Multiple regression analyses of midwinter fattening of the white-throated sparrow

1986 ◽  
Vol 64 (11) ◽  
pp. 2405-2411 ◽  
Author(s):  
Charles R. Blem ◽  
Michael H. Shelor
Keyword(s):  
Body Weight ◽  
Multiple Regression ◽  
Weather Conditions ◽  
Multiple Regression Model ◽  
Fine Tuning ◽  
Zonotrichia Albicollis ◽  
Independent Variables ◽  
Average Temperature ◽  
Extreme High Temperature

Midwinter lipid depots of the white-throated sparrow (Zonotrichia albicollis) at Richmond, Virginia, are correlated with a suite of environmental and morphological variables. Lipid reserves allow this species to survive even the most extreme winter conditions for several hours. Variables having the greatest individual correlations with lipid reserve are average temperature of the 20 days prior to capture, fat class, body weight, and long-term (32-year) average temperature of the date of capture. A comprehensive multiple regression model based on analyses of all possible independent variables accounts for 87% of the variation in lipid reserves. The most important independent variables in this model are body weight, mean temperature of the 20 days preceding collection, fat class, extreme high temperature of the day of capture, long-term average temperature, relative humidity, chill factor, wet-bulb temperatures of the day before and the day of capture, wing length, and precipitation. The "best" equation using only measurements of environment as independent variables included time of collection in hours after sunrise and hours before sunset, Eastern Standard Time, temperature of the 20 days prior to capture, and mean wind velocity of the day before capture. Models computed solely from temperature measurements included dry-bulb temperatures of the day of capture and the day before capture, low extreme temperatures of the day of capture, wet-bulb temperatures of the day before capture, and the 20-day average dry-bulb temperature of the period prior to collection. Fattening in response to weather conditions appears to be a form of "fine-tuning" of energy reserves superimposed on a more stable, intrinsic cycle of winter fattening.

Application of multiple regression analysis to morphometric characters obtained from Serranus cabrilla (linnaeus, 1758) by using stepwise method

2017 ◽  
Author(s):  
Levent Sangün ◽  
O. Ýnanç Güney
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
The Body ◽  
Stepwise Multiple Regression ◽  
Morphometric Characters ◽  
Independent Variables ◽  
Fisheries Science ◽  
Stepwise Method

In fisheries science, high number of morphometric measures (independent variables) taken from different parts of the fish complicates the estimation of the body weight (dependent variable). Therefore, the researchers are seeking for a solution facilitating the interpretation of the equations of correlation between the characteristics. One way to deal with this challenge is the dimension reduction by means of stepwise multiple regression analysis. The aim of this study is to explain total variation with the same accuracy by using fewer independent variables. To accomplish this, 12 morphometric measures from 210 individuals of Serranus cabrilla were measured to estimate the body weight. Firstly, the 95% of the variation was explained by means of multiple regression analysis by using all variables. Then, by step-wise method, the same results were achieved with fewer independent variables. Finally, the variables with inter-multicollinearity eliminated and with two remaining independent variables determination coefficients resulted as 95%. The result showed that using more variables does not create significant distinction for accuracy to estimate the body weight although; the total length and body dept was the most effective features for weight.

scholarly journals Adaptation of silage maize varieties under extreme northern growing condions in Finland

1979 ◽  
Vol 51 (1) ◽  
pp. 197-209
Author(s):  
Seppo Pulli ◽  
P. M. A. Tigerstedt ◽  
Osmo Kara
Keyword(s):  
Weather Conditions ◽  
Growing Season ◽  
Limiting Factor ◽  
Vegetative Period ◽  
Average Temperature ◽  
Silage Maize ◽  
Maize Varieties ◽  
Growing Conditions ◽  
The University

Trials with maize varieties from various places in the world were started in 1975. In preliminary trials in 1975, 280 varieties were tested. Between 19 and 23 varieties were selected for ordinary variety tests in 1976—78 at the University farm in Siuntio. Weather conditions, particularly average daily temperatures in 1975 were better than the long term averages, and in 1976—78 far below the average growing conditions. Dry matter yields of the seven harvested silage varieties in 1975 varied between 5.8 and 11.5 tons/ha. In 1976—78 the variation in DM yields was 3.8—8.0 tons/ha among 19—23 varieties. In 1975, 44 varieties out of 280 produced mature seed. Only one variety matured in 1978, but none in 1976—77. The developmental stage of silage maize is primarily determined by ear percentage and secondarily by DM %. In 1975 the average ear % of seven varieties was 49.1 %, in 1978 18.1 % and in 1976—77 only 4.0—5.7 % in DM. As a result of the variety tests promising varieties from Yugoslavia, France and Germany could be found. It can be concluded from the long term temperature data that with very early hybrid varieties a mature grain yield can be harvested twice in ten years. Good quality silage material can be harvested six times in ten years and a satisfactory crop can be obtained eight times in then years. The limiting factor for the growth and development of maize in Finland is the low average temperature of the growing season. Important but less significant is the length of the vegetative period, which is determined by the first killing frost in the fall. The temperature deficit is particularly critical at the beginning of the growing season.

scholarly journals A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

2020 ◽  
Vol 20 (6) ◽  
pp. 311-321
Author(s):  
YeoungRok Oh ◽  
Gyumin Lee ◽  
Kyung Soo Jun ◽  
Wooyeon Sunwoo ◽  
SeungWoo Baek ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Snow Depth ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Independent Variables ◽  
Fresh Snow ◽  
Humidity Range

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

scholarly journals Mass Migration Process between Turkey’s Eastern Regions and Seaboard-Western Regions

2010 ◽  
Author(s):  
Tahsin Bakırtaş ◽  
Orhan Kandemir
Keyword(s):  
Low Income ◽  
Multiple Regression Model ◽  
Employment Opportunity ◽  
Migration Process ◽  
Registration System ◽  
Independent Variables ◽  
Green Card ◽  
Mass Migration ◽  
Economic Migration

In the study, the aim is to analyze the economic causes of the migration phenomenon that is the most important problem of today’s Turkey, and to suggest different policy proposals for the solution of the problem. Within this frame, by accepting sixteen cities that receive high rates of migration according to arithmetical average of 2007-2008 and 2008-2009 periods indicated in the Address-Based Population Registration System as “center of attraction”, migration movements from eastern cities and from cities apart from eastern cities (western cities) to these sixteen cities of “center of attraction” were analyzed separately. In order to explain migration that oriented to centers of attraction, a multiple regression model that is convenient with international literature was formed. In this model, the number of enterprise that represents employment opportunity, the number of green card holders that represents poverty related to low income, the number of illiterates that represents lack of education and lastly the number of criminals that represents unrest were taken as independent variables. According to analysis results, independent variables that are used in the model explained migration process in both eastern cities and western cities as above 90%. On the other hand, when the coefficient rates of independent variables are examined, it was revealed that the most important determinant in economic migration is the employment opportunity. Consequently, making only income increasing social aids in regions where migration to other cities takes place is not sufficient; in addition to that, increasing employment opportunities is a healthier and a long-term solution.

Using trade show information to enhance company success: an empirical investigation

2012 ◽  
Vol 27 (5) ◽  
pp. 384-391 ◽  
Author(s):  
Harriette Bettis‐Outland ◽  
Wesley J. Johnston ◽  
R. Dale Wilson
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Information Acquisition ◽  
Value Of Information ◽  
Multiple Regression Model ◽  
Content Type ◽  
Independent Variables ◽  
Trade Show ◽  
Trade Shows ◽  
The Relationship

PurposeThis paper seeks to provide an exploratory empirical study of the variables that are part of the return on trade show information (RTSI) concept, which is based on the use and value of information gathered at a trade show.Design/methodology/approachThe research is designed to explore relationships and identify those variables that are a particularly important part of the RTSI concept. The paper provides an exploratory test of the relationship between a series of variables that are related to the value of information gathered at trade shows. Data were collected from trade show attendees approximately 60 days after the trade show. A multiple regression model was developed that explores the relationship between the dependent variable that focuses on information value and the independent variables on various aspects of information acquisition, information dissemination, and information use.FindingsThe final multiple regression model found a significant relationship for several variables and has an adjusted R2 value of 0.552. Four significant independent variables were identified – one each in the information use and the shared information categories and two in the information acquisition category. These findings present an interesting picture of how information is used within an organization after it is acquired at a trade show.Research limitations/implicationsThe research is limited by the multiple regression model used to explore the relationships in the data. Also, data from only one trade show were used in the model.Practical implicationsThis paper focuses on the intangible, longer‐term benefits as important considerations when determining the value of new trade show information to the firm. The evaluation of trade show information also should include these intangible benefits, such as improved interdepartmental relations or interactions as well as discussions with other trade show participants in finding new uses for information that impacts the company's future success, as well as shorter‐term benefits such as booth activity.Originality/valueThe paper offers a unique approach for determining the value of information acquired at trade shows. Though information gathering has been included as an outcome variable in previous trade show studies, no other research has studied the value of this new trade show information to the company.

scholarly journals Multiple regression model of the consumers’ price sensitivity

2020 ◽  
Vol 183 (5-6) ◽  
pp. 60-65
Author(s):  
Marie Slaba ◽  
Keyword(s):  
Multiple Regression ◽  
Negative Correlation ◽  
Multiple Regression Model ◽  
Demographic Characteristics ◽  
Bachelor's Degree ◽  
Price Sensitivity ◽  
Or Education ◽  
Independent Variables ◽  
The Family ◽  
On Line

All marketing decisions have to take into account the customer’s attitude to the price and consumers’ price sensitivity. Consumers’ price sensitivity depends on various factors: perceived quality and benefits, service quality, and of course, demographic characteristics. The main aim of this paper is to explore the influence of socio-demographic characteristics on the customers’ price sensitivity. Potential influencing factors and sample size were identified based on the literature search. The questionnaires were disseminated on-line in 2019. The majority of respondents were at the age group 25-54 years old (63%) which corresponds to the composition of the Czech population. Most of the respondents live in the family with two children (29%) or only with a partner (28%), 61% of the respondents have a master or bachelor’s degree. The variable Age shows high significance, which means, no surprisingly, that older customers are more price-sensitive than the younger ones. Talking about gender, females are slightly more price-sensitive than males. Then, we can state that the more family members has got the household, the more price-sensitive the customer is. In comparison with other independent variables, Income and Education show a negative correlation with customer price sensitivity, which means that the higher level of Income or Education is, the lower is the customer’s price sensitivity. Based on the multiple regression analysis, the author’s research proves that the most significant impact on the consumers’ price sensitivity have two independent variables - age (positive correlation) and income (negative correlation).

scholarly journals Assessment of the relationship between the logistics infrastructure of the Ural Federal District and the economic indicators of the district and the country

2020 ◽  
Vol 208 ◽  
pp. 05014
Author(s):  
Pavel Popov
Keyword(s):  
Multiple Regression ◽  
Federal District ◽  
Multiple Regression Model ◽  
Economic Indicators ◽  
Infrastructure Development ◽  
Development Programs ◽  
Independent Variables ◽  
Transport Component ◽  
The Russian Federation ◽  
The Relationship

The article shows the relationship between the logistics infrastructure of the Ural Federal District and the economic indicators of both the Russian Federation and the district. The parameters of the logistics infrastructure included indicators of its warehouse, transport, financial, and informational components. The analyzed data are for 2004-2018. To exclude redundant variables, they were tested for normality, multicollinearity, and the measure of the relationship with dependent indicators was assessed. The research method is multiple regression analysis. The study has proved that the relationship between economic indicators and independent variables can be represented as a linear multiple regression model. Based on the models obtained, the significant influence of the transport component of the logistics infrastructure of the Ural Federal District on the economic indicators of the country and the district is shown. The creation of logistics infrastructure development programs based on the results obtained will promote the socio-economic development and investment attractiveness of the district.

scholarly journals RETURN ON INVESTMENT ANALYSIS OF UNLICENSED SOLAR ENERGY PROJECTS IN TURKEY

2020 ◽  
Vol 8 (1) ◽  
pp. 903-923
Author(s):  
Murat ERTUĞRUL ◽  
Mustafa Hakan SALDI
Keyword(s):  
Solar Energy ◽  
Regression Model ◽  
Multiple Regression ◽  
Return On Investment ◽  
Research Question ◽  
Multiple Regression Model ◽  
Power Capacity ◽  
Independent Variables ◽  
Energy Projects ◽  
Investment Rates

First of all, this study aims to show how the power size and currency affect the return on investment percentages of unlicensed solar energy projects in Turkey. Commonly, the investors have confusions on their minds while taking investment decisions. Particularly, there are definite variables which may affect a solar energy project’s return on investment percentage and so the research question of how a multiple regression model can represent this percentage comes back to minds too. In order to simulate investment scenarios, this study is designed by using the sample of unlicensed solar energy installations which have the capacity of 250 KW, 500 KW and 1000 KW. According to the cash flow analyses for these samples the effects of power size and currency variables to return on investment percentages are observed. Therefore, the multiple regression model of return on investment percentages is offered by taking into account the power capacity and currency as independent variables to estimate the future cash flows by comparing each cases. As a result, the correlations are observed between dependent variable and independent variables. Especially, the power capacity has significant effect on return on investment rates of projects in accordance with the fundamental rule of risk-reward relation in finance. Also, the share of currency risk is calculated to prove how the volatility in currency index may affect the return on investment rates.

scholarly journals Use of factor scores in multiple regression analysis for estimation of body weight by certain body measurements in Romanov Lambs

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7434 ◽  
Author(s):  
Yalcin Tahtali
Keyword(s):  
Factor Analysis ◽  
Body Weight ◽  
Regression Model ◽  
Multiple Regression ◽  
Prediction Equation ◽  
Multiple Regression Model ◽  
The Body ◽  
Body Measurements ◽  
Factor Scores ◽  
Two Factors

The study investigates the solution of the multicollinearity between certain body measurements of Romanov lambs and prediction of the body weight of Romanov lambs using the thus calculated factor analysis scores and a multiple regression model. For this purpose, the body measurements (wither height (WH), croup height (CH), body length (BL), chest depth (CD), chest circumference (CC), chest width behind shoulders (CWS) and head length (HL)) and body weight (BW) of 6-month-old 50 Romanov lambs born in 2015 were used. The factor analysis scores were used to obtain the prediction equation for the relationship between the investigated traits. The analysis results showed that there was a multicollinearity between the wither and croup height traits used in the prediction equation. Moreover, the results revealed that the variables for the body measurements can be represented by two factors. These factors explained 50.89% and 22.86% of the total variance, respectively. The multicollinearity between the independent variables was eliminated with the use of the factor scores obtained with the factor analysis in the multiple regression model, and thus it was observed that better results can be obtained by using the factor analysis scores in the prediction of the body weight of 6-month-old Romanov lambs.

scholarly journals SELECTED ANTECEDENTS OF THE IMPORTANCE OF NUTRITION CLAIMS FOR FOOD PROCESSORS AND DISTRIBUTORS

2019 ◽  
Vol 52 (2) ◽  
Author(s):  
Paweł Bryła
Keyword(s):  
Multiple Regression ◽  
Food Packaging ◽  
Multiple Regression Model ◽  
Regression Analyses ◽  
Independent Variables ◽  
Nutrition Claims ◽  
Perceived Credibility ◽  
Related Information ◽  
Health Related ◽  
Excessive Number

The purpose of this paper is to examine the importance attached to nutrition claims placed on food packaging from the perspective of Polish food processors and distributors. A total of 78 completed questionnaires were obtained with the use of the CAWI methodology. The author used correlations, t-statistics, ANOVAs, and simple and multiple regression analyses. In simple regressions, 6 independent variables turned out to influence the declared importance of nutrition claims in a statistically significant way: 1) perceived credibility of nutrition claims; 2) agreeing that the availability of health-related information is not sufficient for the vast majority of Polish consumers to change their preferences for the choice of foods; 3) strong belief that food products carry too many nutrition claims; 4) self-reported knowledge about the healthiness of one’s diet; 5) respondent age; and 6) seniority of the respondent in the companies surveyed. In a multiple regression model, only variables 1, 2, and 3 remained statistically significant at p < 0.05. An increase in the perceived credibility and stronger agreement with the above statement increase the importance attached to nutrition claims, whereas believing that foods carry an excessive number of nutrition claims reduces it.

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