Browsing by Subject "Regression"
Now showing items 1-13 of 13
-
Econometric analysis of the impact of market concentration on prices in the offshore drilling rig market
(2010-12)This thesis presents an econometric methodology for analyzing the impact of market concentration (HHI) on the day rate prices paid by E&P operators for the lease of drilling rigs. It is an extension of the work of Lee ... -
Eksen : regression test selection for VHDL
(2018-01-26)Regression testing - running tests after a change - has become a critical component of software development, but as projects grow bigger it becomes a time consuming task. For this reason Regression Test Selection (RTS) ... -
Influences of protocol changes in ERCOT on ancillary services and wind integration
Wind energy has been playing an increasingly significant role in today’s world, and requirements for more wind integration challenges the existing electricity structure and market. ERCOT, for example, has installed ... -
Large-scale network analytics
(2011-08)Scalable and accurate analysis of networks is essential to a wide variety of existing and emerging network systems. Specifically, network measurement and analysis helps to understand networks, improve existing services, ... -
Low pH waters in the vicinity of Oak Hill Mine : a statistical evaluation of water quality
(2014-08)Lignite (brown coal) mine-mouth power plants supply a significance portion of electricity generated annually in Texas. Most lignite is produced from the Wilcox Group at surface mines located near a power plant. At the ... -
Methods for analyzing proportions
(2013-08)The analysis of proportions is interesting and noteworthy in that there are no commonly accepted regression models for analyzing proportions; indeed, researchers most often use ordinary least squares to estimate the ... -
Municipal economic growth through green projects and policies
(2012-05)Cities generally need economic growth. Green policies and projects are environmentally beneficial, desirable, expected by the public, and pragmatic in the long term. However, there is insufficient research on what, if any, ... -
Overview of machine learning methods in predicting house prices and its application in R
(2018-01-25)This report aims to predict house prices by using several machine learning methods. These methods include ordinary least squares regression, Ridge regression, Lasso regression, and k-nearest neighbor regression. We compare ... -
An overview of multilevel regression
(2010-12)Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, ... -
Quantification of stock option risks and returns
(2010-05)Under mild assumptions, the expected returns of call options increase as the strike price becomes higher. Two ways to define option moneyness are the ratio of strike price to stock price (K/S ratio) and log(K/S)/σ. This ... -
Regression : when a nonparametric approach is most fitting
(2012-05)This paper aims to demonstrate the benefits of adopting a nonparametric regression approach when the standard regression model is not appropriate; it also provides an overview of circumstances where a nonparametric approach ... -
Regression model ridership forecasts for Houston light rail
(2012-12)The 4-step process has been the standard procedure for transit forecasting for over 50 years. In recent decades, researchers have developed ridership forecasting regression models as alternatives to the costly and time ... -
Simultaneous partitioning and modeling : a framework for learning from complex data
(2010-05)While a single learned model is adequate for simple prediction problems, it may not be sufficient to represent heterogeneous populations that difficult classification or regression problems often involve. In such ...
