Multiple linear regression with correlated explanatory variables and responses

Survey Review ◽  
2016 ◽  
Vol 49 (352) ◽  
pp. 1-8 ◽  
Author(s):  
B. Li ◽  
M. Wang ◽  
Y. Yang
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Explanatory Variables

scholarly journals Statistical Modelling of the Capital Asset Pricing Model (CAPM)

2018 ◽  
Vol 7 (2) ◽  
pp. 146
Author(s):  
Silvi Qemo ◽  
Eahab Elsaid
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Statistical Modelling ◽  
Expected Returns ◽  
Explanatory Variables ◽  
Bid Ask Spread ◽  
Model Average

The purpose of this study is to derive a multiple linear regression model of the CAPM. More specifically, to test for other potential explanatory variables that can be added to the basic linear regression model for the expected returns on Apple Inc. The following explanatory variables were examined: share volume, outstanding shares, closing bid/ask spread, high/low spread and average spread. Using daily returns of Apple Inc. stock from 2007 till 2014 we were able to create a multiple linear regression model of CAPM that increase the R2 value from the basic linear regression model and enhances the amount of variability in the returns on an asset. This is an important modification that can help better forecast returns on assets.Keywords: CAPM; multiple linear regression model; average spread; variability in the returns

A preliminary investigation into honey bee (Apis mellifera) pollination of canola (Brassica napus cv. Karoo) in Western Australia

2000 ◽  
Vol 40 (3) ◽  
pp. 439 ◽  
Author(s):  
R. Manning ◽  
R. Manning ◽  
J. Boland ◽  
J. Boland
Keyword(s):  
Apis Mellifera ◽  
Brassica Napus ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Honey Bee ◽  
Preliminary Investigation ◽  
Conservative Estimate ◽  
Branch Number ◽  
Explanatory Variables ◽  
Pod Yield

The aim of this preliminary experiment was to evaluate the effect of distance from the apiary on pod yield in canola. Beehives were used at a density of 1.28 hives/ha. The results showed that the number of pods/plant decreased as distance from the apiary increased, when plant height and branch number were used as explanatory variables. Multiple linear regression indicated a predicted pod loss of 15.3 pods/plant over a distance of 1000 m from an apiary. This was equivalent to a 16% loss based on an average of 59 plants/m2 and average pod production of 5666 pods/m2 from this experiment. For a 2 t/ha crop this would be equivalent to about 320 kg/ha. The results are only indicative because of the variation in the crop studied and lack of replication, but may, in fact, be a conservative estimate.

scholarly journals Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project

2018 ◽  
Author(s):  
Lester Melie-Garcia ◽  
Bogdan Draganski ◽  
John Ashburner ◽  
Ferath Kherif
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Human Brain ◽  
Multiple Linear Regression ◽  
Medical Informatics ◽  
Data Privacy ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Explanatory Variables ◽  
Human Brain Project

ABSTRACTWe propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical, neuropsychological and neurophysiological variables in the field of neuroimaging. One of the main concepts behind MIP is to federate data, which is stored locally in geographically distributed sites (hospitals, customized databases, etc.) around the world. We restrain from using a unique federation node for two main reasons: first the maintenance of data privacy, and second the efficiency in management of big volumes of data in terms of latency and storage resources needed in the federation node. Considering these conditions and the distributed nature of data, MLR cannot be estimated in the classical way, which raises the necessity of modifications of the standard algorithms. We use the Bayesian formalism that provides the armamentarium necessary to implement the MLR methodology for distributed Big Data. It allows us to account for the heterogeneity of the possible mechanisms that explain data sets across sites expressed through different models of explanatory variables. This approach enables the integration of highly heterogeneous data coming from different subjects and hospitals across the globe. Additionally, it offers general and sophisticated ways, which are extendable to other statistical models, to suit high-dimensional and distributed multimodal data. This work forms part of a series of papers related to the methodological developments embedded in the MIP.

scholarly journals Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1156 ◽  
Author(s):  
Jae Cho ◽  
Jong Lee
Keyword(s):  
Linear Regression ◽  
Root Mean Square Error ◽  
Multiple Linear Regression ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sediment Runoff ◽  
Mean Square ◽  
Pollutant Load ◽  
Explanatory Variables ◽  
Pollutant Discharge

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

scholarly journals ANALISIS SPASIAL INDUSTRI KECIL DAN MENENGAH DI PROPINSI NUSA TENGGARA TIMUR

2011 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Zainal Arifin
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Raw Materials ◽  
Small And Medium Enterprises ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Explanatory Variables ◽  
Business Units ◽  
Medium Enterprises

This study aims to identify patterns of spatial concentration of Small and Medium Enterprises in East Nusa Tenggara judging by the amount of labor and production as well as the factors that affect the employment period 2005-2009. Analysis tools used include: Spatial Analysis, Geographic Information Systems, and multiple linear regression. This study found that the distribution of Small and Medium Enterprises in East Nusa Tenggara is not evenly distributed geographically, when viewed from the employment and production quantities. In some  counties and cities experienced employment and production quantities are high, while some others were experiencing employment and production quantities are low. It also reinforced the results of multiple linear regression analysis with panel data with the result that all explanatory variables X1 (business units), X2 (investment), X3 (production) and X4 (raw materials) are able to explain to the employment of Small and Medium Industries in East Nusa Tenggara.

Pengaruh Motivasi Kerja Terhadap Kinerja Pegawai Kantor Sekretariat Daerah Kabupaten Gunungkidul

2017 ◽  
Vol 23 (2) ◽  
pp. 121-137
Author(s):  
Ary Sutrischastini ◽  
Agus Riyanto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Work Motivation ◽  
Probability Sampling ◽  
Random Probability

This paper will discuss the effect of work motivation (incentives, motives and expectations) on the performance of the staff of the Regional Secretariat Gunungkidul. The purpose of this paper is: 1) Determine the effect of incentives on the performance of the staff of the Regional Secretariat Gunungkidul, 2) Determine the effect of motive on the performance of the staff of the Regional Secretariat Gunungkidul, 3) To know the effect of expectations on the performance of the staff of the Regional Secretariat Gunungkidul, 4)To know the effect of incentives, motives and expectations on the performance of the staff of the Regional Secretariat Gunungkidul.Research sites in the Regional Secretariat Gunungkidul and the population is 162entire employee in the Regional Secretariat Gunungkidul. Samples amounted to 116 respondents taken with simple random probability sampling method. Data were analyzed using multiple linear regression. Results obtained: (1) incentives positive and significant effect on the performance of, (2) motif positive and significant effect on the performance of, (3) expectations positive and significant impact on the performance of , and (4) incentives, motives and expectations of positive and significant impact on the performance of the staff of the Regional Secretariat Gunungkidul.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

Faktor-Faktor Yang Mempengaruhi Keputusan Mahasiswa Memilih Program Studi Kebidanan STIKES Muhammadiyah Ciamis Tahun 2016

2018 ◽  
Vol 5 (1) ◽  
pp. 69-82
Author(s):  
Evi Mariana
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Sampling Method ◽  
Marketing Mix ◽  
Stratified Random Sampling ◽  
Price Promotion ◽  
F Test ◽  
Physical Evidence

The purpose of this study was to analyze the factors that influence the decisionof the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis and analyze the factors that most influence the decision of the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis. Collecting data in this study was conducted using a survey by questionnaire to 114 students by stratified random sampling method. Methods of data analysis using multiple linear regression, F test and test T. The result is a marketing mix that significantly is the product, place, and physical evidence. And that does not affect the marketing mix is price, promotion, place, and processes

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