier]

Linear regression of calibration lines passing through the origin was investigated for three models of y-direction random errors: normally distributed errors with an invariable standard deviation (SD) and log normally and normally distributed errors with an invariable relative standard deviation (RSD). The weighted (weighting factor is x2 i), geometric and arithmetic means of the ratios y i/ x i estimate the calibration slope for these models, respectively. Regression of the calibration lines with errors in both directions was also studied. The x-direction errors were assumed to be normally distributed random errors with either an invariable SD or invariable RSD, both combined with a constant relative systematic error. The random errors disperse the true, unknown x-values about the plotted, demanded x-values, which are shifted by the constant relative systematic error. The systematic error biases the slope estimate while the random errors do not. They only increase automatically the slope estimate uncertainty, in which the uncertainty component reflecting the range of the possible values of the systematic error must be additionally included. [ABSTRACT FROM AUTHOR]