In this study, two pairs of OEM brake disc-pads have been used. One of these discs belongs to a passenger car, and the other one belongs to a light commercial vehicle. The disc-pad pair of the passenger car (PC) has been subjected to global brake effectiveness test by full scale inertia dynamometer according to SAE J2522 test standard; and the other one has been subjected to the tests by full-scale inertia dynamometer according to FIAT 7-H4020 and 7-H2000 standards. During these tests, 13 variables for passenger car disc-pad pair and 11 variables for light commercial vehicle disc-pad pair have been measured and recorded. Interrelation of the parameters has been analyzed with Multiple regression method and importance levels have been determined. In this study, dependent variables in Multiple regression method are selected as braking time, friction coefficient, disc final temperature, brake speed and brake pressure for each braking pair. In Multiple regression analysis for PC, for each unit increase in deceleration and friction coefficient, braking time decreases with 7. 3 and 60. 9 units, respectively. Also, for each unit increase in brake pressure and friction coefficient for LCV, braking time increases with 1. 267 and 91. 887 units, respectively.

In recent years, landslides occurrence has been increased in different regions of Iran because of different agents, especially road construction. Therefore, inspection of this phenomenon including effective agents in landslide occurrence, determination of susceptible area … is necessary to prevent the landslides formation. For this purpose, the Ilam dam basin having 476 (km2) area was selected for investigations. At the beginning of this research, after field investigations, seven factors including: geology, slope, aspect, fault, drainage network and land use were recognized as primary effective parameters in landslide occurrence. In the next stage, the information layers of above parameters were prepared in Geographic Information System (GIS) environment using Arc view 3.2 software and these maps were overlaid with the landslide inventory map. Analysis of the results indicated that aspect factor has minimum effect on landslide occurrence in the area. Then, using the six remainder factors, homogeneous units map is prepared. In order to quantify the factors and give an appropriate weight to the parameters, the slide area percentage in each unit was calculated. Multiple regressions were used for statistical analysis. The distance from faults and land use parameters were put aside by the model. The results showed that the four factors including road, slop, drainage network and geology have the highest affect on landslide occurrence respectively.

In a Multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in Multiple linear regression to make the computations more efficient. By resorting to an initial solution, we first employ the Sherman-Morrison formula to update the inverse of the transpose of the design matrix multiplied by the design matrix. We then modify the calculation of the product of the transpose of design matrix and the design matrix by the Cholesky decomposition method to solve the system. Finally, we compare these two modifications by several appropriate examples.

One of the important parameters in the design of flood control structures is to determine flood peak discharge for various return periods. A primary issue of planners in the face with flood is lack of data or insufficient data. One of the most reliable strategies is generalizing the results from sites with observed data to ungauged locations. The main goal of this study is regional flood frequency analysis using Multiple regression method for Qazvin province of Iran.8 out of 23 existing hydrometric station were removed because of the short-term statistics and construction of storage dam at upstream. The results of factor analysis showed that perimeter, equivalent diameter, time of concentration, length of main waterway and area were the main variables affecting lfood magnitude. The remaining 15 stations were divided into two homogenous regions using cluster analysis. Homogeneity of these two regions was confirmed using homogeneity and heterogeneity tests of L-moments. Based on the best-fit criteria of Zdist, GNO distribution with the statistic of 0.29 has the best fit for the entire region but for one and two homogeneous regions, GLO and GPA distributions with the statistics equal to 0.09 & 1.56 have the best fit respectively. After calculating parameter values for selected distributions, discharges with different return periods were estimated for all stations. Then, regression relations were obtained between peak discharge and factors affecting flood peak for each return periods at two homogeneous regions. Peak discharges at ungauged locations can be estimated for different recurrence interval using these relationships.

Estimating the required propulsive power of a vessel at early stage of design is important from two aspects, which are estimating the weight of the machinery as a function of required power and estimating the amount of fuel needed to determine the initial capacity of the vessel. This approximation can be based on empirical formulas, data of similar ships or diagrams deduced from statistical data for various types and sizes of ships. Current empirical formulas for estimating ship power are only limited to very few general equations and are inaccurate for specific type of vessels. In this study, by using 90 container ship data and Multiple linear regression method, several statistical models were prepared and statistically analyzed considering the influence of the main parameters of the vessels on the required amount of power, and finally a suitable formula extracted. Evaluation of the extracted formula indicates acceptable accuracy for the container ships which can be used to estimate the required power of container ships within the range of this formula.

Quantitative structure-retention relationships (QSRRs) are used to correlate paper chromatographic retention factors of disperse dyes with theoretical molecular descriptors. A data set of 23 compounds with known RF values was used. The genetic algorithm-Multiple linear regression analysis (GA-MLR) with three selected theoretical descriptors was obtained. The stability and predictability of the model was validated by use of leave-one-out (LOO), leavemany-out (LMO) cross-validation, external validation, Y-randomization and applicability domain (AD) analysis. The GA-MLR revealed a statistically meaningful model showing the dependence of the RF value on sum of topological distances between N and Br atoms (T(N. . Br)), global topological charge index (JGT) and R autocorrelation of lag 5 / unweighted (R5u_A) of the compounds. Prog. Color Colorants Coat. 9 (2016), 195-206© Institute for Color Science and Technology.

In this paper, we consider the issue of data classification in which the response (dependent) variable is two (or multi) valued and the predictor (independent) variables are ordinary variables. The errors could be nonprecise and random. In this case, the response variable is also a fuzzy random variable. Based on this and logistic regression, we formulate a model and find the estimation of the coefficients using the least squares method. We will describe the results with an example of one independent random variable. Finally, we provide recurrence relations for the estimation of parameters. This relation can be used in machine learning and big data classification.