Cross-Validation of Regression Equations to Predict Performance in a Pursuit Tracking Task

1978 ◽  
Vol 22 (1) ◽  
pp. 369-372
Author(s):  
Ricky E. Savage ◽  
Robert C. Williges ◽  
Beverly H. Williges
Keyword(s):  
Cross Validation ◽  
Regression Equation ◽  
Tracking Task ◽  
Training Condition ◽  
Regression Equations ◽  
Training Time ◽  
Pooled Data ◽  
Pursuit Tracking ◽  
Validation Procedure ◽  
Time To Learn

A double, cross-validation procedure was used to validate regression equations which predict training time to learn a two-dimensional pursuit tracking task. Motor skill and information processing tasks were used as predictors. The results yielded a reliable regression equation for each training condition, and these equations were quite similar in cross-validation. Subsequently, a regression equation based on pooled data from the original and cross-validation sample was calculated for each training condition. To establish the usefulness of a regression approach for selecting training strategies, these equations will be used in a future study where students will be matched, mismatched, and randomly assigned to various training alternatives.

LEAF NUMBER ESTIMATION FROM SHOOT DRY WEIGHT MEASUREMENTS FOR TWO TURFGRASS SPECIES

1989 ◽  
Vol 69 (1) ◽  
pp. 297-304
Author(s):  
C. P. M. WRIGHT ◽  
J. L. EGGENS ◽  
K. CAREY ◽  
R. J. HINES
Keyword(s):  
Creeping Bentgrass ◽  
Dry Weight ◽  
Regression Equation ◽  
Leaf Number ◽  
Individual Plant ◽  
Regression Equations ◽  
Data Set ◽  
Annual Bluegrass ◽  
Pooled Data ◽  
Shoot Dry Weight

The objective of this study was to determine if total plant leaf numbers in a large data set could be predicted from individual plant shoot dry weight measurements using regression equations derived from a subset of the data. The species used were annual bluegrass (Poa annua L.) and creeping bentgrass (Agrostis palustris Huds. ’Penncross’). There was significant correlation between leaf number and shoot dry weight measurements in data subsets. Leaf numbers for the total data set were estimated by the regression equation derived from a subset consisting of pooled data from one, two or three replicates. This procedure was assessed by comparing predictions from regression equations with actual values, using a number of different sets of replicates to generate the regression equation. On the basis of the results we suggest that, for annual bluegrass and creeping bentgrass in greenhouse pot culture, this procedure can be used to accurately estimate leaf number data for remaining replicates within an experimental design, once regression coefficients are established from pooled data from two replicates.Key words: Leaf number estimation, shoot dry weight, annual bluegrass, creeping bentgrass

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

Tracking in Two Dimensions as a Function of Dimension Priorities and Tracking Difficulty

1977 ◽  
Vol 21 (2) ◽  
pp. 118-122 ◽  
Author(s):  
Daniel Gopher ◽  
David Navon ◽  
Nela Chillag
Keyword(s):  
Two Dimensions ◽  
Regression Equations ◽  
Time Sharing ◽  
Evaluation Of Performance ◽  
Adaptive Procedures ◽  
Pursuit Tracking ◽  
Tracking Ability ◽  
And Performance ◽  
Task Conditions ◽  
Tracking Behavior

The present paper develops the argument that an effective evaluation of performance under time-sharing conditions requires a joint manipulation of tasks difficulty and operator's resources allocation. An experiment is presented in which each of the dimensions in a two dimensional pursuit tracking task was manipulated and controlled seperately. Single and dual task conditions were created by presenting one dimension or two dimensions simultaneously. Time-sharing efficiency was assessed under a joint manipulation of tracking difficulty on each dimension and their relative priorities. Subjects' tracking ability was individually calibrated by adaptive procedures. Regression equations and performance functions were obtained to describe the joint effects of the experimental variables. Results are discussed in terms of their implications to the problem of measuring capacity, and their contribution to the understanding of tracking behavior.

Performance on an Adaptive Automated Team Tracking Task with Human and Computer Teammates

1997 ◽  
Vol 41 (1) ◽  
pp. 551-555 ◽  
Author(s):  
Kristin Krahl ◽  
Mark W. Scerbo
Keyword(s):  
Adaptive Systems ◽  
High Performance ◽  
Vertical Axis ◽  
Tracking Task ◽  
Superior Performance ◽  
Performance Skills ◽  
Group Assignment ◽  
Pursuit Tracking ◽  
Human Participants ◽  
High Level

The present study examined team performance on an adaptive pursuit tracking task with human-human and human-computer teams. The participants were randomly assigned to one of three team conditions where their partner was either a computer novice, computer expert, or human. Participants began the experiment with control over either the horizontal or vertical axis, but had the option of taking control of their teammate's axis if they achieved superior performance on the previous trial. A control condition was also run where a single participant controlled both axes. Performance was assessed by RMSE scores over 100 trials. The results showed that performance along the horizontal axis improved over the session regardless of the experimental condition, but the degree of improvement was dependent upon group assignment. Individuals working alone or paired with an expert computer maintained a high level of performance throughout the experiment. Those paired with a computer-novice or another human performed poorly initially, but eventually reached the level of those in the other conditions. The results showed that team training can be as effective as individual training, but that the quality of training is moderated by the skill level of one's teammate. Moreover, these findings suggest that task partitioning of high performance skills between a human and a computer is not only possible but may be considered a viable option in the design of adaptive systems.

scholarly journals Prediksi Tinggi Badan Ditentukan dari Panjang Rentang Lengan dan Panjang Telapak Kaki Pada Mongoloid Feminina Khususnya Etnis Jawa

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Nur Mujaddidah Mochtar
Keyword(s):  
Clinical Medicine ◽  
Pearson Correlation ◽  
Body Height ◽  
Regression Equation ◽  
The Body ◽  
Regression Equations ◽  
Foot Length ◽  
Arm Span ◽  
Knee Height ◽  
The Mean

Background: There are various circumstances where measurements are not actually possible, replacement parameters can be used to estimate body height. Many characteristics of body height measurement and how to measure it. These include anthropometric measurements that can be used for the identification of medicolegal-forensic processes. Body height in clinical medicine and in the field of scientific research can be easily estimated using various anthropometric parameters such as arm span, knee height, foot length and foot breadth, and others. The arm span and foot length has proved to be one of the most reliable predictors. This study was conducted to estimate of body height from arm span and foot length using the regression equation and to determine the correlation between the body height and arm span and foot length.Methods: This study was conducted at Universitas Muhammadiyah Surabaya with 182 Javanese female students. Stature, arm span and foot length measured directly using anthropometric technique and measuring tape. The data obtained were then analyzed with SPSS version 16. The regression equation was derived for the estimate of body height and the relationship between stature, arm span and foot length determined by the Pearson correlation.               Results: We found that the mean body height of Javanese women was 1534,45 ± 47,623  mm, mean of arm span 1543,25 ± 60,468 mm and the mean of foot length 226,14 ± 9,586 mm. The correlation between stature and arm span was positive and significant (r = 0,715  , p <0,05). The correlation between stature and foot length was positive and significant (r = 0,726 , p <0,05). The correlation between stature and arm span and foot length was positive and significant (r = 0,798, p <0,05).               Conclusion: Body height correlates well with the arm span and foot length so that it can be used as a reliable marker for high estimates using regression equations.

scholarly journals PROBLEMS FOR ANTI-CRISIS ANALYTICAL PROCEDURES OF FINANCIAL STABILITY OF TRADE ENTERPRISES

2020 ◽  
Author(s):  
Ludmila Anipko ◽  
◽  
Irina Klimovych ◽  
Keyword(s):  
Financial Stability ◽  
Regression Equation ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Financial Condition ◽  
Financial Audit ◽  
Analytical Procedures ◽  
Financial Monitoring ◽  
Complex Indicators

Anti-crisis analytical procedures the financial stability of trade enterprises (hereinafter – AP FS) are part of the internal financial audit of economic activity. The system of financial monitoring is practically acceptable for the implementation of AP FS. The developed classification allows to determine the ability of the enterprise to implement AP FS. An analytical method has been developed that allows, based on the analysis of the financial condition and multivariate forecast, to develop measures to ensure the financial stability of the trade enterprise continuously. By interpolation, the study of the current financial situation, and extrapolation – a multivariate forecast, the numerical value of the integrated (complex) indicator that characterizes financial stability is determined by the regression equation, including factors listed in the classification, the significance of which is determined by regression equations. Based on the analysis of the numerical values of the regression coefficients, it is possible to determine the most important factors that affect the financial stability of trade enterprises, and those that have almost no effect on it. Components with significantly small numerical values of the regression coefficients can be generally discarded. This will reduce the number of indicators that affect financial stability and thus, you can reduce the number of components in the regression equation to the two three most important, which allows you to solve the problem of optimization. The expediency of using integrated and complex indicators is shown. The obtained results are only an information basis for the economic administration of the trade enterprise in making management decisions, the formation of long-term plans. The developed approaches to assessing the financial stability of enterprises are universal and can be used for enterprises in other sectors of the economy.

scholarly journals Physical Qualities Pertaining to Shorter and Longer Change-of-Direction Speed Test Performance in Men and Women

Sports ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 45 ◽  
Author(s):  
Robert Lockie ◽  
Brett Post ◽  
J. Dawes
Keyword(s):  
Test Performance ◽  
Vertical Jump ◽  
Anaerobic Power ◽  
Regression Equations ◽  
Lower Body ◽  
Training Time ◽  
Linear Speed ◽  
Change Of Direction ◽  
Explained Variance ◽  
Lower Body Power

This study investigated relationships between shorter (505, change-of-direction (COD) deficit as a derived physical quality) and longer (Illinois agility test; IAT) COD tests with linear speed, lower-body power (multidirectional jumping), and strength in recreationally-trained individuals. Twenty-one males and 22 females (similar to collegiate club-sport and tactical athletes) were assessed in: 505 and COD deficit from each leg; IAT; 20 m sprint; vertical jump (VJ height, peak anaerobic power measured in watts (PAPw), power-to-body mass ratio); standing broad jump; lateral jump (LJ) from each leg; and absolute and relative isometric midthigh pull (IMTP) strength. Partial correlations calculated sex-determined relationships between the COD and performance tests, with regression equations calculated (p < 0.05). The 505 and IAT correlated with all tests except PAPw and absolute IMTP (r = ±0.43–0.71). COD deficit correlated with the LJ (r = −0.34–0.60). Left- and right-leg 505 was predicted by sex, 20 m sprint, and left-leg LJ (70–77% explained variance). Right-leg COD deficit was predicted by sex and left-leg LJ (27% explained variance). IAT was predicted by sex, 20 m sprint, right-leg LJ, and relative IMTP (84% explained variance). For individuals with limited training time, improving linear speed, and relative lower-body power and strength, could enhance shorter and longer COD performance.

Comparison of dye-dilution and flowmeter estimates of pulmonary valve incompetence in dogs

1960 ◽  
Vol 15 (3) ◽  
pp. 465-472 ◽  
Author(s):  
P. I. Korner ◽  
J. B. Uther ◽  
J. P. Chalmers ◽  
R. Nicks
Keyword(s):  
Distribution Curve ◽  
Pulmonary Valve ◽  
Regression Equations ◽  
Forward Flow ◽  
Pooled Data ◽  
Quantitative Estimates ◽  
Single Determination ◽  
Frequency Distribution Curve ◽  
Good Agreement

Quantitative estimates of backflow were obtained in dogs with experimental pulmonary valve incompetence by means of a bristle flowmeter and the dye curve variance method. The variance of the curve was found to be the best index of indicator dispersion and is calculated from analogy to a frequency distribution curve. The method postulates that (forward flow + backflow)/forward flow = variance observed during incompetence/Vx(F; V), where Vx(F; V) is the expected variance for the same forward flow and volume between injection and sampling sites and is determined from regression equations calculated from data of normal dogs. There was good agreement between dye and flowmeter results, provided that Vx(F2;V) was estimated for the specific regression equations obtained from individual dogs. Three or four successive dye curves, obtained while the animal was in a steady state, permitted quantitation of backflow with the accuracy of a single determination of forward flow by the direct Fick or dye method. When Vx(F;V) was estimated from regression equations obtained from the pooled data and normal dogs, agreement with the flowmeter estimates was poor. Submitted on October 28, 1959

Time-Life, World Reporter and the secretary: experiments with end-users

1986 ◽  
Vol 12 (4) ◽  
pp. 167-175 ◽  
Author(s):  
David Nicholas ◽  
Kevin Harris ◽  
Gertrud Erbach
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Information Source ◽  
End Users ◽  
End User ◽  
Training Time ◽  
Wide Scale ◽  
Time To Learn ◽  
Information Retrieval Methods ◽  
Near Future

After six months of training Time-Life book researchers to use online databases it is clear that they will not become end-users overnight—despite plentiful training, good facilities, user-friendly interfaces and the like. The reasons for this are less clear but high on the list come: a lack of time (to learn and maintain the necessary searching skills); a general reluctance to abandon the tried and tested—and often pleasurable—con ventional information retrieval methods (there is certainly nothing to suggest that the computer is going to replace the telephone as an information source); and the low priority given to the (formal) information-seeking component of the job (high priority being given to the more visible and pressing elements, like writing and commissioning pictures). There is little in Time-Life's online experience to lend support to the belief that there will be wide-scale end-user searching in the near future. Online will find its place in the array of information retrieval methods at the disposal of the user and will undoubtedly be used where manual methods have failed: it is unlikely, however, to supplant manual systems that work well and are well-liked. Secretaries do appear to be well-qualified, and in an excel lent position to become a major end-user group and might indeed pose a threat to the librarian intermediary in the near future.

scholarly journals Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO

2019 ◽  
Vol 16 (14) ◽  
pp. 2492 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Cheng ◽  
Cheng
Keyword(s):  
Policy Development ◽  
Sediment Concentration ◽  
Ordinary Least Squares ◽  
Complex Problem ◽  
Regression Equation ◽  
Management Policy ◽  
Major Influence ◽  
Chaohu Lake ◽  
Regression Equations ◽  
Lasso Regression

Black blooms are a serious and complex problem for lake bays, with far-reaching implications for water quality and drinking safety. While Fe(II) and S(−II) have been reported as the most important triggers of this phenomenon, little effort has been devoted in investigating the relationships between Fe(II) and S(−II) and the host of potentially important aquatic factors. However, a model involving many putative predictors and their interactions will be oversaturated and ill-defined, making ordinary least squares (OLS) estimation unfeasible. In such a case, sparsity assumption is typically required to exclude the redundant predictors from the model, either through variable selection or regularization. In this study, Bayesian least absolute shrinkage and selection operator (LASSO) regression was employed to identify the major influence variables from 11 aquatic factors for Fe(II), S(−II), and suspended sediment concentration (SSC) in the Chaohu Lake (Eastern of China) bay during black bloom maintenance. Both the main effects and the interactions between these factors were studied. The method successfully screened the most important variables from many items. The determination coefficients (R2) and adjusted determination coefficients (Adjust R2) showed that all regression equations for Fe(II), S(-II), and SSC were in good agreement with the situation observed in the Chaohu Lake. The outcome of correlation and LASSO regression indicated that total phosphorus (TP) was the single most important factor for Fe(II), S(-II), and SSC in black bloom with explanation ratios (ERs) of 76.1% , 37.0%, and 12.9%, respectively. The regression results showed that the interaction items previously deemed negligible have significant effects on Fe(II), S(−II), and SSC. For the Fe(II) equation, total nitrogen (TN) × dissolved oxygen (DO) and chlorophyll a (CHLA) × oxidation reduction potential (ORP), which contributed 10.6% and 13.3% ERs, respectively, were important interaction variables. TP emerged in each key interaction item of the regression equation for S(−II). Water depth (DEP) × Fe(II) (30.7% ER) was not only the main interaction item, but DEP (5.6% ER) was also an important single factor for the SSC regression equation. It also indicated that the sediment in shallow bay is an important source for SSC in water. The uncertainty of these relationships was also estimated by the posterior distribution and coefficient of variation (CV) of these items. Overall, our results suggest that TP concentration is the most important driver of black blooms in a lake bay, whereas the other factors, such as DO, DEP, and CHLA act in concert with other aquatic factors. There results provide a basis for the further control and management policy development of black blooms.

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