Regression Models

The construction project management is about quality, timely, and costly managing construction inputs and outputs. However, its achievement continues to present a major challenge to most large construction projects all over the world due to the high degrees of complexity and variability. For instance, delivery of construction projects has been adversely influenced by several drastic risks. A major and critical concern in the time scheduling process is the accurate determination of activities durations. This calls for a continuous improvement in the conventional techniques of estimating activity duration and in utilizing the deterministic scheduling.
This thesis presents a comprehensive analysis of activities duration of construction projects in the Middle East countries. The analysis was carried out on twenty construction projects including more than 125,000 activities with a total budget of about fifteen billions Egyptian Pounds (EGP) and comprising all engineering, procurement and construction (EPC) activities. The thesis seeks to bridge the gap between theory and application and to utilize      a probabilistic scheduling approach that yields reliable estimations of activity duration. The focus is to evaluate the actual behaviour of activities exposed to different types of risk and uncertainty and to derive regression models predicting activities’ durations categorized according to country, type of work, and to the Construction Specifications Institute (CSI) divisions.
First, previous research on the causes of delay and the probabilistic analysis of activities in developing and developed countries has been compiled and critically reviewed. This review indicated that there is no generally accepted Probability Density Function (PDF) to represent the activity duration through the simulation process. However, Beta and Triangular distributions were the mostly used PDF’s.
Then, activity and project durations of different projects such as hospitals, hotels, stadiums, highways, malls, and high rise buildings in Egypt, Kuwait, Saudi Arabia, UAE, Qatar and Oman were analysed. The analysis considered the estimated durations, the actual durations, and the budgets of the EPC activities. It also included the quantitative analysis of causes of delay on the performance of seven repetitive construction activities in a housing project that is composed of 1271 similar houses in Kuwait. Besides, statistical analysis and curve fitting of the estimated versus the actual durations of activities were conducted. In addition, the study developed predictive regression models to estimate the statistics of activities durations. Finally, a special program that uses the concluded PDF and regression models to predict the project’s overall durations at different levels of confidence was developed.
It is found that the three most influencing risks on the time performance of construction activities in Kuwait are: (1) inadequate planning and time scheduling; (2) slow financial and payment procedures; and (3) fluctuation of productivity levels. In addition, it is found that the best PDF to represent activities in construction projects is the lognormal distribution, while the second best PDF is the gamma distribution. Besides, it is shown that the normal distribution has the lowest level of confidence. Furthermore, it is found that as the estimated duration decreases, the relative duration slippage increases. The ratio of the highest, actual activity duration to the corresponding mean duration is always more than 4 for projects in the Middle East, which differs from that suggested in previous studies (1.25). As a general rule, the forecasted project’s overall duration- based on the proposed regression models and utilizing lognormal distribution- tends to be greater than that calculated using conventionally estimated activities. Nevertheless, validation tests showed that the forecasted projects’ durations are good estimates of the real ones. Consequently, these forecasted projects’ durations could oblige contractors to implement different methodologies and alternative plans to reduce the project’s duration.

Evaporation is an essential component of hydrological cycle. Several meteorological factors play role in the amount of pan evaporation. These factors are often related to each other. In this study, a multiple linear regression (MLR) in conjunction with Principal Component Analysis (PCA) was used for modeling of pan evaporation. After the standardization of the variables, independent components were obtained using the (PCA). The series of principal component scores were used as input in multiple linear regression models. This method was applied to four stations in East Azerbaijan Province in the North West of Iran. Mathematical models of pan evaporation were derived for each station. The results showed that the first three components in all four stations account for more than 90% of the data variance. Performance criteria, namely coefficient of determination (R 2) and root mean square error (RMSE), were calculated for models in each station. The results showed that in all the PCA-MLR models, the R 2 value was greater than 0.74 (significant at the 5% level) and the RMSE was less than 0.52 mm per day. In general, the results showed an improvement in the results using combination of PCA and MLR models for pan evaporation estimation.

The purpose of this article is to estimate the purchasing and sale opportunities of houses on the market by Machine learning techniques. For financial stability, the housing sector is quite critical. People seeking to purchase a new house appear to be more cautious in their expectations and sales tactics analyzing historical industry patterns and pricing levels, as well as potential changes. The index of our method consists of the price of the house and its efficiency metrics, such as the amount of renovation, the distance from the city center, the construction programs, the height of the property, the floor and the location of the apartment in the home, and there are several other criteria. Service includes a database that recognizes the preferences of its clients and then integrates machine learning software. The program will enable consumers invest in real estate without approaching brokers. It therefore reduces the uncertainties inherent with the deal. The program has a login ID and a pin. At the same time, when the user searches for an attribute, the value of the original attribute and the value of the predicted attribute are displayed.

Background/objectives: When performing activity associated with walking, the amount of walking a person does often will depend on their plans. This study was designed to evaluate the relationship between motor planning and ambulatory persistence in participants with Parkinson's disease (PD) and to see if ambulatory persistence was related to the ability to perform activities of daily living (ADL). Methods: 20 individuals with idiopathic PD were recruited to perform the Trail making Test (a test of motor planning) and to wear a step activity monitor for 48 h. The measurement of persistence of an ambulatory event consisted of the number of steps taken during an event and an ambulatory event was defined as continuous ambulation (taking step) without pausing for 3 or more seconds. The resumption of taking step (ambulation) after 3 or more seconds counted as a new ambulatory event. UPDRS-motor and ADL scale were also obtained. Analysis and results: The cumulative percentage of the total ambulatory events at each number of steps was plotted for each subject which when plotted could be described as a sigmoid curve. We found that this sigmoidal curve defined by the equation y = xn/(kn + xn), fit the data well, where k represents a constant specific to each subject, x represents the number of steps during each ambulatory event, and y represents the projected percentage of movement events containing x number of steps or less. (Root Mean Square Error (RMSE) = 0.02, R2 = 0.98). Trail making test part A was highly associated with the constant k (R = −0.74, p < 0.001). The constant k was also highly associated with the UPDRS ADL subscale (R = −0.81, p = 0.0001). A forward bivariate regression model including Part A of the Trail making test, and the UPDRS-ADL subscale predicted 66% of the variability of the constant k. The overall number of steps taken per day, and the UPDRS motor subscale did not contribute to the model. Conclusions: Defective motor planning in Parkinson's disease as measured by poor performance on a Trail making test is associated with a measurable alteration in ambulatory persistence, and altered ambulatory persistence, quantified by our proposed model parameter, correlates highly with the UPDRS ADL score. Thus, cognitive-motor planning defects might be a major source of disability in PD. We suggest that in future clinical practice gait tests can be used in order to quantify short-term planning ability in neurodegenerative diseases.

This research is aimed to examine effect of return on equity, debt to equity ratio, and cash ratio on dividend policy. Dividend policy measures by list of High Dividend 20 Index of Indonesian Stock Exchange. Research samples are 308 non banking and finance companies with positive earnings and equity. Based on logistic regression analysis, result shows that return on equity, debt to equity ratio, and cash ratio have effect on dividend policy. Keywords-Cash ratio; debt to equity ratio; dividend policy; high dividend 20 index; return on equity.

Solar energy applications require readily available, site-oriented, and long-term solar data. However, the frequent unavailability of diffuse irradiation, in contrast to its need, has led to the evolution of various regression models to predict it from the more commonly available data. Estimating the diffuse component from global radiation is one such technique. The present work focuses on improvement in the accuracy of the models for predicting horizontal diffuse irradiation using hourly solar radiation database from nine sites across the globe. The influence of sunshine fraction, cloud cover, and air mass on estimation of diffuse radiation is investigated. Inclusion of these along with hourly clearness index, leads to the development of a series of models for each site. Estimated values of hourly diffuse radiation are compared with measured values in terms of error statistics and indicators like, R2, mean bias deviation, root mean square deviation, skewness, and kurtosis. A new me...

Suicide rates in Sri Lanka have steadily risen from the 1970’s to peak in 1995 thereafter showing a gradual decrease. We investigated whether pesticide control regulations had an impact on suicide rates. A graphical approach and regression analysis was used to identify time trends in suicide from 1980-2016 and determine the impact pesticide control regulations had on suicide rates while considering other economic variables. A gradual decrease was seen in suicide rates from 1996 to 2016. Mean numbers of suicides have decreased steadily between the time-intervals significant bans on pesticides were imposed. The regression model showed that with the enactment of each regulation, suicide rates were likely to reduce by 9 times when considered with population and economic changes. Restrictions on the use of harmful pesticides in Sri Lanka where pesticide poisoning is a common method of committing suicide has been useful in curtailing suicide rates.

با توجه به رشد روزافزون مالکیت وسیله نقلیه در کشورهای مختلف و نقش برجسته آن در زندگی اجتماعی و اقتصادی جوامع، تلاش بسیاری به منظور شناسایی عوامل موثر در افزایش مالکیت خودرو شده است. هدف از این پژوهش انجام مطالعه تطبیقی به منظور شناسایی این عوامل موثر در کشورهای توسعه یافته نسبت به کشورهای در حال توسعه است تا بتوان تا انجا که امکان دارد از سیاستهای موثر در کاهش مالکیت خودرو استفاده نمود. با استفاده از اطلاعات دادههای همفزون (در سطح ناحیه) مربوط به مطالعات جامع مشهد و کلن، مدلهای رگرسیون خطی تهیه گردیده و عوامل اثرگذار بر مالکیت وسیله نقلیه در سطح همفزون به همراه نحوه اثرگذازی مورد بررسی قرار گرفته است. یافته های این تحقیق نشان می دهند که سهم جمعیت نوجوان، سهم واحد مسکونی، چگالی جمعیت ناحیه و میانگین بعد خانوار متغیرهای مشترکی بودند که اثر مشابهی بر مالکیت وسیله نقلیه در هر دو شهر داشتند. اما اثر سهم جمعیت بیوه در هر شهر متفاوت بود. در کلن اثر سهم جمعیت میانسال و لگاریتم سهم افراد بیکار ظاهر شد. تاثیرگذاری متغیرهای شبکه و شرایط حمل ونقل عمومی در مالکیت خودرو در مشهد قابل توجه است. این امر اهمیت سیاست گذاری و برنامه ریزی در جهت اجرای سیستم های حمل و نقل عمومی برای کنترل مالکیت خودروی شخصی و در نتیجه استفاده از آن را نشان می دهد.

Geographically weighted regression (GWR) and Multiple Linear Regression (MLR) models have been applied to derive the spatial structure of urban heat island (UHI) in Wroc aw, SW Poland and compared. It was found that GWR is better suited for spatial modeling of UHI than MLR, as it takes into account non-stationarity of the spatial process. Both local and global models were extended by the interpolation of regression residuals, and used for spatial interpolation of the UHI structure. The combined: GWR + interpolated regression residuals (GWRK) approach is recommended for spatial modeling of UHI.

On-line monitoring of Total Oil and Grease (TOG) in produced water of offshore platforms has been a challenge over the past decades. There are difficulties imposed by the sensitivity to various factors (e.g., fluid temperature, pressure, gas presence, oil type, drop size, process flow). This paper proposes a model based on Machine Learning that estimates, in real time, the approximated Total Oil and Grease value from instruments and valves data of water treatment process. Neural Networks, Support Vector Machines, Regression Tree and Bagged Trees models have been trained to infer TOG values. The proposed model was applied to an offshore oil extraction plant. The results obtained from the model were similar to those obtained by the on-site lab measurement, with the Bagged Trees having presented the best results, reaching a Mean Absolute Error (MAE) by 1.15 mg/L. The results suggest that the model used can be used for on-line detection of imminent risk situations of TOG contamination.

The objective of this paper is to describe cross-country similarities and differences in national pride and to explain national pride variations on the individual and country levels. The analysis in this paper is applied to different measures of national pride, with some of them being relatively complex cognitively and the others more elementary. The paper presents the results of cross-country comparison of national pride based on empirical evidence from the ISSP-2003 database which included data from 45993 respondents from 36 countries and regions. The survey participants estimated their overall level of national pride by responding to the direct one-item question and, separately, they estimated pride of each of ten specific achievements of their countries in various domains. Factor analysis of these ten items yielded two dimensions of domain-based national pride, one of them being the factor of general pride of various country achievements and the other reflects the inverse relations between the prides of elitist and mass achievements of the nation.

The multilevel regression models estimated for the three indicators of national pride confirm the feasibility of dividing these indicators into cognitively processed and normatively imposed national pride. Cognitively processed national pride measured by the domain-based estimates have been affected by objective country achievements and by the level of standards which the achievements are compared against. The normatively imposed national pride measured by direct one-item question has been influenced by the country level of religiosity that indicates the individual willingness to accept normative messages from the state uncritically.

Rational national pride requires some objective grounds to believe in a nation’s perfection, and normative national pride is not so strongly related to objective achievements and therefore can be more easily manipulated. The practical implication of this difference stems from the fact that in their search for objectively grounded national pride people would be eager to foster country achievements and their maintenance of normatively imposed pride requires in many cases just reliably protected wishful thinking.

https://towardsdatascience.com/data-smoothing-for-data-science-visualization-the-goldilocks-trio-part-1-867765050615

Making peace with piecewise. We've all heard that information is beautiful. But then life happens, and we find ourselves trying to prettify the mess that often is reality…

Sentences and prosecutors’ demands for aggravated drunk driving are categorised into three classes: The sentence is more lenient than, is compatible with, or is harsher than the prosecutor’s demand. The probability of a sentence falling into one of the three ordered categories is explained by a cumulative logit model. The following circumstances affect the probability of a more lenient or harsher sentence, in decreasing order of importance: driving a truck, facing at least four counts, having a legal assistant, and being present in the trial. The hypothesis that factors known by the prosecutor, at the time of writing the demand, should not systematically affect sentences is refuted. The judges assess circumstances differently than the prosecutors. The prosecutors’ role is nevertheless prominent in the sense that the sentences follow, to a great extent, their demands. Notable gender effects of the actors in the courtroom are found.

Mass properties have a profound effect on automotive fuel economy, emissions, safety, ride, acceleration , braking, and maneuver . Because of this fact, it is important to have a reliable and comprehensive methodology for the estimation of key mass property parameters in the conceptual design stage. Also, such a methodology would be important for researchers investigating aspects of automotive dynamics, for programmers creating realistic automotive simulations, and for investigators studying the dynamics of automotive crash scenarios .

There is a scarcity of published information of sufficient accuracy and/or completeness so as to constitute a viable methodology. Published automotive mass property estimation methods seem to be available only in a non-comprehensive fashion through a variety of scattered sources. It is the intent of this paper to systematize the information drawn from published sources and, with the employment of techniques based on those used in the aerospace industry, to augment and improve upon the published information so as to develop a basis for a comprehensive automotive mass properties estimation methodology.

Note the use of the word “basis”; it is not to be imagined that this paper will represent the “last word” in automotive mass properties estimation. What is presented herein is intended to provide a possible overall framework for, and an initial “first cut” at, the development of a comprehensive methodology. Automotive design practitioners working within the established industry may have a far more potent estimation methodology available to them, but in the form of proprietary techniques that they are not at liberty to divulge. Yet even such automotive industry insiders may find an independently derived methodology interesting, and perhaps even useful for comparison with in-house procedures. However, it is the independent designer or researcher that is most likely to find this paper to be of great value, and it is the purpose of this paper to aid such independent efforts through promoting the development of a publicly accessible methodology.

To that end this paper presents the development of a preliminary “top-down” methodology which requires as input only those most basic and common overall parameters as would be available in the earliest of design stages or, for existing designs, from the commonly available literature. This includes such parameters as vehicle dimensions, applicable general legal specification or regulation, general vehicle configuration and category, type of suspension, and level of technology (which is generally time dependent). The desired output consists of the curb weight/c.g. coordinates/inertias, the unsprung weight/c.g. coordinates/inertias, the sprung weight/c.g. coordinates/inertias, and the sprung weight roll moment of inertia (i.e., a rotational inertia about an essentially longitudinal axis, the location of which is determined by the suspension geometry).

"Carter's music poses struggles of opposition, for instance in timbre (Double Concerto), space (String Quartet No. 3) or pulse (String Quartet No. 5). His preference for the all-interval tetrachords, 4–Z15 [0, 1, 4, 6] and 4–Z29 [0, 1, 3, 7], is also well known. From these facets of Carter's music, I develop a narrative interpretation of his Petrarch sonnet–inspired solo flute piece, Scrivo in Vento (1991). Specifically, I forge narrative pathways by imagining the two tetrachords as active agents opposed in competition. Previous Scrivo analyses (Capuzzo 2002; Childs 2006) stress continuity by revealing Q-transforms and common-note voice leading between the tetrachords. While acknowledging such features, my analysis emphasises oppositional struggle by tracing the tetrachords as separate entities which cooperate and conflict as they manoeuvre to outdo each other.

The analysis advances three theses: (1) it guides listening to and reading Scrivo in a way which resonates with Carter's concern for the aesthetics of oppositional struggle, his choice of a sonnet as inspiration and his affinity for all-interval tetrachords; (2) it shows that music-analytical detail can be organised into dramatic narratives by (a) projecting dramatic roles onto categories asserted by a formal theory and (b) treating the formal theory's relations metaphorically as actions performed by each role as the musical work unfolds; and (3) it shows how detailed pc-set analysis can support a Heraclitean view of music: a flux of opposing forces seeking and resisting unity."

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

This paper describes the use of multi-variable linear regression algorithm for estimation of a data set. The output depends linearly on m number of inputs, then linear regression is defined as, h (θ)(x) = θ 0 + m i=1 θ i (x i), where h (θ)(x) is hypothised value for given input for a particular set of parameters θ For this assignment I have applied batch gradient descent method and have analysed the effect of various hyperparameters and optimizers on the accuracy of prediction.

Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors-Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms-Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regression, Lasso Regression, and Elastic Net Regression has been used to combine the advance regression techniques. Tree-based algorithms generate a decision tree from categorical 'YES' and 'NO' values, Ensemble methods to boosting up the learning and prediction accuracy, Support Vector Machine to extend the model for both classification and regression approach and lastly advance linear regression to predict the house price with different features values.

The question of bank failures using econometric models has been widely researched worldwide but not in Zimbabwe. This paper builds and applies the logit and survival time models to establish the factors that caused banks to fail in Zimbabwe after the December 2003 monetary policy statement. Overall results show that bank-specific, macroeconomic and political factors have all contributed immensely to the collapse of the economy’s banks. Surprisingly, events that happened prior to the monetary policy statement are still in existence, thus policy makers can use the models herein to develop early warning systems
and setting screening conditions for bank license applications.

Mathematical models can help predict the outcome of sporting events. Here a Multiple regression model is used to predict the annual score of Chelsea FC, an English premier league club, by analyzing time series data of 43 years . Significant positive relationship is observed between annual scores and the two predictors (Foreign players and New Arrivals) while last year's position of the club has negative effect on annual scores. Cointegration results show one cointegrating equation so long run relationship exists. Unit root test is used to find the stationary properties which imply that the series of variables may exhibit a long run relationship. It is also concluded that the model possesses forecasting ability and can make future forecasts of the Chelsea's annual league score. This model suggests improvements to be made in the team to maintain top positions in the League.

Goldmann constants and Norris-Landzberg acceleration factors for SAC305 lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermomechanical loads. Models have been developed in conjunction with stepwise regression methods for identification of the main effects. Package architectures studied include ballgrid array (BGA) packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermomechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermomechanical failure data. Data have been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature, and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann constants and the Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experimental study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages (Lall et al.

This study examines the impact of capital structure on profitability in the UK banking industry from 2002 to 2012, focusing on six major banks: Santander, Barclays, HSBC, Lloyds, RBS, and Standard Chartered. Utilizing panel data and applying both Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) methods, the research explores how debt to equity (DE), debt to assets (DA), and capital adequacy ratio (CAR) influence return on equity (ROE) and return on assets (ROA). The findings reveal that higher DE and DA ratios positively affect profitability, indicating the benefits of leveraging debt. Conversely, a higher CAR is associated with lower profitability, highlighting the trade-off between financial stability and returns. Additionally, the study considers the impact of bank size, operating efficiency, inflation, and economic growth on profitability. The results provide valuable insights into the strategic management of capital structure in the banking sector, emphasizing the need to balance leverage and capital adequacy to optimize performance while maintaining stability. This research contributes to the broader understanding of capital structure dynamics and offers practical implications for bank management and regulatory policies.

Few statistical measures are as highly respected by
social scientists as is the coefficient of determination. R2  is an
indispensable  part  of  any  serious  research  report  and  its  sheer
magnitude is often regarded as the most important indicator of
the quality  of a study.  In this paper, I  challenge  this  view  and
argue that in research aimed at the test of a theory, R2, whether
big  or  small,  is,  in  general,  completely  irrelevant.  I  maintain,
moreover, that the common interpretation of R2 as a measure of
“explanatory  power”  is  misleading,  as is  the belief that  a  high
value  of  R2  testifies  that  the  “true”  or  “best”  or  “complete”
model  has  been  found.  I  also  discuss  the  implications  for
research practice of the effect that the spread of the independent
variable exerts on the coefficient of determination."

This research is concerned with a statistical method that has recently become widespread
in the international literature; although, it is still limited in Hungarian research. The method
is geographically weighted regression (GWR), which is demonstrated through an example
of its application. GWR is a local model that is founded on the basis of regression,
prominently taking into consideration the geographical distance. Since it does not calculate
the global relations of the whole data, but concentrates on the relationship of the dependent
and independent variables locally within a determined search area, it allows consideration
of the spatially varying processes. Simply, GWR is a developed version of the global
regression model, since, through its use, it is possible to take into account the local features
that are hidden by the global approach.

Model building and creation of benchmark models is the first step towards model validation. In this section, we will learn various kinds of model building techniques starting with the simple Linear Regression and then dealing with more complex techniques like GLM (Generalised Linear Models) and GAM (Generalised Additive Models).

There are many economic arguments or phenomenon which best described by a Seemingly Unrelated Regression equation system.  For an examples there are :  (1) Investment demand equations for several firms / company at the same industry. (2) Consumer demand function / equations which implied by behavior in maximizing utility. And (3) Input demand function / equations which implied by behavior of the company to minimizing cost and maximizing profit
This article has writen to help reveal a brief introduction as a basis to understand SUR.  The article was contains short about SUR, its implementation and also development of SUR

Energy consumers in Nigeria have long complained about Distribution Companies' unfair billing practices, exorbitant monthly electricity bills resulting from meter estimation rather than accurate meter reading. The study's objectives were to establish the relationship between the prepaid metering system and customer satisfaction; and evaluate the level of satisfaction concerning the usage of the prepaid metering system in Niger State, Nigeria. In carrying out the study, the structured questionnaire was administered to 393 randomly chosen respondents drawn from prepaid meter users, out of which 344 responded, generating a response rate of 87.5%. The data derived were subjected to spearman correlation and multiple regression models. The major findings from the study showed a significant, moderate, and positive relationship between the prepaid metering system and customer satisfaction. Additionally, three significant predictors, Affordability, Availability, and Flexibility with p < .01 are statistically significant. Further findings from descriptive statistics revealed that users had the highest level of satisfaction with their privacy as a result of no meter readers and no accumulated. The study concluded a positive and beneficial link between the prepaid metering system and customer satisfaction. Therefore, the study recommends good customer care units and a marketing campaign for better knowledge of the prepaid metering system.

Buying a house is commonly the most important financial transaction for the average person. The fact that most prices are negotiated individually (unlike a stock exchange system) creates an environment that results in an inefficient system. Most people buying houses are inexperienced amateurs with limited information about the market. A housing bubble cannot exist if individuals are making rational decisions. The objective of this paper is to evaluate the performance of a stacked regression model compared to several sub models based on predicting house prices. House characteristics and the final house price was gathered from King County, Washington, USA during the period of May 2014 and May 2015. The observed result indicates that combining the sub algorithms using a general linear model did not significantly improve results.