COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

Chinook salmon (Oncorhynchus tshawytscha) — seabird covariation off central California and possible forecasting applications

2007 ◽  
Vol 64 (8) ◽  
pp. 1080-1090 ◽  
Author(s):  
Jennifer E Roth ◽  
Kyra L Mills ◽  
William J Sydeman
Keyword(s):  
Chinook Salmon ◽  
Breeding Success ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Oncorhynchus Tshawytscha ◽  
Central California ◽  
Time Periods ◽  
Ocean Conditions

We evaluated covariation between Chinook salmon (Oncorhynchus tshawytscha) abundance and seabird breeding success in central California, USA, and compared potential forecasts to predictive models based on jack (2-year-old male) returns in the previous year. Stepwise regression models based on seabird breeding success in the previous year were comparable to or stronger than jack-based models. Including seabird breeding success in the current year improved the strength of the relationships. Combined approaches that included seabird and jack data further improved the models in some cases. The relationships based on seabird breeding success remained relatively strong over both shorter (1990–2004) and longer (1976–2004) time periods. Regression models based on multivariate seabird or combined seabird–jack indices were not as strong as stepwise regression models. Our results indicate that there is significant covariation in the responses of salmon and seabirds to variability in ocean conditions and that seabird data may offer an alternate way of forecasting salmon abundance in central California.

scholarly journals Regression analysis of milk production traits in simmental cows

2011 ◽  
Vol 27 (4) ◽  
pp. 1433-1441
Author(s):  
M.D. Petrovic ◽  
V. Bogdanovic ◽  
M.M. Petrovic ◽  
S. Rakonjac
Keyword(s):  
Regression Analysis ◽  
Milk Production ◽  
Milk Fat ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Production Traits ◽  
Milk Production Traits ◽  
Partial Regression ◽  
Three Generations ◽  
Lactation Length

The relationship between milk production traits over whole lactations was evaluated across three generations of Simmental cows, i.e. between daughters, dams and grand dams, by a phenotypic regression analysis with whole lactation traits in the daughter generation being used as the dependent variables (x1), and those in the dam and grand dam generations being used as the independent variables (x2 and x3). The results were obtained from a sample of 1170 daughters and as many dams and grand dams. The significance of the partial regression coefficients b2 and b3 was separately evaluated by a t-test. An analysis of variance was used to estimate the significance of the simultaneous effect of the production traits of dams and grand dams on the milk production achieved in the daughter generation. The calculated value of the partial regression coefficients for the whole lactation production traits across three generations (grand dams, dams and daughters) and their statistical significances determined by the t and F tests, as well as the regression equations used, suggested that the effect of the grand dam generation on the milk production traits in granddaughters was substantially lower than the effect of dams. The calculated partial regression coefficients (b2 and b3) were positive and statistically very significant (P<0.01), excepting the regression coefficients b3 for lactation length and b2 for milk fat content that were not statistically significant (P>0.05). A very significant change (P<0.01) was observed in all production traits in the daughter generation as simultaneously affected by the traits in the dam and grand dam generations.

AGRICULTURAL METEOROLOGY: CORRELATION OF AIR TEMPERATURES IN CENTRAL AND SOUTHERN ALBERTA AND SASKATCHEWAN WITH LATITUDE, LONGITUDE AND ALTITUDE

1938 ◽  
Vol 16c (1) ◽  
pp. 16-26 ◽  
Author(s):  
J. W. Hopkins
Keyword(s):  
Air Temperature ◽  
Local Variation ◽  
Temperature Gradients ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Partial Regression ◽  
Air Temperatures ◽  
Southern Alberta ◽  
Different Seasons ◽  
Agricultural Meteorology

Linear partial regression coefficients of the 18-year average (1917–34) monthly mean air temperature recorded at 43 points in central and southern Alberta and Saskatchewan on latitude, longitude, and altitude were determined for each month of the year. The three series of coefficients each show an independent seasonal trend. The decrease in air temperature with altitude is greatest in summer and least in winter, whereas the gradient associated with longitude is most pronounced in winter and least in evidence in summer. The influence of latitude is likewise most pronounced in winter, but shows two minima, in spring and autumn respectively. The monthly regression equations account for most of the variance of the station averages, and hence provide a reasonably satisfactory graduation of the climatological temperature gradients characteristic of this area at different seasons of the year.These regression equations could not, however, be applied satisfactorily to the monthly averages for individual years, owing to greater local variation. Additional equations were therefore determined from the records for 1935 at 27 stations in the sub-area bounded by the 50th and 52nd parallels and the 104th and 108th meridians. The results suggest that further additions to the number of stations would still be desirable, and that if this was effected a fairly accurate graduation should be possible within this district, even in individual years.

scholarly journals The Deletion of Variables From Regression Models Based on Tests of Significance: A Statistical and Moral Issue

1975 ◽  
Vol 7 (1) ◽  
pp. 211-216 ◽  
Author(s):  
David L. Debertin ◽  
R. J. Freund
Keyword(s):  
Regression Models ◽  
Stepwise Regression ◽  
Statistical Tests ◽  
A Priori ◽  
Regression Coefficients ◽  
Model Specification ◽  
Moral Issue ◽  
Significant Regression ◽  
Data Dredging ◽  
Published Research

The purpose of this paper is to illustrate some of the dangers inherent in use of statistical tests as a criterion for deleting variables from regression models. The deletion of variables from regression models based on t or F tests of regression coefficients has been a procedure widely followed by applied economists and other researchers. When economic theory does not provide an adequate conceptual basis for rigorous a priori specification of the regression model, one approach to model specification has been to include in the regression equation all variables thought to be “somehow” related to the dependent variable of interest. Subsets of variables with statistically significant coefficients are identified, with the aid of a stepwise regression routine. Truncated models consisting of only those variables with statistically significant regression coefficients are sometimes presented in the published research without reference to the initial data dredging that took place.

The Effects of Rainfall and Temperature on Percolation Through Drain Gauges

1934 ◽  
Vol 24 (1) ◽  
pp. 105-135 ◽  
Author(s):  
R. S. Koshal ◽  
R. A. Fisher
Keyword(s):  
Numerical Methods ◽  
Soil Mass ◽  
Rate Of Change ◽  
Regression Coefficients ◽  
Standard Errors ◽  
Regression Equations ◽  
Partial Regression ◽  
Direct Knowledge ◽  
The Mean ◽  
Partial Depletion

Summary1. Partial regression equations representing the average drainage observed in any month in terms of the temperature and rainfall of that month, and including terms representing the mean secular rate of change of the drainage discharge and of its regression coefficients on rainfall and temperature, have been fitted to the thirty-six series of observations provided by the three Rothamsted drain gauges in the twelve months of the year.2. An account is given of adequate and direct numerical methods of handling equations involving observed quantities, and chosen functions of them, as independent variates, and of calculating standard errors appropriate to the several sorts of comparison which are to be made.3. In the absence of direct knowledge of the amount of water contained from time to time in the soil mass of the gauge it has been customary to assume that the lower average drainage of the summer months is directly due to a greater amount of evaporation taking place in these months. The results of the present enquiry direct attention to a second possibility, namely that the water content of the gauges differs considerably at different times of the year, and that the high drainage in winter is in part to be ascribed to the accumulation of water during the rainy months of autumn, while the lower drainage in summer is due to the partial depletion of the gauges during the lower rainfall of the spring months.

MACROCLIMATIC MODEL FOR ESTIMATING MONTHLY SOIL TEMPERATURES UNDER SHORT-GRASS COVER IN CANADA

1973 ◽  
Vol 53 (3) ◽  
pp. 263-274 ◽  
Author(s):  
C. E. OUELLET
Keyword(s):  
Soil Temperature ◽  
Potential Evapotranspiration ◽  
Important Variable ◽  
Climatic Variables ◽  
Standard Errors ◽  
Climatic Data ◽  
Regression Equations ◽  
Grass Cover ◽  
Temperature Variations ◽  
Soil Temperatures

A macroclimatic model was developed to estimate monthly soil temperatures under short-grass cover. It involved multiple regression equations for each month and for each of six depths (1, 10, 20, 50, 100, and 150 cm). Data used were obtained from published records of soil temperature and corresponding climatic variables. They were from 41 stations over several years with station-years per regression varying from 88 to 226 according to depths and months. The climatic variables were related to air temperature, rainfall, snowfall, and potential evapotranspiration. An additional important variable was the estimated soil temperature of the previous month. The equations explain 70–96% of the soil temperature variations and the standard errors of estimate varied from 0.7 to 2.2 C. Temperatures estimated for 1 yr and eight stations with climatic data not used in the development of the equations departed from the observed values by less than 0.5, 1.0, and 2.0 C in 34, 62, and 92% of the cases, respectively. Errors resulting from the estimation of monthly normals by this model are expected to be generally less than 1.0 degree C.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

EEG-correlates of work efficiency level among young persons with stress-induced bruxism

2021 ◽  
pp. 25-29
Author(s):  
A. E. Barulin ◽  
S. V. Klauchek ◽  
A. E. Klauchek
Keyword(s):  
Regression Models ◽  
Alpha Rhythm ◽  
Negative Relationship ◽  
Maximum Amplitude ◽  
Dominant Frequency ◽  
Regression Equations ◽  
Microsoft Excel ◽  
The Subject ◽  
Males And Females ◽  
The Relationship

Purpose of the study. To establish the relationship between neurophysiological status and the level of efficiency in young people with bruxism.Materials and methods. Two groups of 64 and 53 subjects (males and females) aged 20–35 years old with bruxism and non-bruxers were formed according to questionnaire results and physical examination. The level of efficiency was assessed by the results of sensorimotor tracking of a moving object (the ‘Smile’ model). Spectral analysis was performed for evaluation of the baseline electroencephalograms. Microsoft Excel and Statistica 10.0 programs were used for statistical data processing.Results. The level of efficiency was statistically significantly lower in the hardest test of Smile model among the individuals with bruxism (p < 0.05). The bruxers also demonstrated a significantly lower dominant frequency and maximum amplitude of alpha-rhythm (p < 0.05), and significantly higher dominant frequency of beta2 rhythm (p < 0.05). The dominant frequency and the maximum amplitude of the alpha-rhythm are parameters corresponding to significant coefficients of the regression analysis. A negative relationship was found between the degree of error during sensorimotor tracking and the frequency and amplitude of alpha-rhythm.Conclusion. Regression models present the relationship between the level of efficiency and the alpha-rhythm severity. The regression equations make it possible to determine the functional state of the subject using an electroencephalogram.

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