Anthropometric characteristics are a non-negligible factor even in world's elite 100-m sprinters
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BACKGROUND: Previous studies have simply shown correlation relationships between spatiotemporal variables and anthropometric characteristics. In case targeted only elite sprinter, there is a possibility that the relationship may differ from previous study. The purpose of this study was to clarify the relationship between anthropometric characteristics and spatiotemporal variables, and to assess the relative influence of determinant anthropometric characteristics on spatiotemporal variables in world-class sprinters.METHODS: Study participants were 64 of 117 world-class sprinters who had a personal record of less than 9.99 seconds in a 100-m sprint. Correlations between the anthropometric characteristics and spatiotemporal variables were analyzed, and multiple linear regression analysis was performed to determine the ability of determinant anthropometric characteristics to predict spatiotemporal variables.RESULTS: Average step length was significantly positively correlated with height and weight, and average step frequency was significantly negatively correlated with height and weight. There were no significant correlations between spatiotemporal variables and BMI. Multiple linear regression revealed that both height and weight were significant predictors of step length, and height was the only significant predictor of step frequency. The coefficients of determination (R2) for step length and step frequency were 0.423 and 0.354, respectively.CONCLUSIONS: These results suggest that anthropometric characteristics such as height and weight are major determinants of step frequency and length even in world-class sprinters.Keywords:
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Taking the region around the Qinghai Lake as the study area and using the Landsat Thematic Mapper data and the measured grass yield data, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between grassland biomass and the vegetation indices. There are two types of sampling site, i.e., the larger one is 30 m×30 m and the smaller one is 1 m×1 m. Each larger sampling site includes one smaller one which was randomly selected. The major conclusions from this study are: 1) the fitting accuracies of the non-linear regression models are much higher than those of the non-linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the measured grassland biomass data in comparison with those from the monadic linear regression models; 2) the comparison of different forms of the non-linear regression analysis on the relations between the vegetation indices and the measured grassland biomass data indicates that the cubic equation is the best one in terms of the suitability of use in the study area; 3) the results from the non-linear regression analysis show that the order is RVI, NDVI, SAVI, MSAVI and DVI in terms of the fitting accuracy between these vegetation indices and grassland biomass data; and 4) the non-linear model Y = -18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around the Qinghai Lake.
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2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (2021)
This paper analyzes the influencing factors of highway passenger and freight traffic, determines its influencing factors, and collects relevant statistical data from 117 different regions. Based on the principle of multiple linear regression method, first all variables are incorporated into the multiple regression equation for simulation. Second, integrate, demonstrate the applicability of the model, and then use the stepwise multiple regression method for model fitting. Based on this idea, the multiple linear regression model is constructed and forecasted for the highway passenger and freight volume. The results show that the stepwise multiple regression is effective. While the number of variables is greatly reduced and the calculation process is simplified, the model's fit is still good, and the problem of collinear between multiple variables is solved, and the regression coefficient of the variable is not consistent with the actual problem, and the result is predicted, It is also consistent with the actual situation and the applicability of the verification method, which can provide application references for road passenger and freight volume forecasting in other related areas.
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Experimental findings of the working capacity at a heart rate of 170 bts/min (W170) were compared to predicted values. Statistical tests were applied to examine the suitability and the error of prediction of three different regression models: a linear regression line, a polynomial regression model, and a "break point" regression model, which were compared to the time course of the heart rate during a linearly increasing work load from 0 to 100 W during 10 min. For this study the results of 28 children, 15 and 16 years old, and students of physical education were investigated. When a linear regression line was compared to these data, systematic deviations between measured data and the values estimated by this model were found. When the W170 was predicted using this model from the data collected during the first 10 min of an exercise procedure for the determination of the heart rate index, the physical working capacity was overestimated. The polynomial regression model and the "break point" regression model agreed with the time course of the heart rate without systematic error and allowed an unbiased prediction of the W170 from the first 10 min of the exercise test.
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Coke strength was predicted by selecting the indexes of blending coal quality.The indexes were used as independent variables for multiple linear regression analysis and got regression models.Then took regression coefficient to make t test,eliminated the no significant index and renewed to make the linear regression models.The research indicated that the model can be used to effectively predict coke strength and the relative error forecasting results was within 5%.
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Abstract This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000. Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days’ worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable’s mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.
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Physical and anthropometric characteristics have been associated with differences between levels of swimming performance, swimming events, training status, gender, and age. It has been suggested that the greater body fatness observed in girl swimmers than boy swimmers could explain the gender differences in performance. Few studies, however, have examined gender differences in the physical and anthropometric characteristics of young swimmers. The purpose of this study was to compare the body composition, body build, and anthropometric characteristics of boy and girl sprint swimmers. Two groups (boys, n = 38 and girls, n = 31) of sprint swimmers (mean age ± SD = 11.03 ± 2.29 and 10.45 ± 2.29, years, respectively) volunteered for this study. The subjects were members of local swimming clubs who competed in sprint swimming events (≤ 200 m). Gender comparisons were made for age, body weight (BW), height (HT), fat-free weight (FFW), percent body fat (%fat), endomorphic rating, mesomorphic rating, ectomorphic rating, sum of 12 diameters, sum of 11circumferences, biacromial diameter/biiliac diameter, and FFW/HT. The results of the independent t-tests indicated that the only mean differences between the boy and girl sprint swimmers were for % fat (boys = 9.40 ± 5.35 % fat; girls = 12.73 ± 6.19 % fat) and endomorphic rating (boys = 2.87 ± 0.96; girls = 4.29 ± 1.22). For the current age group of sprint swimmers, the only gender differences were for measures associated with body fatness and there were no differences for body build measures associated with musculoskeletal size, muscularity, skeletal size, total body mass, or body breadth dimensions. Further studies are needed to examine gender differences in the body composition and body build of distance swimmers, older sprint and distance swimmers, and athletes in sports other than swimming. These findings suggest that gender differences in sprint swimming performance may be reduced through training programs for girls designed to reduce body fatness.
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Stepwise regression
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The aim of the study is to achieve a statistical analysis of linear regression models of specific industrial processes data. The work strategy involves the regression analysis which is the most widely used statistical tools to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. This application focused on the fitting and checking of linear regression models, using small and large data sets, with pocket calculators or computers. The performance of regression analysis methods in practice depends on the form of the data generating process , and how it relates to the regression approach being used. It was used some statistical criteria as: Cochran criteria; Student criteria and Fischer criteria. After solving statistical analysis of the linear regression models, in the end there was obtained an applied statistical analysis of the linear regression model through the use of the classical method with a pocket computer. The same data were calculated with C++ software. By using this software we obtained more accurate results and the application time was reduced by several hours to 2-3 minutes.
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A 12 week kayak training programme was evaluated in children who either had or did not have the anthropometric characteristics identified as being unique to senior elite sprint kayakers. Altogether, 234 male and female school children were screened to select 10 children with and 10 children without the identified key anthropometric characteristics. Before and after training, the children completed an all-out 2 min kayak ergometer simulation test; measures of oxygen consumption, plasma lactate and total work accomplished were recorded. In addition, a 500 m time trial was performed at weeks 3 and 12. The coaches were unaware which 20 children possessed those anthropometric characteristics deemed to favour development of kayak ability. All children improved in both the 2 min ergometer simulation test and 500 m time trial. However, boys who were selected according to favourable anthropometric characteristics showed greater improvement than those without such characteristics in the 2 min ergometer test only. In summary, in a small group of children selected according to anthropometric data unique to elite adult kayakers, 12 weeks of intensive kayak training did not influence the rate of improvement of on-water sprint kayak performance.
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