Augsburg, University, Diss., 2008.

Pete Riegel, a well-known sport expert, proposed the formula of race time prediction in 1977. This article discusses whether it is also suitable for Taiwan marathoners. We compiled two hundred and four effective datum by questionary. Some variables possible to affect the running result are added in this work, namely: sex, age, the year of run, height, weight, the race number of marathon, the quantity and the frequency of practices each week. Next, we use multiple regression and sliced inverse regression to increase the accuracy of the running time prediction. The best model, found here has eighty percentage's player with predictive error within fifteen minuates, which is better than the original model by Riegel(1977) with only having sixty-two percentages.

Konstanz, Univ., Diplomarb., 2001.

No description available.

The purpose of this study was to examine whether there are significant relationships between the general satisfaction of students and learner-content interaction, learner-instructor interaction, learner-learner interaction, and learner-technology interaction in 100% online courses. There were 310 responses from the students. This study did not use data from duplicate students and instructors. Excel was used to find duplicate students and instructors; therefore, 128 responses were deleted. After examination of box plots, an additional four cases were removed because they were outliers on seven or more variables. Nineteen responses were deleted because they did not answer all questions of interest, resulting in a total sample of 159 students. Multiple regression analysis was used to examine the relationship between the four independent variables and the dependent variable. In addition to tests for statistical significance, practical significance was evaluated with the multiple R2 , which reported the common variance between independent variables and dependent variable. The two variables of learner-content and learner-instructor interaction play a significant role in predicting online satisfaction. Minimally, the variable learner-technology can predict online satisfaction and is an important construct that must be considered when offering online courses. Results of this study provide help in establishing a valid and reliable survey instrument and in developing an online best learning environment, as well as recommendations for institutions offering online learning or considering the development of online learning courses.

Doctor of Education / Department of Educational Leadership / Tweed R. Ross / The purpose of this quantitative study was to examine the relationship between the length of tenure of a superintendent and academic achievement as defined by the percentage of students who scored “Proficient” or better on the 2008 Third Grade Kansas Reading Assessment. To put this relationship into context, five other predictive variables were included as a part of this study: the individual’s total length of experience as a superintendent, the individual’s total length of experience in education, each district’s assessed valuation per pupil, each district’s percentage of students who qualified for free or reduced meal prices, and each district’s total student headcount. To gain the most comprehensive view possible, all 295 Kansas school districts in existence in 2008 were included in this study. The backward method of multiple regression was utilized to analyze these data. Before performing this analysis, the researcher first checked to ensure that the assumption of no multicollinearity had been met. From this analysis, all six predictive variables were retained as no relationships between them were found to be too strong. Following this check, the backward method of multiple regression analysis was performed. This method of multiple regression seeks to create the most parsimonious model, so two of the predictive variables were excluded from the final summary model based on removal criterion, the significance value of the t-test of each predictive variable. Results of this study revealed that 9.9% of the variance in the dependent variable, the percentage of students who scored “Proficient” or better on the 2008 Third Grade Kansas Reading Assessment, was accounted for by the predictive variables in the model retained. Further, multiple regression analysis tested the unique contributions of the four remaining predictive variables. Although included as one of the four predictive variables that had a significant effect on the percentage of students who scored “Proficient” or better on the 2008 Third Grade Kansas Reading Assessment, the primary focus of this study – to examine the impact a superintendent’s length of tenure has on students’ academic achievement – proved to have the least relative impact, according to beta weights.

The purpose of this study was to examine the potential of three independent psychological scales (Rotter's Locus of Control, Karasek's Job Content Questionnaire [non-injury job stress], and Rosenberg's Self-Esteem Scale) to predict job satisfaction, as measured by Brayfield and Rothe's Index of Job Satisfaction, among salespersons in a small independent department store in Wichita Falls, Texas. An 82-item survey which examined the dynamics of a salesperson's work life was administered to 20 individuals who were full-time employees of the department store. Demographic data were also gathered although these factors were not entered into the regression analysis. A multiple regression procedure examined the responses of the 20 employees who participated in the study. The R-squared coefficient indicates that 41 percent of the variance in Job Satisfaction was explained by the three predictor measures. A major proportion of this unexplained variance may be in variables outside the scope of this study, e.g., salaries, vacation time, benefits, bonuses, or commissions. Results suggest that the independent variables measured by the Locus of Control Scale and the Job Content Questionnaire in combination were the best predictors of job satisfaction with a significance level of .01. The single best predictor was the Job Content Questionnaire, significant at .03. The three instruments (Locus of Control, Self-Esteem, and Job Content Questionnaire) which comprised the independent variables, reached a significance level of .03 in their prediction of job satisfaction (Brayfield-Rothe Index of Job Satisfaction). Study results indicate that a majority of the employees in the sample population were satisfied with their jobs and with the leadership style manifested by the store manager. In addition, job security was believed to be satisfactory. Inasmuch as there is a void in the literature regarding personal characteristics of salespersons as variables that interact with job satisfaction, comparisons of the findings of this research with other studies that have explored the intricacies of job satisfaction among salespersons who work in small, independent department stores cannot be made. Further research on the predictability of job satisfaction among salespersons in small, independent retail operations such as the department store investigated in this study would be useful not only to managerial staff in decision making and personnel management but would promote greater understanding of the personal characteristics of salespersons as human investment capital which has the potential to create the effective competitive edge required for survival in the new economy.

This empirical study is aimed to analyze the determinants of residential water demand and performed water use practice at household level in Hawassa. This study will fill the research gap and information on factors affecting household water demand in regions being water scarce and will provide useful information for policy-makers and water utility planners in order to use scarce drinking water resource more efficiently. In this study the proposed potential factors determine household water demand in Hawassa were; Socioeconomic and demographic characteristics, the average monthly household expenditure, use of water appliances and household water use patterns for various purposes, and household awareness towards water source conservation. The cross sectional survey was done in 169 rondomly selected households. The collected Data was analyzed using multiple regression models with different functional forms (linear, semi-log) and heteroskedaticity corrected model was also used in each of functional forms to examine the structural relationship between the quantity of water demand and explanatory variables. The gretl statisitcal software package was used. The descriptive statistics analysis was also followed to present results in tables, charts and graphs (mean, median, minimum, maximum, frequency...

American schools have been struggling with improving achievement in science, technology, engineering, and mathematics for decades. For the last four decades, the overall mathematics performance of 17 year-olds on the National Assessment of Educational Progress has not shown any significant improvement. Mathematics teachers can use metacognitive techniques to make immediate adjustments in instruction that may assist students in becoming more skillful problem solvers. The purpose of this study was to provide new knowledge about the potential predictors of mathematics teachers' use of the six subfactors of the Metacognitive Awareness Inventory for Teachers. The inventory was administered to 120 K-12 grade teachers from the membership list of the National Council of Teachers of Mathematics via an online survey. Multiple regression analysis indicates that there are significant differences among the participants in the influence of potential predictor variables for declarative knowledge, procedural knowledge, conditional knowledge, planning awareness, and monitoring awareness. The positive β coefficient indicates that the number of years of teaching experience plays a role in increasing the mathematics teachers' awareness of metacognition, β=.207, p

This study investigated the effectiveness of various analytical methods used for estimating R2 shrinkage in multiple regression analysis. Two categories of analytical formulae were identified: estimators of the population squared multiple correlation coefficient (ρ2), and estimators of the population cross-validity coefficient (ρc2). To avoid possible confounding factors that might be associated with a real data set such as data nonnormality, lack of precise population parameters, different degrees of multicollinearity among the predictor variables, and so forth, the Monte Carlo method was used to simulate multivariate normal sample data, with prespecified population parameters such as the squared multiple correlation coefficient (ρ2), number of predictors, different sample sizes, known degree of multicollinearity, and controlled data normality conditions. Five hundred replicates were simulated within each cell of the sampling conditions. Various analytical formulae were applied to the simulated data in each sampling condition, and the "adjusted" coefficients were obtained and then compared to their corresponding population parameters (ρ2 and ρc2). Analysis of the results indicates that the currently most widely used (in both SAS and SPSS) "Wherry" formula is probably not the most effective analytical formula in estimating ρ2. Instead, the Pratt formula appeared to outperform other analytical formulae across most of these sampling conditions. Among the analytical formulae designed to estimate ρc2, the Browne formula appeared to be the most effective and stable in minimizing statistical bias across different sampling conditions. The study also concludes that it is the n/p (sample size/number of predictor variables) ratio that affects the performances of these analytical formulae the most; different degrees of multicollinearity among predictor variables do not have dramatic influence on the performances of these analytical formulae. Further replicants on both real and simulated data re still needed to investigate the effectiveness of these analytical formulae.