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<p> The estimators of the mean, standard deviation and group effects for one-way-classification experimental designs are obtained from type I censored samples. The bias and the variances and covariances of these estimators are evaluated. A test statistic is proposed for testing a linear contrast of the group-effects. Two numerical examples are presented.</p> / Thesis / Master of Science (MSc)

Although it is well established that spacing of piles within a pile group influences the lateral load resistance of that group, additional research is needed to better understand trends for large pile groups (greater than three rows) and for groups in sand. A 15-pile group in a 3x5 configuration situated in sand was laterally loaded and data were collected to derive p-multipliers. A single pile separate from the 15-pile group was loaded for comparison. Results were compared to those of a similar test in clays. The load resisted by the single pile was greater than the average load resisted by each pile in the pile group. While the loads resisted by the first row of piles (i.e. the only row deflected away from all other rows of piles) were approximately equal to that resisted by the single pile, following rows resisted increasingly less load up through the fourth row. The fifth row consistently resisted more than the fourth row. The pile group in sand resisted much higher loads than did the pile group in clay. Maximum bending moments appeared largest in first row piles. For all deflection levels, first row moments seemed slightly smaller than those measured in the single pile. Maximum bending moments for the second through fifth rows appeared consistently lower than those of the first row at the same deflection. First row moments achieved in the group in sand appeared larger than those achieved in the group in clay at the same deflections, while bending moments normalized by associated loads appeared nearly equal regardless of soil type. Group effects became more influential at higher deflections, manifest by lower stiffness per pile. The single pile test was modeled using LPILE Plus, version 4.0. Soil parameters in LPILE were adjusted until a good match between measured and computed responses was obtained. This refined soil profile was then used to model the 15-pile group in GROUP, version 4.0. User-defined p-multipliers were selected to match GROUP calculated results with actual measured results. For the first loading cycle, p-multipliers were found to be 1.0, 0.5, 0.35, 0.3, and 0.4 for the first through fifth rows, respectively. For the tenth loading, p-multipliers were found to be 1.0, 0.6, 0.4, 0.37, and 0.4 for the first through fifth rows, respectively. Design curves suggested by Rollins et al. (2005) appear appropriate for Rows 1 and 2 while curves specified by AASHTO (2000) appear appropriate for subsequent rows.

The present dissertation research has three purposes: the first one is to predict anthropogenic deforestation caused by small farmers firstly using only pathways of past land cover change and secondly using demographic, socioeconomic and land cover data at the farm level. The second purpose is to compare the explanatory and predictive capacity of both approaches at identifying areas at high risk of deforestation among small farms in Rondõnia, Brazil. The third purpose is to test the assumptions of stationary probabilities and homogeneous subjects, both commonly used assumptions in predictive stochastic models applied to small farmers' deforestation decisions. This study uses the following data: household surveys, maps, satellite images and their land cover classification at the pixel level, and pathways of past land cover change for each farm. These data are available for a panel sample of farms in three municipios in Rondõnia, Brazil (Alto Paraiso, Nova União, and Rolim de Moura) and cover a ten-year period of study (1992-2002). Pathways of past land cover change are graphic representations in the form of flow charts that depict Land Cover Change (LCC) in each farm during the ten-year period of study. Pathways were constructed using satellite images, survey data and maps, and a set of interviews performed on a sub-sample of 70 farms. A panel data analysis of the estimated empirical probabilities was conducted to test for subject and time effects using a Fixed Group Effects Model (FGEM), specifically the Least Square Dummy Variable (LSDV1) fixed effects technique. Finally, the two predictive modeling approaches are compared. The first modeling approach predicts future LCC using only past land cover change data in the form of empirical transitional probabilities of LCC obtained from pathways of past LCC. These empirical probabilities are used in a LSDV1 for fixed–group effects, a LSDV1 for fixed-time effects, and an Ordinary Least Square model (OLS) for the pooled sample. Results from these models are entered in a modified Markov chain model's matrix multiplication. The second modeling approach predicts future LCC using socio-demographic and economic survey variables at the household level. The survey data is used to perform a multinomial logit regression model to predict the LC class of each pixel. In order to compare the explanatory and predictive capacity of both modeling approaches, LCC predictions at the pixel level are summarized in terms of percentage of cells in which future LC was predicted correctly. Percentage of correct predicted land cover class is compared against actual pixel classification from satellite images. The presence of differences among farmers in the LSDV1-fixed group effect by farmer suggests that small farmers are not a homogeneous group in term of their probabilities of LCC and that further classification of farmers into homogeneous subgroups will depict better their LCC decisions. Changes in the total area of landholdings proved a stronger influence in farmer's LCC decisions in their main property (primary lot) when compared to changes in the area of the primary lot. Panel data analysis of the LCC empirical transition probabilities (LSDV1 fixed time effects model) does not find enough evidence to prefer the fixed time effects model when compared to a Ordinary Least Square (OLS) pooled version of the probabilities. When applying the results of the panel data analysis to a modified markov chain model the LSDV1-farmer model provided a slightly better accuracy (59.25% accuracy) than the LSDV1-time and the OLS-pooled models (57.54% and 57.18%, respectively). The main finding for policy and planning purposes is that owners type 1—with stable total landholdings over time—tend to preserve forest with a much higher probability (0.9033) than owner with subdividing or expanding properties (probs. of 0.0013 and 0.0030). The main implication for policy making and planning is to encourage primary forest preservation, given that the Markov chain analysis shows that primary forest changes into another land cover, it will never go back to this original land cover class. Policy and planning recommendations are provided to encourage owner type 1 to continue their pattern of high forest conservation rates. Some recommendations include: securing land titling, providing health care and alternative sources of income for the OT1's family members and elderly owners to remain in the lot. Future research is encouraged to explore spatial autocorrelation in the pixel's probabilities of land cover change, effects of local policies and macro-economic variables in the farmer's LCC decisions. / Ph. D.

Die vorliegende Dissertation beschäftigt sich modelltheoretisch mit drei Themenbereichen aus dem Feld der Bildungsforschung. Das erste Kapitel behandelt die Existenz von Studiengebühren. Bei der persönlichen Entscheidung für oder gegen ein Studium sind Studiengebühren ein Nachteil, vor allem, wenn dafür kein Angebot in Form von besseren Studienbedingungen existiert. Andererseits ist das Studium nicht nur eine finanzielle Entscheidung, sondern kann auch Nutzen in anderer Form wie Status oder Prestige bedeuten. Wenn der Status negativ von der Anzahl der Absolventen abhängt, könnte es von Vorteil sein, die Menge an Studenten künstlich durch Studiengebühren zu reduzieren. Es wird ein Modell präsentiert, in dem in einem statischen Gleichgewicht wohlhabendere und fähigere Studenten für höhere Studiengebühren stimmen, um die Absolventenzahl zu verknappen und den Status zu erhöhen. Das darauffolgende Kapitel enthält eine Erweiterung eines Signaling-Modells zur Notenvergabe. Das ursprüngliche Modell kommt zu dem Ergebnis, dass Noteninflation unausweichlich ist, da gute Noten der Schule keine Kosten verursachen. Es existiert aber in der Realität ein gegenläufiger Effekt: Noteninflation hat Einfluss auf die Reputation einer Schule und führt daher bei zukünftigen Absolventen zu einer Erwartungsanpassung der Arbeitgeber. Die Erweiterung des Modells zeigt, dass Noteninflation mit Reputation verringert oder sogar vermieden werden kann. Im letzten Kapitel wird ein Modell präsentiert, das den Einfluss eines separierenden Schulsystems und eines Gesamtschulsystems auf die akademische Leistung abbildet. Es wird zwischen einer Anfangsfähigkeit eines Schülers unterschieden, die hauptsächlich durch die Familienherkunft bestimmt wird, und der Lernfähigkeit eines Schülers. Dabei wird gezeigt, dass die Leistung schlechterer Schüler in der Gesamtschule steigen kann, selbst wenn es keine Synergieeffekte zwischen guten und schlechten Schülern gibt. Der Effekt entsteht, da die Gesamtschule einen Kompromiss im Anspruchsniveau finden muss, welcher höher ist als das Anspruchsniveau in der separierten, schlechteren Klasse. Wenn die schlechteren Schüler die größere Lernfähigkeit besitzen, erhöht sich in der Gesamtschule sogar die Durchschnittsleistung aller Schüler.

No description available.

A series of static lateral load tests were conducted on a group of fifteen piles arranged in a 3x5 pattern. The piles were placed at a center-to-center spacing of 3.92 pile diameters. A single isolated pile was also tested for comparison to the group response. The subsurface profile consisted of cohesive layers of soft to medium consistency underlain by interbedded layers of sands and fine-grained soils. The piles were instrumented to measure pile-head deflection, rotation, and load, as well as strain versus pile depth.