Modelling geographic variation in the timing of shoot extension by ericaceous shrubs

1983 ◽  
Vol 61 (7) ◽  
pp. 2032-2037 ◽  
Author(s):  
R. J. Reader
Keyword(s):  
Regression Model ◽  
Seasonal Pattern ◽  
Geographic Origin ◽  
Peat Bogs ◽  
Degree Days ◽  
Seasonal Timing ◽  
Independent Variables ◽  
Latitudinal Transect ◽  
Ericaceous Shrubs ◽  
Frost Free Period

The seasonal timing of shoot extension by three ericaceous shrubs (Ledum groenlandicum, Chamaedaphne calyculata, and Kalmia polifolia) was monitored at eight peat bogs along an 800-km latitudinal transect through Ontario, Canada, and at a transplant garden containing plants from four of the peat bogs. The timing of shoot extension varied among plants from different locations, both in the transplant garden and in the field. A regression model containing two independent variables (heat units (i.e., degree-days) and either the average annual degree-day total for a plant's geographic origin or the average frost-free period) could account for 95 to 99% of the seasonal plus intersite variation in shoot extension recorded in the transplant garden. The seasonal pattern of shoot extension predicted for each of the eight peat bogs with the regression model was close to the observed pattern in most cases. The average difference between predicted and observed percentages of shoot extension ranged from 4% for L. groenlandicum to 7% for K. polifolia.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

Dampak Brand Image dan Harga terhadap Keputusan Pembelian Produk Miniso

2021 ◽  
Vol 2 (2) ◽  
pp. 75-87
Author(s):  
Kardinah Indrianna Meutia ◽  
Hadita Hadita ◽  
Wirawan Widjarnarko
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Brand Image ◽  
Multiple Linear Regression Model ◽  
Purchasing Decisions ◽  
Business World ◽  
Independent Variables ◽  
Spss Software ◽  
Fierce Competition

The economy in the current era of globalization has fierce competition, especially in the business world, where each company moves to continue to make products primarily to meet what is needed by consumers and companies are always innovating to make products that are different from before and from  competitors and strive to be superior to other products.  This study was conducted with the aim of analyzing the independent variables which include brand image and price variables on their influence on the dependent variable, namely purchasing decisions.  This study uses multiple linear regression model and with classical assumption test using SPSS software version 24. Data were obtained primarily by distributing questionnaires to 162 students at Bhayangkara University, Jakarta Raya.  This study states that brand image and price variables can partially and significantly influence consumer purchasing decisions positively. The F test explains that the brand image and price variables together can influence purchasing decisions with results showing f-count>f-table.

scholarly journals Estimation of Regression Parameters from Noise Multiplied Data

2013 ◽  
Vol 4 (2) ◽  
Author(s):  
Yan-Xia Lin ◽  
Phillip Wise
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Real Life ◽  
Simulation Studies ◽  
Independent Variables ◽  
Multiplicative Noises ◽  
Life Data ◽  
Regression Parameters ◽  
Data Application ◽  
Real Life Data

This paper considers the scenario that all data entries in a confidentialised unit record file were masked by multiplicative noises, regardless of whether unit records are sensitive or not and regardless of whether the masked variables are dependent or independent variables in the underlying regression analysis. A technique is introduced in this paper to show how to estimate parameters in a regression model, which is originally fitted by unmasked data, based on masked data. Several simulation studies and a real-life data application are presented.

scholarly journals Regional regression models of percentile flows for the contiguous US: Expert versus data-driven independent variable selection

2016 ◽  
Author(s):  
Geoffrey Fouad ◽  
André Skupin ◽  
Christina L. Tague
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Predictive Performance ◽  
Data Driven ◽  
Mean Annual Precipitation ◽  
Expert Assessment ◽  
Independent Variables ◽  
Regional Regression ◽  
Data Driven Approach ◽  
Small Set

Abstract. Percentile flows are statistics derived from the flow duration curve (FDC) that describe the flow equaled or exceeded for a given percent of time. These statistics provide important information for managing rivers, but are often unavailable since most basins are ungauged. A common approach for predicting percentile flows is to deploy regional regression models based on gauged percentile flows and related independent variables derived from physical and climatic data. The first step of this process identifies groups of basins through a cluster analysis of the independent variables, followed by the development of a regression model for each group. This entire process hinges on the independent variables selected to summarize the physical and climatic state of basins. Distributed physical and climatic datasets now exist for the contiguous United States (US). However, it remains unclear how to best represent these data for the development of regional regression models. The study presented here developed regional regression models for the contiguous US, and evaluated the effect of different approaches for selecting the initial set of independent variables on the predictive performance of the regional regression models. An expert assessment of the dominant controls on the FDC was used to identify a small set of independent variables likely related to percentile flows. A data-driven approach was also applied to evaluate two larger sets of variables that consist of either (1) the averages of data for each basin or (2) both the averages and statistical distribution of basin data distributed in space and time. The small set of variables from the expert assessment of the FDC and two larger sets of variables for the data-driven approach were each applied for a regional regression procedure. Differences in predictive performance were evaluated using 184 validation basins withheld from regression model development. The small set of independent variables selected through expert assessment produced similar, if not better, performance than the two larger sets of variables. A parsimonious set of variables only consisted of mean annual precipitation, potential evapotranspiration, and baseflow index. Additional variables in the two larger sets of variables added little to no predictive information. Regional regression models based on the parsimonious set of variables were developed using 734 calibration basins, and were converted into a tool for predicting 13 percentile flows in the contiguous US. Supplementary Material for this paper includes an R graphical user interface for predicting the percentile flows of basins within the range of conditions used to calibrate the regression models. The equations and performance statistics of the models are also supplied in tabular form.

scholarly journals CAPITAL STRUCTURE DETERMINANTS OF PLANTATION SUB-SECTOR COMPANIES IN INDONESIA STOCK EXCHANGE PERIOD 2014 – 2018

2020 ◽  
Vol 1 (3) ◽  
pp. 455-468
Author(s):  
Septian Wildan Mujaddid ◽  
Bambang Santoso Marsoem
Keyword(s):  
Capital Structure ◽  
Regression Model ◽  
Significant Relationship ◽  
Stock Exchange ◽  
The Other ◽  
Corporate Liquidity ◽  
Independent Variables ◽  
Other Hand

The purpose of this study is to analyze the factors that influence the Debt to Asset Ratio which is a proxy of Capital Structure as the dependent variable. The independent variables studied as determinants of Capital Structure (DAR) include Size (SIZE), Profitability (ROA), Asset Structure (SA), and Corporate Liquidity (CR) using regression model. The population in this study are plantation sub-sector companies listed on the Indonesia Stock Exchange for the period 2014 - 2018. The findings suggest that ROA negatively significant affect DAR, while SA positively significant affect DAR. On the other hand, both SIZE & CR have no significant relationship with DAR

Abstract 12280: Endothelial Cell Inflammation and Antioxidant Capacity are Associated With Exercise Performance and Microcirculation in Patients With Peripheral Artery Disease

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Andrew W Gardner ◽  
Donald E Parker ◽  
Polly S Montgomery ◽  
Danuta Sosnowska ◽  
Ana I Casanegra ◽  
...  
Keyword(s):  
Endothelial Cell ◽  
Lower Extremity ◽  
Regression Model ◽  
Antioxidant Capacity ◽  
Exercise Performance ◽  
Multivariate Regression ◽  
Calf Muscle ◽  
Circulating Biomarkers ◽  
Multivariate Regression Model ◽  
Independent Variables

Background: We determined whether exercise performance and lower extremity microcirculation were associated with endothelial cell inflammation, oxidative stress, and apoptosis, and with circulating biomarkers of inflammation and antioxidant capacity. Methods: One hundred sixty symptomatic patients with PAD were characterized on the endothelial effects of circulating factors present in the sera using a cell culture-based bioassay on primary human arterial endothelial cells. Patients were further evaluated on circulating inflammatory and vascular biomarkers, physical examination, medical history, exercise performance measured during treadmill evaluation by peak walking time (PWT), and claudication onset time (COT), and lower extremity microcirculation measured by calf muscle hemoglobin oxygen saturation (StO 2 ) during treadmill exercise. Results: In the multivariate regression model for PWT, the significant independent variables included ankle-brachial index (p<0.001), age (p=0.017), hydroxyl radical antioxidant capacity (HORAC) (p=0.008), and endothelial cell NF-κB activity (p=0.015). In the multivariate regression model for COT, the significant independent variable was endothelial cell NF-κB activity (p=0.013). In the multivariate analyses for the average rate of decline in calf muscle StO 2 during exercise, the significant independent variables included body mass index (p<0.001) and HORAC (p=0.024). Conclusions: Endothelial cell inflammation and circulating biomarkers of inflammation and antioxidant capacity are significant factors for exercise performance and microcirculation of the ischemic calf musculature during exercise. The clinical implication is that interventions designed to alleviate endothelial cell inflammation and circulating inflammatory biomarkers, such as antioxidant therapy, may improve exercise performance of symptomatic patients with PAD.

scholarly journals Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3996 ◽  
Author(s):  
Marwen Elkamel ◽  
Lily Schleider ◽  
Eduardo L. Pasiliao ◽  
Ali Diabat ◽  
Qipeng P. Zheng
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Multiple Regression Model ◽  
Electricity Demand ◽  
Degree Days ◽  
Population Number ◽  
Demand Prediction

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

scholarly journals Morphogenesis and structure of Tifton 85 cultivated in subtropical climate and fertilized with nitrogen

2016 ◽  
Vol 38 (4) ◽  
pp. 389 ◽  
Author(s):  
Diego Daniel Tiecher ◽  
Marta Gomes da Rocha ◽  
Luciana Pötter ◽  
Paulo Roberto Salvador ◽  
Tuani Lopes Bergoli ◽  
...  
Keyword(s):  
Regression Model ◽  
Nonlinear Regression Model ◽  
Subtropical Climate ◽  
Degree Days ◽  
Leaf Lifespan ◽  
Experimental Animals ◽  
Sward Height ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
Four Levels

The experiment evaluated the morphogenesis and structure of Tifton 85 (Cynodon spp.) cultivated in subtropical climate and fertilized with nitrogen (N). The experiment was a completely randomized design with four levels of N (Zero; 75; 150 or 225 kg ha-1) in nine replicates per area. The experimental animals were Suffolk female lambs. The grazing method was continuous to maintain the sward height at 15 cm ± 10%. The stem expansion increased by 0.000027 cm degree-days-1 to each kg N applied (linear model). According to nonlinear model, the highest stem expansion (0.0226 cm degree-days-1) was observed with the use 220.1 kg ha-1 N. The leaf lifespan fitted a linear regression model, with increase of 50 degree-days leaf -1, comparing the levels zero and 225 kg ha-1 of N. According to nonlinear regression model, the longest leaf lifespan (407.1 degree-days) was observed with the use 208.8 kg ha-1 N. With the maintenance of sward height at approximately 15 cm, we recommended to use 200 kg ha-1 N in Tifton 85 cultivated in subtropical climate. 

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