Multiple Regression Analysis

Abstract— Construction cost estimation is essential for turnkey construction. The good estimate should be a fair price for both customer and construction company. This research aims to compare the cost estimates of electrical and communication system for industrial factory construction. Three forecasting methods compared in this research are Multiple Regression Analysis (MRA), Multiple Regression Analysis incorporating Genetic Algorithm (MRA-GA), and Neural Network (NN). The data sets are collected from 31 industrial factory projects constructed in Thailand between year 2005 and 2011 which are divided into 25 training data sets and 6 testing data sets. The selected input variables are area, cost percentage from copper, cost percentage from main equipment, cost percentage from labor, and air condition system. The results show that MRA-GA model provides slightly lower root mean squared error (RMSE) than MRA and NN models.

Finite element software, ANSYS version 14 is used for free vibration analysis of the crack free and cracked beams. Beam length, thickness and depth are along X axis, Y axis and Z axis respectively in ANSYS coordinate system. A 20- node three dimension structural solid element under SOLID 186 was selected to model the beam because it is suitable for all structural analysis and it is mid node element which gives the more accurate result. Fig shows finite element model of a cracked beam. The modal analysis of cracked and crack free beams are performed. The Block Lanczons mode extraction method is used to calculate the natural frequencies of the beam. The corresponding mode shapes for both cracked and cracked free beam are also captured. The displacement values are used as a numerical data of ANN and Multiple Regression Analysis tool for subsequent identification of crack length and location of the beam.

Multiple regression analysis is usually used to determine the relationship of two or more independent variables. Correlation between rainfall and climate variables plays an important role in order to build a good model for rainfall prediction. There are 3 methods used in multiple regression analysis which are the backward elimination, forward selection and stepwise. The results of these 3 methods are compared to determine which method is the best. The data obtained from Malaysia Meteorological Servicesare analysed by using Microsoft Excel and SPSS 22.0. In this study, the data analysed were in the form of daily data and monthly data.

competition may be the reasons that attract practitioners to select from a dedicated list of promising suppliers. Most of the previous studies on this subject have concentrated on the selection of either the criteria or methods used to choose the right supplier(s). This paper also addresses these two issues. It focuses on the methodology of selecting the right supplier(s) from a list of suppliers. Criteria have been chosen in line with the requirements of the firm and a multiple regression analysis has been used as a statistical tool to choose the right supplier(s). Here, criteria have been translated into three different indexes from different perspectives, and ultimately supplier selection is based on the index values and their interrelationships. This is the addition to the current state of knowledge in which suppliers'

Microfinance Institutions (MFIs) are those institutions which are providing microfinance services such as savings, credit, insurance and remittance services to poor. The study aims at analyzing the financial performance of MFIs in Bangladesh by employing multiple regression analysis. The data have been collected from Microfinance Information Exchange (MIX) from the fiscal year 2007 to 2011. The statistical tools numerical scoring and multiple regression analysis have been used for analyzing the data.It is found that the variables, namely, debt to equity ratio, gross loan portfolio to total assets, number of active borrowers, return on assets, operational self-sufficiency, financial revenue/assets, profit margin, operating expense/assets, operating expense/loan portfolio, average salary/GNI per capita,loans per staff member, personnel allocation ratio, PAR > 90 days and risk coveragehave been found to be the key drivers of the overall performance of MFIs in Bangladesh

Abstract. The aim of this article is to forecast anemia from a population through biomedical variables of individuals using the multiple linear regression model. The study is conducted in terms of dataset consisting of 539 subjects provided from blood laboratories. A multiple linear regression model is produced through biomedical information. To achieve this, a mathematical method based on multiple regression analysis has been applied in this research for a reliable model that investigate if there exists a relation between the anemia and the biomedical variables and to provide the more realistic one. For comparison purposes, the linear deep learning methods have also been considered and the current results are seen to be slightly better. The model based on the variables and outcomes is expected to serve as a good indicator of disease diagnosis for health providers and planning treatment schedules for their patients, especially predict of the type of anemia.

This value is much smaller than any of the data values, indicating that this model accurately follows the data. The results of these multiple regression analysis are summarized by the following newly proposed mathematical model:

Regression analysis is one of the most widely used statistical techniques. Today, regression analysis is applied in the social sciences, medical research, economics, agriculture, biology, meteorology, marketing, retail, insurance and many other areas of academic and applied science. It is not only suited to suggesting decisions as to whether or not a relationship between two variables exists. It goes beyond this decision making and provides a different type of precise statement. Regression analysis specifies a functional form for the relationship between the variables under study that allows one to estimate the degree of change in the dependent variable that goes hand in hand with changes in the independent variable. At the same time, regression analysis allows one to make statements about how certain one can be about the predicted change in Y that is associated with the observed change in X. The main objective of the present study is to investigate factors that contribute significantly to estimate the selling price of a house in a locality. The dependent variable is Average house selling price, the multiple regression analysis applied for exploring the factors affecting the house

Talking about property right, there are two distinct mean ings: economic property rights (Besley & Ghatak, 2010) and lega l property rights (Meinzen -Dick & Pradhan, 2002). The economic property rights of an individual over a commodity or an asset are the individual's capacity, in te rms of anticipation, to consume the good or the services of the asset directly or to consume it indirect ly through trade. These can include the right to use an asset, the right to earn income fro m an asset and contract over the terms with other individuals, and the right to transfer ownership rights permanently to another party. The legal property rights are those that are recognized and enforced by the government. In the case of our analysis the real property right is highlighted. In the other hands the natural resources’ right is characterized by the right of using a piece of land defined by boundaries to which ownership is usually ascribed, including any improvements on this land. In the case of our analysis, the land can be represented by the mines. As we see in previous chapter, in CA R mines are owned by the state (Govern ment). Property right is one of the princ ipal characteristics of Institutional Economics.

In this model, R² value for the first stage of analysis re- gression model is 0.967 (refer to Table 2 ), which means that the influencing factors explain 97 per cent of the variance in the urban services growth. Standard multiple regression also provides an adjusted R² value. The ad- justed R² value in this model was 0.966, indicating a pretty well fitness of the model.

between these two methods is that in the latter, the variables are entered for examination at each step for entry and removal analysis. However, in the former method, all of the variables are entered in a single step and the predicted model will usually include all of the variables (unless a variable is below the tolerance criterion - 0.0001) [2]. Selection between ‘‘enter’’ and ‘‘stepwise’’ models is based on values of t-statistic, F-statistic, and minimization of mulitcollinearity. Usually stepwise method provides robust models by including most of the significant factors. Regression model for preparation activities

The government and local agencies had been known to monitor the water level manually by putting measurements in certain areas under the bridge to determine the volume of water. From time to time, a representative has to go to these danger zones to check any changes in the water level. Delays in broadcasting may result to human errors in the analysis and forecast which sometimes caused alarm. These manual staff gages and flood markers used to measure the water level, which were painted on bridge’s pier, walls and posts lack durability since they will usually fade in a short span of time.

Tang et al. (2003) have used adapted time-series, neural network, nonparametric regression, and Gaussian maximum methods in order to develop models for traffic volume forecasting by day of the week, by month and AADT for the entire year 1999. The research has been completed using traffic data for the period 1994-1998 in Hong Kong [15]. Duddu and Pulugurtha (2013) have developed a model using statistical methods and ANN taking into account demographic principles in order to estimate link-level AADT based on characteristics of the land use, in the city of Charlotte, North Carolina [16].

A multiple linear regression model is shown in Eq. (1), and its least-squares solution is given by Eq. (2), where X T X is singular because of the variables in X exceeds the number of observations or the collinearities. In order to elude the singularity of X T X, the principal component regression (PCR) decomposes X into orthogonal scores T and loadings P, as shown in Eq. (3). As such, regressing Y does not only depend on X itself but also the first a columns of scores T. In the principal component regression, the scores are present by the left singular vector of X multiplied with the corre- sponding singular values, while the loadings are shown the right singular vectors of X. In PCR, the X matrix consists of the first a principal components (PCs), usually obtained from

A risk is defined as the potential for complications and problems with respect to the completion of a project and the achievement of a project goal. The aim of the risk assessment is to identify hazards, after which it may be possible to treat risk, thereby preventing them. Regression analysis using SPSS and fuzzy analysis using MATLAB were used in this study for developing the models.

Based on the information contained in Table- 2(A), we conclude that model 2 to 9 gives R2>0.6 and in Table-2(B) model 4 to 9 shows significant R2.On comparing observations in Table-[r]

So far we have tested hypotheses in which one parameter, or a subset of parameters of the model, is different for two groups (women and men, for example). But sometimes we wish to test the null hypothesis that two groups have the same population regression function, against the alternative that it is not the same. In other words, we want to test whether the same equation is valid for the two groups. There are two procedures for this: using dummy variables and running separate regressions through the Chow test.

Among many different modeling approaches, time series technique is an alternative tool applying to analyze the relationships between different water quality indexes and to predict some unknown parameters. The method is widely used in in other academic research fields, such as economics, hydrology, and biology (Renard 2007; Maiti and Tiwari 2014; Seeboonruang 2014). Another easy method is statistical-based and called multiple linear regression technique. The method is relatively straightforward and less resource consuming. Multiple linear regression modeling has been applied for predicting water quality indexes (Joarder et al. 2008; Agarwal and Agarwal 2013). Hence, this multiple linear regression technique will be applied in this study.

Quantile Regression. An important point to remember about the OLS estimates is that they are designed to represent the best overall estimate for all students, and therefore are most representative of students with the average level of reading comprehension. A critical innovation of this study was to determine whether these relationships differed depending on children’s reading comprehension ability. To do this, we used quantile regression analysis to examine how each construct was related to reading comprehension individually at different quantiles, and how constructs were uniquely related to reading comprehension while controlling for the influences of the others. Our questions are well suited to quantile regression, as this technique allows for the estimation of relations between a dependent and independent variable at multiple locations (i.e., quantiles) of the dependent variable. Quantile regression calculates the strength of these relations without creating subgroups (which would violate the normality assumption of OLS regression). Rather, it uses every observation when estimating the relations at a given point in the

Based on lane changing rules of cellular automata traffic flow model constructed in Problem Two, it applies to the highway road, no matter the vehicle is on the left or right. The obvious difference is the body's physiological factors. As is known to all, when we are running, we are used to doing it counterclockwise, this is because the clockwise running can cause oppression to the heart, and is not conducive to human health. Similarly, when vehicles on the left lane change the lane, it will produce a part of clockwise driving. Because of inertia, the heart will be on right avertence. In order to optimize this, we design the approximate "S" shape curve of highway, as shown in figure 2. We mainly consider the influence of the radius of curvature, steering angle and road friction coefficient on the car when it drives on the curve. Through the establishment of multiple regression analysis model, we study the common effect of several factors on the curve driving[4].