This paper presents an outlier detection technique for univariate normal datasets. Outliers are observations that lips an abnormal distance from the mean. Outlier detection is a useful technique in such areas as fraud detection, financial analysis, health monitoring and Statistical modelling. Many recent approaches detect outliers according to reasonable, predefined concepts of an outlier. Methods of outlier detection such as Gaussian method of outlier detection have been widely used in the detection of outliers for univariate data-sets, however, such methods use measure of central tendency and dispersion that are affected by outliers hence making the method to be less robust towards detection of outliers. The study aimed at providing an alternative method that can be used in outlier detection for univariate normal data sets by deploying the measures of variation and central tendency that are least affected by the outliers (median and the geometric measure of variation). The study formulated an outlier detection formula using median and geometric measure of variation and then applied the formulation on randomly simulated normal dataset with outliers and recorded the number of outliers detected by the method in comparison to the other two existing best methods of outlier detection. The study then compared the sensitivity of the three methods in outlier detection. The simulation was done in two different ways, the first considered the variation in mean with a constant standard deviation while the second test held the mean constant while varying the standard deviation. The formulated outlier detection technique performed the best, eliminating the most required number of outliers compared to other two Gaussian outlier detection techniques when there was variation in mean. The study also established that the formulated method of outlier detection was stricter when the standard deviation was varied but still stands out to be the best as an outlier is defined relative to the mean and not the standard deviation. The study established that the formulated method is more sensitive than the Gaussian Method of outlier detection but performed as well as the best existing outlier detection technique. In conclusion, the study established that the formulated method could be employed in outlier detections for univariate normal data-sets as it performed almost the same to the best existing method of outlier detection for univariate data-sets.

After the establishment of the Narok County government and the transition from the central system of government into the Devolved system of governance, majority of the residents of Narok County had much anticipation in terms of developments that will take place as a result of governance being brought close to them. The study was checking economic development changes that has taken place in Narok Town the Headquarter of Narok County since the establishment of the Narok County Government. The objective was to access how the introduction of county government has impacted on the economic development by investigating its impact on the various key indicators of the economic development such as health, trade, and infrastructure. The study used a sample of 320 residents drawn at random from all parts of the town, the samples was surveyed using a written survey instrument and their opinion on the state of various economic indicators was captured and used to develop a structural equation model using SmartPLS 3 software, in order to use in examining the economic development status of Narok Town. The study fits a significant model that can tell the whereabouts of the economic status of the Town presently and in future. It was concluded that the county government has not done much in terms of economic development since the introduction of County government because the rural areas in the county are still struggling to catch up with the indicators of economic development. It was also evidenced that the impact of County government on trade is good compared to its impact on health and infrastructure.

From the past studies, we realized that minimum distance estimation technique is not commonly used for fitting wind speed data to a distribution yet it is believed to the best alternative for Maximum Likelihood Estimation (MLE) method which is known to give good estimates than Least Square Estimates (LSE) and Method of Moments (MOM). To achieve this, the study aims at fitting data to a probability distribution using minimum distance estimation techniques to find the best distribution. The study uses wind speed data from five sites in Narok county namely; Irbaan primary, Imortott primary, Mara conservancy, Oldrkesi and Maasai Mara University. The best wind speed models were examined using the Cullen and Frey graph and a suitability test on the models done using Kolmogorov-Smirnov statistical test of goodness of fit. The wind speed data are fitted to the recommended distributions using minimum distance estimation techniques. The best distribution was identified using Akaike's Information Criterion (AIC) and Bayesian Information criterion (BIC). From the distribution comparison for the two and three parameter distributions, gamma is the best in all cases. Gamma with three parameter distribution gives lower AIC and BIC values and model comparison test showing that gamma 3-parameter is the better than gamma with 2-parameters. The study concluded that gamma distribution with three parameters is the best distribution for fitting wind speed data with the three parameters given as; threshold parameter of 0.1174, shape parameter of 1.8646 and scale parameter of 0.9937.