scholarly journals Evaluation of Impact of the Operational and Technical Factors on Downtime of Municipal Buses Based on a Linear Regression Model

2021 ◽  
Vol 23 (4) ◽  
pp. A241-A247
Author(s):  
Joanna Rymarz ◽  
Anna Borucka ◽  
Andrzej Niewczas
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
External Factors ◽  
Impact Factors ◽  
Estimation Of Parameters ◽  
Failure Type ◽  
Technical Factors ◽  
Failure Types ◽  
Organizational Problems

The objective of this study was to assess the effect of selected operational and technical factors on downtime of vehicles. The sample consisted of buses from a municipal transport company (Poland). Estimation of parameters of a linear regression model was performed. Month of failure (downtime event) and its type were used as predictors. Failures were divided into three categories: events related to the company’s operations, including vehicle failures (1) and other (organizational) problems (2), as well as failures caused by external factors unrelated to the operations of the transport company (3). The downtime was found to be significantly associated with failure type and month of failure. A linear regression model of downtime with a reduced number of impact factors, taking into account two main failure types and two main periods of their occurrence during the year, was developed.

scholarly journals Toepassing van 'n lineêre regressiemodel op verskillende inligtingstelselfasette

1991 ◽  
Vol 22 (3) ◽  
pp. 41-45
Author(s):  
P. J.S. Bruwer
Keyword(s):  
Information System ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
System Function ◽  
Small Organizations ◽  
Application Systems ◽  
Computer Based ◽  
Technical Factors ◽  
Evaluation Techniques

Application of a linear regression model to different information system facets Evaluation techniques with regard to the information system function in organizations could well be used to serve as a tool to identify problem areas and the corresponding corrective actions to be taken. In the past quite a number of computer-based application systems have failed in large as well as small organizations not because of inadequate technical quality but because some important non-technical factors of critical importance for the success of the system have not been diagnosed timely. In a previous research project (Bruwer: 1983) a restricted linear regression model method has been used to evaluate the success of the information system function in organizations. In this article the same model is applied on three different areas of the information system function. These areas are: Aspects of strategic planning of information systems; evaluation of the use of micro-computer systems and the evaluation of computer facilities in organizations.

scholarly journals A Treatise on Ordinary Least Squares Estimation of Parameters of Linear Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 518
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Linear Models ◽  
Ordinary Least Squares ◽  
Linear Estimation ◽  
Estimation Of Parameters ◽  
Parametric Function ◽  
Ols Regression

This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir.  Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.  

scholarly journals Market Openness and Its Relationship to Connecting Markets Due to COVID-19

2021 ◽  
Vol 13 (19) ◽  
pp. 10964
Author(s):  
Karime Chahuán-Jiménez ◽  
Rolando Rubilar-Torrealba ◽  
Hanns de la Fuente-Mella
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mixed Model ◽  
Financial Contagion ◽  
Estimation Of Parameters ◽  
Health Crisis ◽  
Economic Markets ◽  
Trade Relationships ◽  
Mixed Linear Regression Model

In this research, statistical models were formulated to study the effect of the health crisis arising from COVID-19 in economic markets. Economic markets experience economic crises irrespective of effects corresponding to financial contagion. This investigation was based on a mixed linear regression model that contains both fixed and random effects for the estimation of parameters and a mixed linear regression model corresponding to the generalisation of a linear model using the incorporation of random deviations and used data on the evolution of the international trade of a group of 42 countries, in order to quantify the effect that COVID-19 has had on their trade relationships and considering the average state of trade relationships before the global pandemic was declared and its subsequent effects. To measure, quantify and model the effect of COVID-19 on trade relationships, three main indicators were used: imports, exports and the sum of imports and exports, using six model specifications for the variation in foreign trade as response variables. The results suggest that trade openness, measured through the trade variable, should be modelled with a mixed model, while imports and exports can be modelled with an ordinary linear regression model. The trade relationship between countries with greater economic openness (using imports and exports as a trade variable) has a higher correlation with the country’s health index and its effect on the financial market through its main trading index; the same is true for country risk. However, regarding the association with OECD membership, the relations are only with imports.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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