Expenditure analysis and planning in a changed economy: a case study approach of Gweru City Council, Zimbabwe
- Authors: Kuhudzai, Anesu G
- Date: 2014
- Subjects: Economic development -- Zimbabwe , Poverty -- Zimbabwe , Regression analysis
- Language: English
- Type: Thesis , Masters , MSc (Mathematical Statistics)
- Identifier: vital:11786 , http://hdl.handle.net/10353/d1019780 , Economic development -- Zimbabwe , Poverty -- Zimbabwe , Regression analysis
- Description: The purpose of this study is to analyse Gweru City Council`s spending pattern and behaviour and to determine if this spending pattern is directed towards poverty reduction and economic development or not. Furthermore, to fit a log-differenced regression model to a historical financial dataset obtained from Gweru City Council Finance Department for the time period July 2009 to September 2012. Regression techniques were used to determine how Gweru City Council`s total income (dependent variable) is affected by its expenditure (independent variables). Econometric modeling techniques were employed for the evaluation of estimate tests, conducted to determine the reliability of the estimated model. The study concludes by providing some recommendations for possible financial plans which could be adopted by Gweru City Council and other local authorities in Zimbabwe for the well-being of Zimbabweans and economic development.
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Maximization of power in randomized clinical trials using the minimization treatment allocation technique
- Authors: Marange, Chioneso Show
- Date: 2010
- Subjects: Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11777 , http://hdl.handle.net/10353/399 , Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Description: Generally the primary goal of randomized clinical trials (RCT) is to make comparisons among two or more treatments hence clinical investigators require the most appropriate treatment allocation procedure to yield reliable results regardless of whether the ultimate data suggest a clinically important difference between the treatments being studied. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the statistical efficiency in detecting treatment effect and its complexity in implementation. Methods: A SAS simulation code was designed for allocating patients into two different treatment groups. Categorical prognostic factors were used together with multi-level response variables and demonstration of how simulation of data can help to determine the power of the minimization technique was carried out using ordinal logistic regression models. Results: Several scenarios were simulated in this study. Within the selected scenarios, increasing the sample size significantly increased the power of detecting the treatment effect. This was contrary to the case when the probability of allocation was decreased. Power did not change when the probability of allocation given that the treatment groups are balanced was increased. The probability of allocation { } k P was seen to be the only one with a significant effect on treatment balance. Conclusion: Maximum power can be achieved with a sample of size 300 although a small sample of size 200 can be adequate to attain at least 80% power. In order to have maximum power, the probability of allocation should be fixed at 0.75 and set to 0.5 if the treatment groups are equally balanced.
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