https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=6475&context=rtd

Fundamental statistics for business and economics

U.S. electrical energy dilemma and an energy model for the electrical utilities of Iowa

As reflected by the literature, the environment of each country influences the characteristics of the budgetary process in that country. Often-cited factors which shape this environment are: economic wealth, financial predictability, political institutions, elite values, and size. Economic wealth and financial predictability are considered the most powerful. Economic wealth and financial predictability permit four classi­ fications of budgetary environments, namely: rich and certain, poor and certain, poor and uncertain, and rich and uncertain environments. Each of the first three classes of budgetary environments has been investi­ gated in the existing literature. However, the rich and uncertain environment remains more elusive to the investigator. This author believes that a government exists with sufficient wealth and environmental uncertainty to be classified in the fourth category. Namely, such government exists in Saudi Arabia. Therefore, the purpose of this study was to examine empirically the characteristics of the governmental budgetary process in which the environment is uncer­ tain but where the government is rich. A combination of different research methods were used to gather the data for this study. This combination included library research, interviews, and empirical testing of the quantitative budgetary data. The findings, based on both research tools, are summarized as

/pdf/an-empirical-study-of-the-characteristics-of-the-38kv3hpd31.pdf

An Empirical Study of the Characteristics of the Governmental Budgetary Process in Rich and Uncertain Environments: the Case of Saudi Arabia.

Anthropogenic utilization of forest and woodland ecosystems can cause declines in flora and fauna species. It is imperative to restore these ecosystems to mitigate further declines. In this thesis, I focused on a highly degraded region, the Box-Ironbark forests and woodlands of Victoria, Australia. Rather than mature stands with large trees, stands are currently dominated by high densities of small stems. This change has resulted in reduced populations of many flora and fauna species dependent on older-growth forests and woodlands. Managers are interested in restoring mature Box-Ironbark forests and woodlands through three alternative management strategies: allocating land to National Parks and allowing stands to develop naturally without harvesting, modifying timber harvesting regimes to retain more medium and large trees, or a new ecological thinning technique that retains target habitat trees and removes competing trees to encourage growth of retained stems.
 The effects of each management strategy are not easy to predict due to complex interactions between intervention and stochastic natural processes. Forest simulation models are often employed to overcome this problem. I constructed state-and-transition simulation models (STSMs) to predict the effects of alternative management actions and natural disturbances on vegetation structure. Due to a lack of empirical data, I relied on the knowledge of experts in Box-Ironbark ecology and management to construct STSMs. Models predicted that the development of mature woodlands under all strategies was minimal over the next 150 years, and neither current harvesting nor ecological thinning is likely to expedite the development of mature stands relative to growth and natural disturbances. However, differences in experts’ opinions led to widely diverging model predictions.
 Uncertainty must be acknowledged in model construction because it can affect model predictions. I quantified uncertainty due to four sources – between-expert variation, imperfect expert knowledge, natural stochasticity, and model parameterization – to determine which source caused the most variance in model predictions. I found that models were very uncertain and between-expert uncertainty contributed the majority of variance in model predictions. This brings into question the use of consensus methods in forest management where differences between experts are ignored.
 Using uncertain model predictions to make management decisions is problematic because any given action can have many plausible outcomes. I applied several decision criteria to uncertain STSM predictions using a formal decision-making framework to determine the optimal management action in Box-Ironbark forests and woodlands. I found that natural development is the most risk-averse option, while ecological thinning is the most risky option because there is a small likelihood that it will greatly expedite the development of mature woodlands. Rather than selecting one option, managers could rely on a risk-spreading approach where the majority of land is allocated to no-cutting National Parks and a small amount of land is allocated to the other two harvesting strategies. This would allow managers to collect monitoring data for all management strategies in order to learn about effects of harvesting and update model predictions through time using adaptive management.

background

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/35232/118647__whole thesis-garamond (REVISED).pdf

Incorporating uncertainty into expert models for management of box-ironbark forests and woodlands in Victoria, Australia

The study set to investigate the use of marketable securities by firms listed at Nairobi Stock Exchange. This was done with the objective of finding out the extent of use of marketable securities by managers. The study identifies the motives and factors that managers consider while choosing marketable securities. The assumption in this study is that managers maximise return while minimising risk. Data was collected by structured questionnaires and through examination of annual report. The findings were that, firstly, firms hold marketable securities for precautionary purpose with most firms holding commercial paper. The decision to invest depends on certainty of the cash flow pattern of the investing firm and also accessibility to capital when need arises. Secondly, firms that invest in marketable securities regard the investments yield as very important. The yield is a critical variable because it compares the return from an investment to the existing market price. It is also relied on in estimating the risk of an investment. Thirdly, majority of the firms that invest in marketable securities have specialised department who undertake the investment activities. Some firms consider investing in marketable securities as part time job and a few outsource this function. The conclusion is that firms invest in marketable securities for precautionary purpose and to earn yield till need for it arises. However, amount to hold for precautionary purpose will depend on individual firms attitude towards risk and the quality of marketable securities available.

A survey on use of marketable securities by firms quoted at Nairobi Stock Exchange

This study was designed to test the relationship of the backgrounds of 429 insurance agents in one firm to success in their jobs. From their company f ile s , information was gathered on 40 variables related to the health, family situation, employment background, finances, education, and social activ ities of these agents. The aim was to determine whether any relationship existed among these variables that would allow a discrimination between successful and unsuccessful agents. The sample used in this research was comprised of 429 male, Cau­ casian, ordinary agents hired by a large, nationally ranked insurance company between January 1, 1967, and June 30, 1972. They were a ll hired on a fulltim e basis and had a ll been previously inexperienced at selling l i f e insurance. These agents were classified as successes or failures on the basis of productivity. A sales volume of $330,000 for their seventh-through-eighteenth months with the company was used as a cut-o ff point. Of the total sample, 51 had succeeded and 378 had fa iled . A multivariate analysis of the data gathered on these two groups of agents was conducted through the use of the discriminant procedure of the Statistical Analysis System (SAS). The "simple statistics" generated by this program revealed appreciable differences between successes and failures when individual variables were considered. At the time of application to the company studied, successful aqents had lived at their present residences twice as long as had the failures. Also, successful agents owned over twice the assets and well over twice the l i fe insurance owned by unsuccessful agents at application. Finally, agents who succeeded with this company reported being in the labor force longer, had earned more at their last jobs, and had averaged longer stays at previous jobs than did those who had not succeeded. The SAS discriminant procedure develops a discriminant function on the basis of the generalized squared distance between successes and failures. Prior probabilities of success and failure and costs of misclassifying successes and failures are normally assumed equal. Because these prior probabilities and costs of misclassification were clearly unequal in this study, a Bayesian-1ike model was used to in­ troduce unequal probabilities and costs to the program. Estimates of rea lis tic prior probabilities and of the cost of misclassifying (hiring) a fa ilure were gleaned from the industry. However, since an approximation of the cost of misclassifying (rejecting) a success could not be found, 11 different estimates were made of this figure, ranging from $3,500 to $350,000. The dis­ criminant procedure was thus applied to the data 11 times on the basis of 11 different assumptions about the cost of misclassifying successes. The cost of misclassifying failures was assumed to be $3,500 for each application of the procedure. The results are summarized as follows. At an assumed $3,500 cost of misclassifying successes, the discriminant function correctly classified 89 percent of the entire sample, 97 percent of the failures, and 26 percent of the successes. When misclassifying successes was

/pdf/a-multivariate-analysis-of-personal-background-factors-3i0h5a2fmh.pdf

Fundamental statistics for business and economics

Citations

U.S. electrical energy dilemma and an energy model for the electrical utilities of Iowa

An Empirical Study of the Characteristics of the Governmental Budgetary Process in Rich and Uncertain Environments: the Case of Saudi Arabia.

Incorporating uncertainty into expert models for management of box-ironbark forests and woodlands in Victoria, Australia

A survey on use of marketable securities by firms quoted at Nairobi Stock Exchange

A Multivariate Analysis of Personal Background Factors Related to the Success or Failure of Life Insurance Agents.

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