Studentization and prediction problems in multivariate multiple regression

1985 ◽  
Vol 14 (6) ◽  
pp. 1251-1258 ◽  
Author(s):  
Irwin Guttman ◽  
P. Hougaard
Keyword(s):  
Multiple Regression ◽  
Multivariate Multiple Regression ◽  
Prediction Problems

scholarly journals Cycling performance prediction based on cadence analysis by using multiple regression

2021 ◽  
Vol 2107 (1) ◽  
pp. 012058
Author(s):  
Sukhairi Sudin ◽  
Azizi Naim Abdul Aziz ◽  
Fathinul Syahir Ahmad Saad ◽  
Nurul Syahirah Khalid ◽  
Ismail Ishaq Ibrahim
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Regression Analysis ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Cycling Performance ◽  
Continuous Output ◽  
Independent Variable ◽  
Prediction Problems

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.

Multivariate Multiple Regression Prediction Models: A Euclidean Distance Approach

2003 ◽  
Vol 92 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Effect Size ◽  
Euclidean Distance ◽  
Prediction Models ◽  
Regression Coefficients ◽  
Multiple Response ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances ◽  
Distance Approach

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

Antecedents Of, Temporal Changes In, and Relationships between CSAI-2 Subcomponents

1984 ◽  
Vol 6 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Daniel Gould ◽  
Linda Petlichkoff ◽  
Robert S. Weinberg
Keyword(s):  
Multiple Regression ◽  
State Anxiety ◽  
Multivariate Analyses ◽  
Temporal Changes ◽  
Somatic Anxiety ◽  
Analysis Techniques ◽  
Self Confidence ◽  
Cognitive Anxiety ◽  
Multivariate Multiple Regression ◽  
And Performance

Two studies were conducted to examine antecedents of, relationships between, and temporal changes in the cognitive anxiety, somatic anxiety, and the self-confidence components of the Martens, Burton, Vealey, Bump, and Smith (1983) newly developed Competitive State Anxiety Inventory-2 (CSAI-2). In addition, the prediction that cognitive and somatic anxiety should differentially influence performance was examined. In Study 1, 37 elite intercollegiate wrestlers were administered the CSAI-2 immediately before two different competitions, whereas in Study 2, 63 female high school volleyball players completed the CSAI-2 on five different occasions (1 week, 48 hrs, 24 hrs, 2 hrs, and 20 min) prior to a major tournament. The results were analyzed using multiple regression, multivariate multiple regression, univariate and multivariate analyses of variance, and general linear model trend analysis techniques. The findings supported the scale development work of Martens and his colleagues by verifying that the CSAI-2 assesses three separate components of state anxiety. A number of other important findings also emerged. First, the prediction was confirmed that somatic anxiety increases during the time leading to competition, while cognitive anxiety and confidence remain constant. Second, CSAI-2 subscales were found to have different antecedents, although the precise predictions of Martens and his colleagues were not supported. Third, the prediction that cognitive anxiety would be a more powerful predictor of performance than somatic anxiety was only partially supported. Fourth, the prediction that precompetitive anxiety differences between experienced and inexperienced athletes initially found by Fenz (1975) result from somatic anxiety changes was not supported. It was concluded that the CSAI-2 shows much promise as a multidimensional sport-specific state anxiety inventory, although more research is needed to determine how and why specific antecedent factors influence various CSAI-2 components and to examine the predicted relationships between CSAI-2 components and performance.

Multivariate Multiple Regression Analyses: A Permutation Method for Linear Models

2002 ◽  
Vol 91 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Multiple Regression ◽  
Euclidean Distance ◽  
Linear Models ◽  
Latin Square ◽  
Experimental Designs ◽  
Regression Analyses ◽  
Permutation Procedure ◽  
Multivariate Extension ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances

A multivariate extension of a univariate procedure for the analysis of experimental designs is presented. A Euclidean-distance permutation procedure is used to evaluate multivariate residuals obtained from a regression algorithm, also based on Euclidean distances. Applications include various completely randomized and randomized block experimental designs such as one-way, Latin square, factorial, nested, and split-plot designs, with and without covariates. Unlike parametric procedures, the only required assumption is the randomization of subjects to treatments.

scholarly journals Organizational cooperation and knowledge management in research and development organizations

Psihologija ◽  
2017 ◽  
Vol 50 (1) ◽  
pp. 1-20
Author(s):  
Dos Rebelo ◽  
Leonor Pais ◽  
Lisete Mónico ◽  
Luísa Rebelo ◽  
Carolina Moliner
Keyword(s):  
Knowledge Management ◽  
Research And Development ◽  
Multiple Regression ◽  
Short Form ◽  
Regression Analyses ◽  
Large Effect Size ◽  
Development Organizations ◽  
Multivariate Multiple Regression ◽  
Organizational Cooperation ◽  
A Current

Organizational Cooperation (OC) is a current concept that responds to the growing interdependence among individuals and teams. Likewise, Knowledge Management (KM) accompanies specialization in all sectors of human activity. Most KM processes are cooperation-intensive, and the way both constructs relate to each other is relevant in understanding organizations and promoting performance. The present paper focuses on that relationship. The Organizational Cooperation Questionnaire (ORCOQ) and the Short form of the Knowledge Management Questionnaire (KMQ-SF) were applied to 639 members of research and development (R&D) organizations (Universities and Research Institutes). Descriptive, correlational, linear multiple regression and multivariate multiple regression analyses were performed. Results showed significant positive relationships between the ORCOQ and all the KMQ-SF dimensions. The prediction of KMQ-SF showed a large effect size (R2 = 62%). These findings will impact on how KM and OC are seen, and will be a step forward in the development of this field.

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