The main purpose of the present study was to investigate the relationship between the Miller Analogies Test scores of graduate school students and their undergraduate grade-point averages, as well as to find the relationship between Miller Analogies Test scores and the grade-point averages in graduate school of these students. A secondary purpose of this study was to investigate the relationship between each of the following variables: sex, age, curriculum and the performance levels of the subjects.

The Swedish housing market has experienced an almost constant increase of housing prices since the economic crisis in the early 90‟s. Many studies have been conducted on the field which have tried to find an explanation to the constant trend and if there is an end in sight. However, this study aims at focusing on the supply/demand relationship in determining the housing prices in the County of Stockholm. The method that was used was both a time series regression and a cross sectional regression, by applying data on the amount of housing that has been constructed per thousand inhabitants in each municipality, the development of housing prices in each municipality and the average annual development of wages. Since there are 26 municipalities in Stockholm County, it would be too time consuming to go through each and every single one of the municipalities, instead the focus was on the 5 municipalities with the highest and lowest construction rate per thousand inhabitants. Thus, we can observe if there is any general difference depending on the construction rate in determining the house price development. The results on the time series regression implies that most of the municipalities housing prices are primarily dependent on the housing construction rate, when construction goes down the prices goes up and vice versa. However, the municipality of Vallentuna had suspicious signs which imply that other factors (then the variables used) are driving the prices up. In the cross sectional regression where both the 5 highest and lowest municipalities with construction rate were regressed together, we can see similar signs as in Vallentuna. It would therefore be interesting to find out what the underlying factors that are driving the prices up in the case of Vallentuna and in the cross sectional analysis.

This thesis seeks to shed further light on how blockchain technology has fostered increases in efficiency at an overall level looking specifically at the Ethereum network, and a continued analysis of its evolving state. A network at the forefront of blockchain technology and smart contract utilization. Firstly, we introduce blockchain technology itself and the various facets of the technology, including consensus protocols, smart contracts and smart contract applications. Subsequently, we further analyse and showcase how blockchain technology has developed in efficiency over its maturation recently. Through the employment of various econometric models and strong discussion promotes insights to the key metrics of the Ethereum network. Finally, we explore whether we are able to note these changes over time and look to the future of blockchain technology. Not only to shed light on how this evolving state will continue to cultivate but also detail the other necessary advances needed to continue this growth.

The current National Academies of Sciences, Engineering and Medicine (NASEM) dry matter intake (DMI) prediction models are inadequate for DMI prediction of beef cattle in the Northern Great Plains. Four studies were conducted to account for additional variation in DMI and average daily gain (ADG) caused by weather variables. Experiment 1 and 2 had 13,895 steer-weeks observations, experiment 3 had 13,739 steer-weeks observations, and experiment 4 had 2,161 cow-weeks observations, respectively. Experiment 1 examined the influence of ambient temperature and solar radiation on DMI of beef steers. In experiment 2, 3, and 4, we examined the influence of ambient temperature, range of temperature, dew point, solar radiation, wind speed and their lags (two-week lag and monthly lag) on DMI of beef steers, ADG of beef steers, and DMI of beef cows, respectively. After adjusting for week of the year, linear and quadratic relationships of predictor variables on response variables were evaluated. In experiment 1 and 2, body weight (BW) had both linear and quadratic relationship with DMI of steers. In experiment 3 and 4, BW had a linear relationship with ADG of steers and DMI of cows, respectively. Week of the year, BW, and dietary energy density (NEm) were accounted for in the base model in experiment 1, 2 and 4 while in experiment 3, DMI was also accounted for. For the models, stepwise regression procedure was utilized. In experiment 1, ambient temperature and solar radiation interacted (P = 0.0001) and accounted for additional variation in DMI of beef steers. In experiment 2, weather variables and their interactions (P = 0.0001) accounted for additional variation in DMI of beef steers. In experiment 3, weather variables (P = 0.0001) accounted for additional variation in ADG of beef steers. In experiment 4, wind speed interacted (P <0.001) with ambient temperature and range of temperature which all accounted for additional variation in DMI of beef cows. These studies show that weather variables interact and cause variation in DMI and ADG in beef cattle. This has helped in better understanding the relationship between weather variables with DMI and ADG. This will improve the accuracy of DMI and ADG prediction equations and help beef cattle producers in managing their feed resources more efficiently.

Potatoes are a capital-intensive crop. A farmer who is considering expanding his potato acreage must carefully consider revenue requirements to offset the high costs of raising the crop. A method to forecast annual farm potato prices would be useful not only to the farmer, who is considering potato acreage expansion (or contraction), but also to the potato buyers. Seven forecasting models were considered: (1) a simultaneous equation model (with five equations); (2) a Box-Jenkins type ARIMA model; (3) an exponential smoothing model; (4) a moving-ave rage model; (5) a trend model; (6) an "opposite" model; and (7) a current. or naive, model. The results reveal the following three things: (I) The "best" model was the trend model. This model gave the most accurate one-period out-of-sample forecasts of the models tested (as measured by the mean absolute error (MAE), the root mean squared error (RMSE), and Theil's U2 statistics). The simultaneous equation model could be considered as the next best model. (2) The forecast for the average Utah farm potato price for 1992 was about $5.40 per cwt. (3) The average Utah farm potato price for 1993 should be in the $5.51 to $5.95 range (the forecasts from the trend and simultaneous equation models, respectively).

Indiana University-Purdue University Indianapolis (IUPUI) / Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.

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The number of African American males who are incarcerated is extremely high and alarming. African American males have the lowest attainment of associate degrees when compared to other ethnicities and to females. This places a monumental task on institutions of higher education, particularly community colleges, to enroll, retain, and graduate African American males. The purpose of this study was to examine the performance of an African American male initiative program at a small, rural community college in Mississippi. This study utilized a quantitative, cross-sectional research design to look at variables of Grade Point Average (GPA) and number of credits earned between African American male participants of the program and those who did not participate in the program over a period of six semesters. Results from this study showed a significant difference in number of credits earned between the students who participated in the program and those who did not, with non-participants earning an average of one to two credits more than participants. The study also showed that students who did not participate in the program had a slightly higher GPA than the students who did participate in the program for three out of the six semesters. This study enhances the ability for community college and university administrators to implement policies and best practices to engage African American male students.

Indiana University-Purdue University Indianapolis (IUPUI) / This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme. Also, a mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model. Finally, differnt simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.

The purpose of this study was to examine the impact of the "C" average rule on student achievement at Stagg High School, Stockton, California. This study particularly addressed the issue of whether there were significant differences between athletes' grade point averages (GPA) before implementation of the policy and after. In addition, the study included assessing differences in attendance patterns of the athletes. The 1981-82 school year was identified as the year prior to implementation of the "C" average-rule. The 1982-83 school year was considered the implementation year and, finally, the 1983-84 school year was identified as the year after the implementation of the "C" average rule.For an athlete to be included in this study, a grade point average must have been available for at least one of the three athletic seasons during the year preceding the implementation of the "C" average policy. In addition, each athlete must have participated in athletics subsequent to the 1981-82 year. Thus, each athlete was required to have GPA and attendance data for two particular points in time over the three years included in the study. The total number of male and female athletes for whom all analyses were done was five hundred sixty-two. These athletes represented the four major ethnic groups, Black, Asian, White and Hispanic. All data were organized to correspond with the fall, winter, and spring athletic seasons. Grade point averages were recorded from report cards and transcripts. Attendance data were recorded from individual attendance sheets maintained at the school site. Ethnicity and gender were recorded based upon school emergency cards. Ten questions provided the focus of the study. Each of the ten questions to be answered asked for a comparison between GPA or attendance prior to implementation of the "C" average rule and subsequent to it. Means for the particular paired groups were obtained, and the t test for related measures was calculated. The .1 0 level was used to determine significance. While some significant differences were noted, usually favoring pre policy data, generally speaking, it appeared that the policy had no direct impact upon either grade point averages or attendance rates. The study was not done in a way to establish a cause and effect relationship, but from a practical perspective, it does not appear that either GPA or attendance was seriously affected. Recommendations for future studies are made including replication of this study now that California has implemented the "C" average rule statewide, as well as in a variety of other high school settings.