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This thesis deals with regression analysis of experimentally measured data of processed cheese. There is a polynomial regression used. The choice of regressors is based on Stepwise Regression and Mallows's Statistics. The estimation of the mean value is used to find the best mixture of the emulsifying salts with regards to the observed characteristic of the processed cheese. Analysis of the experiment and its results are well documented graphically.

Namaqualand is world renowned for its mass displays of annual wildflowers occurring in highly disturbed areas. Leysera gnaphalodes is a short-lived perennial shrub that encroaches into this wildflower display, lessening the aesthetic appeal. For this reason populations of L. gnaphalodes need to be kept as small as possible. This is usually achieved by tilling the area regularly, but a less disruptive method would be preferable. Alternatives to this approach are explored. The effect of many interacting factors needed to be examined over long periods of time so that alternative management strategies could be evaluated. Ecological modelling was used as it is ideally suited to this purpose. A review of modelling and its application in ecology is given, which includes a description of the modelling process and a discussion of different types of models and their applications. It was hypothesised that grazing and low rainfall, in addition to tilling, could control the population size of L. gnaphalodes. Data was used from an eight-year study conducted to determine the effects of tilling, grazing and environmental factors on the seedbank and population size of L. gnaphalodes. A rule-based mechanistic mathematical model based on the logistic growth curve was constructed to describe the population dynamics of this species. The model-fit was evaluated using Pearson’s correlation coefficients and graphs, and it proved to be a good model. Tilling and low rainfall were both found to be effective in reducing populations of L. gnaphalodes but grazing had no reducing effect. Simulations based on the model were run to test three different basic management strategies under stochastic rainfall conditions. The management strategy, which most effectively controlled the population was to till the lands whenever the population of L. gnaphalodes reaches of exceeds a relative frequency of 45%. Multivariate statistical models were constructed to determine the effects of all of these factors on the population of L. gnaphalodes. Tilling was confirmed to be effective in reducing the population, but grazing was found to have no effect. Low rainfall was also effective in controlling the population but has the disadvantages of being out of management control and also affecting the desirable wildflowers. / Dissertation (MSc (Botany))--University of Pretoria, 2005. / Plant Science / unrestricted

Doctor of Philosophy / Department of Human Nutrition / Edgar Chambers IV / Companies introduce new products with the goal of achieving success. However, many products fail. The overall objective of this research was to design processes for determining sensory and market characteristics of food products that could predict success. The first sub-objective was to determine if success could be predicted using information known before launch. The second sub-objective was to describe a process for determining specific sensory characteristics that promote success. Most methods chosen for this research are commonly used. However, previous research has identified a relationship between consumers liking and salivation, without defining a method. Thus, three salivation methods were selected for initial testing: spit, cotton rolls and sensory scale. These were tested on foods with different textures. Although all methods gave similar results, the spit method was chosen for further testing of flavor differences. Differences in salivation measurements were found for snacks where flavors were different but texture was unchanged. Next, flavored snack products from 15 countries were selected that were successful or had failed. Questionnaires were completed for each product and included questions related to authenticity, familiarity, current trends, packaging and marketplace issues such as product competition and pricing, all of which would be known before launch. A discriminant function was developed that correctly identified 75.8% of the successful flavored snack products as successful and 66.7% of the unsuccessful products as unsuccessful. Stepwise comparisons were used to determine that four variables are necessary to correctly categorize these products. The products then were clustered into three groups to select 34 products from 11 countries for further sensory testing. Information from extensive sensory descriptive methods were evaluated individually and in various combinations through stepwise regression and discriminant analysis. The final sensory model correctly predicted all successful and unsuccessful products, had an R-square of 0.84 and included nine regression factors: seven flavor attributes and two flavor attribute ratios. Many of the attributes were base flavor notes necessary for this flavored snack category. A process for selecting key attributes for success was described. For this snack category, creating products with flavors that interact well with base flavor notes can lead to a successful product.

State DOTs (department of transportation) spend billions of dollars on construction and maintenance of transportation projects every year. In addition, significant sums go to preliminary and construction engineering (PE and CE). For many projects, DOTs utilize engineering services from consultants, to supplement in-house engineering. The cost and quality of consultant’s engineering services compared to in-house, are important issues to justify the involvement of consultants. This report provides an analysis of those issues on Texas Department of Transportation (TXDOT) projects. Traditionally, the costs of PE and CE are calculated as a fixed percentage of total project construction cost, and the efficiency of engineering organizations is assessed by comparison of their gross percentages. However, the results presented here show that project scope and complexity are significant factors in PE and CE cost. Therefore, simplistic comparisons of PE and CE percentages can be misleading when applied across a mixed program of projects. / text

Tree rings have been used as a proxy in reconstructing streamflow in the western U.S. for many years, but few reconstructions have been attempted in the eastern United States. Clear limitations exist for streamflow reconstructions in the eastern U.S. compared to the western U.S., but value can be established as demonstrated in this research. The primary goal of this research was to reconstruct streamflow using data from five headwater gages in the Appalachian Mountains of North Carolina. These gages are located on the Valley River, the Oconaluftee River, the Nantahala River, the Little Tennessee River, and the Watauga River. Tree-ring chronologies were used to reconstruct streamflow. Tree-ring chronology predictors were selected using a seasonal correlation analysis. Seasonal correlation analysis revealed May-June-July (MJJ) streamflow variability being highly correlated with tree-ring chronologies in the study region and vicinity. Stepwise linear regression methods were used to reconstruct MJJ streamflow. The reconstructions for the Valley, Oconaluftee, and Nantahala Rivers were considered acceptable reconstructions because the models explained approximately 50% of the total variance in historic period MJJ streamflow records. These three streamflow reconstruction models have predictive skill indicated by a positive reduction of error (RE) values. The root mean square error (RMSE) statistic was 11.5 million cubic meters (MCM) for the Valley River (26% of the mean reconstructed MJJ flow), 15.9 MCM for the Oconaluftee River (16% of the mean reconstructed MJJ flow), and 8.2 MCM for the Nantahala River (20% of the mean reconstructed MJJ flow). Analysis of the reconstructed streamflow data for these three rivers revealed low flow periods from 1710 to 1712 at all three sites. The research presented here shows the potential benefit of using tree-ring chronologies to reconstruct streamflow in the Tennessee Valley region by demonstrating the ability of proxy-based reconstructions to provide useful data beyond the instrumental record. These useful data include identification of extreme wet or dry periods and oscillations in the historical reconstructions that are not visible in the instrumental data.

Under path dependent consideration, we discuss vulnerable option pricing problem. Two pricing models are proposed: Model(1) use stepwise regression and Monte Carlo simulation, and Model(2) is based on multi-level regression method. Since the option price was approximated by quadratic surface at each time point in Model(1), large mean square errors are induced. Therefore, we further propose a stepwise subset regression method to improve Model(1) approach. At present, this proposed method can compute the option price accurately for no credit risk options. For Model(2), we utilize a multi-level regression method to price vulnerable options, and simulation results show that the method can also obtain accurate option prices.

The topic of National Innovation System (NIS) is gradually emphasized. The NIS includes four compositions. They are government, industry, university and public research organization. The knowledge flow is transmitted among the four compositions through innovation policy. Thus, many countries have begun to develop NIS. The NIS will raise the economic growth rate, and promote the competitiveness of industry. Consequently, the study of NIS becomes very popular. OECD (Organization for Economic Cooperation and Development) build particular NIS structures to explain the difference between members, and try to find the key successful way to achieve national innovative goals. In the Asia, the Taiwan, China, Japan and South Korea show high relationship in the politics and economics. Japan and South are high-developing countries, and their innovation activities are very successful in the world, especially in those of technology industry. Additionally, China has abundant natural resources to help them develop technology industries. For above reasons, we elect these countries to be studied, and we try to find the essential factors of successful NIS. This study includes two research issues. We first collect the secondary data to explain different NIS structure among four countries. Then, we use Stepwise Regression Analysis to evaluate the performance of innovation. Finally we use the Pearson Correlation Analysis to analyze relationship between NIS performance and semiconductor industry development. The results of this study include: (1) R&D expenditure is the most important factor to influence the performance of national innovation; (2) Expenditure on basic research is an important factor to influence the output of innovation; (3) national innovation and industry development shows high relationship; and (4) the ranking of national innovation performance is not totally the same as that of industry development. Based on these findings, we will provide some important policy suggestions for innovation activities in Taiwan.

An experiment was conducted to obtain a hepatic gene expression dataset from postpubertal dairy heifers that could be fit to a computational model capable of predicting future lactation performance values. The initial animal experiment was conducted to characterize the hepatic transcriptional response to 24-hour total feed withdrawal in one-hundred and two postpubertal Holstein dairy heifers using an 8329-gene oligonucleotide microarray in a randomized block design. Plasma concentration of non-esterified fatty acids was significantly higher, while levels of beta-hydroxybutyrate, triacylglycerol, and glucose were significantly lower with the 24-hour total feed withdrawal. In total, 505 differentially expressed genes were identified and microarray results were confirmed by real-time PCR. Upregulation of key gluconeogenic genes occurred despite diminished dietary substrate and lower hepatic glucose synthesis. Downregulation of ketogenic genes was contrary to the non-ruminant response to feed withdrawal, but was consistent with a lower ruminal supply of short-chain fatty acids as precursors. Following the microarray experiment, the first series of regression analyses was employed to identify relationships between gene expression signal and lactation performance measurements taken over the first lactation of 81 of the subjects from the original study. Regression models were evaluated using mean square prediction error (MSPE) and concordance correlation coefficient (CCC) analysis. The strongest validated stepwise regression models were constructed for milk protein percentage (r = 0.04) and lactation persistency (r = 0.09). To determine if another type of regression analysis would better predict lactation performance, partial least squares (PLS) regression analysis was then applied. Selection of gene expression data was based on an assessment of the linear dependence of all genes in normalized datasets for 81 subjects against 18 dairy herd index (DHI) variables using Pearson correlation analysis. Results were distributed into two lists based on correlation coefficient. Each gene expression dataset was used to construct PLS models for the purpose of predicting lactation performance. The strongest predictive models were generated for protein percentage (r = 0.46), 305-d milk yield (r = 0.44), and 305-d protein yield (r = 0.47). These results demonstrate the suitability of using hepatic gene expression in young animals to quantitatively predict future lactation performance. / Ontario Centre for Agricultural Genomics, NSERC Canada, and the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA)

When we try to understand why some schools perform worse than others, if Covid-19 has struck harder on some demographics or whether income correlates with increased happiness, we may turn to regression to better understand how these variables are correlated. To capture the true relationship between variables we may use variable selection methods in order to ensure that the variables which have an actual effect have been included in the model. Choosing the right model for variable selection is vital. Without it there is a risk of including variables which have little to do with the dependent variable or excluding variables that are important. Failing to capture the true effects would paint a picture disconnected from reality and it would also give a false impression of what reality really looks like. To mitigate this risk a simulation study has been conducted to find out what variable selection algorithms to apply in order to make more accurate inference. The different algorithms being tested are stepwise regression, backward elimination and lasso regression. Lasso performed worst when applied to a small sample but performed best when applied to larger samples. Backward elimination and stepwise regression had very similar results.