Wind energy is expanding rapidly in the United States as the nation\'s energy policy objectives increasingly focus on renewables. Public opinion polls show that a majority of Americans support wind energy development but actual wind farm projects often face intense local opposition. This dichotomy between general support for wind energy but opposition towards siting a project nearby is often attributed to the not-in-my-backyard (NIMBY) phenomenon. In this study we employ a discrete choice experiment to investigate public preferences for different characteristics of a local wind farm. We investigate NIMBY by first controlling for characteristics that might cause local opposition, such as seeing or hearing a wind farm from home, and then after considering these effects of a wind farm we examine whether people who favor wind energy display NIMBY resistance. Finally, we estimate compensation requirements for siting a wind farm within sight or sound of someone\'s home. Results show that people who somewhat favor wind energy do display NIMBY attitudes since they are predisposed to vote against local wind development even after controlling whether they would see and hear the wind farm from their homes. We do not detect NIMBY attitudes among people who strongly favor wind energy because they have a positive disposition towards local wind farms. Our results suggest that if an incentive program is in place from the onset of a wind development project it could offset NIMBY reactions to specific projects. / Master of Science

This thesis presents the rate distortion analysis of conditional motion estimation, a process in which motion computation is restricted to only active pixels in the video. We model active pixels as independent and identically distributed Gaussian process and inactive pixels as Gaussian-Markov process and derive the rate distortion function based on conditional motion estimation. Rate-Distortion curves for the conditional motion estimation scheme are also presented. In addition this thesis also presents the hardware implementation of a block based motion estimation algorithm. Block matching algorithms are difficult to implement on FPGA chip due to its complexity. We implement 2D-Logarithmic search algorithm to estimate the motion vectors for the image. The matching criterion used in the algorithm is Sum of Absolute Differences (SAD). VHDL code for the motion estimation algorithm is verified using ISim and is implemented using Xilinx ISE Design tool. Synthesis results for the algorithm are also presented.

Memory-trace theories of remembering suggest that performance in delayed matching-to-sample (DMTS) procedures depends on a memory trace that degrades with time. By contrast, the theory of direct remembering suggests that increasing the delay between sample and comparison stimuli in DMTS procedures is functionally equivalent to decreasing the disparity between sample stimuli. The present dissertation tested this assumption by assessing the degree to which changes in the frequency of reinforcement for correct choices biased the distribution of choice responses in a conditional-discrimination procedure. Seven pigeons responded under a temporal-discrimination procedure in which temporal sample-stimuli were categorized as being of either short or long duration by a response to a corresponding comparison key. In the sample-stimulus disparity condition, the disparity between the sample stimuli (difference between the short and long samples) was manipulated. In the retention-interval condition, the delay between sample offset and presentation of the comparison stimuli was manipulated. Importantly, the same general procedure was used across conditions, facilitating conclusions regarding functional equivalence of the two manipulations. The theory of direct remembering suggests that the relation between sensitivity of behavior to changes in reinforcer frequency and discriminability (accuracy) should be similar in the sample-stimulus disparity and retention-interval condition. The results showed that discriminability decreased with both the sample-stimulus disparity and retention-interval manipulations. Overall estimates of sensitivity were similar to those obtained previously. There was, however, no difference in the estimates of sensitivity as a function of discriminability during either the sample-stimulus disparity or retention-interval conditions; sensitivity was independent of discriminability. These results are in contrast to most previous reports, and are interpreted in terms of the use of temporal-sample stimuli in the current experiment. Further analyses of the choice-response data showed that the effects of variation in reinforcer ratios differed across conditions as a function of trial type and trial difficulty. These results suggest the need for careful consideration of behavioral outcomes at several levels of analysis when assessing functional equivalence of experimental manipulations. The potential benefits and hindrances of characterization of behavioral outcomes in terms of functional equivalence are discussed.

In surveys, one may face the problem of influential units at the estimation stage. A unit is said to be influential if its inclusion or exclusion from the sample has a drastic impact on the estimates. This is a common situation in business surveys as the distribution of economic variables tends to be highly skewed. We study and examine some commonly used estimators and predictors of a population total and propose a robust estimator and predictor based on an adaptive tuning constant. The proposed tuning constant is based on the concept of conditional bias of a unit, which is a measure of influence. We present the results of a simulation study that compares the performance of several estimators and predictors in terms of bias and efficiency.

<p> This thesis deals with metrizations of sets of conditional entropies and sets of sub-σ-algebras. G Co Rajski's Theorem ([9]) on the metric space of discrete probability distributions can be deduced as a particular case of a theorem on the metric space of sub-σ-algebras given in Chapter III, the proof of which is comparatively very concise. The completeness of this metric space and some other properties are also proved. </p> / Thesis / Master of Science (MSc)

Many students find that learning statistics is difficult, especially novice learners first taking statistical classes. Instructors also struggle to teach statistics due to many challenges, such as students' misconceptions, lack of math skills, negative attitudes, and deficient instructional methods. A new pedagogical approach called Decision-Based Learning (DBL) has been developed to help instructors and students overcome these challenges. A group of Peruvian university professors in 2018 and 2019 were trained to use DBL. However, limited studies have explored DBL instructors' and students' experiences using DBL in Peru. The aim of this qualitative research study is to deeply understand the experiences of one Peruvian statistics instructor and her students using DBL pedagogy and software. This study used a qualitative Gadamerian-Hermeneutic methodology approach. Data were collected from seven participants' interviews (six students and one statistics instructor), the researcher's field notes, and classroom artifacts. Findings identify several specific benefits and challenges participants faced when implementing DBL. Benefits include growth of students' confidence in solving statistical problems and instructor satisfaction, and challenges includes time-consuming instructor initial preparation and students' learning transfer issues. This study concluded that DBL is beneficial for students and instructors alike. Based on the results, some recommendations are offered to help instructors to overcome the challenges faced when implementing DBL.

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