http://repository.unn.edu.ng/handle/123456789/392 Wed, 02 Sep 2026 17:13:40 GMT 2026-09-02T17:13:40Z http://repository.unn.edu.ng/handle/123456789/9645 Assessing the Sensitivity and Robustness of Randomization Test and F-Test in Analysis of Repeated Measures Design with Missing Observations Obasi, John Ajali ABSTRACT In this work, the sensitivity and robustness of randomization test and F-test were assessed in a single factor repeated measures design with missing observations under cases when the data were normal, non normal, contained outliers and when sphericity condition was met or not met under varied sample size and number of treatments. The assessment was done by comparing the statistical power and p-value of the randomization test with that of F-test. The Monte Carlo approach was used in the simulation study on sensory adaptation experiment. The results showed that in the complete case analysis with normal data, the randomization test was approximately as sensitive and robust as the F-test, while it was more sensitive than the F-test when data had skewed distributions (Chi-square, exponential, lognormal and Weibull distributions) used in this work. The randomization test was more sensitive and robust than the F-test in the presence of an outlier, with and without missing observations. When observations were missing, the randomization test was mildly more sensitive than the F-test for normal data, while it was substantially more sensitive than the F-test when the data distribution were skewed as the percentage of missingness increased. When sphericity condition was met, the randomization test and the F-test were approximately equally sensitive; whereas the randomization test was more sensitive and robust than the F-test when sphericity condition was not met. The randomization test was therefore recommended to replace the F-test in analyzing single factor repeated measures design. Fri, 01 Nov 2019 00:00:00 GMT http://repository.unn.edu.ng/handle/123456789/9645 2019-11-01T00:00:00Z http://repository.unn.edu.ng/handle/123456789/9644 Time series analysis of annual rain fall in nsukka (A comparative study) Adaji, Reuben Isaiah This work provides a review and general consideration of models for rain fall data. From the time series plots and test for seasonal effects on the original data, it is evident that the series have seasonal effect and that prompted the need for seasonal differences. With an additive model, the series were decomposed, and the trend and seasonal indices with respect to months were estimated. This informed the use of SARIMA model to mimic the data generating process. It was also discovered that SARIMA (0, 0, 2)*(1, 1, 1)12fits appropriately to the series. In order to capture the behavior of the series in frequency domain, Fourier series model was fitted to the data. The empirical relationships of the Fourier residual modification (FSARIMA model) on the fitted SARIMA model was incorporated, Using some model performance measures (SSE, RMSE, MAE and MBE) on the above models it was discovered that the Fourier series model out perform other models as it yielded minimum error values in all the measures of goodness of fits. Wed, 01 Nov 2017 00:00:00 GMT http://repository.unn.edu.ng/handle/123456789/9644 2017-11-01T00:00:00Z http://repository.unn.edu.ng/handle/123456789/9642 A Comparative Study of Techniques for Identifying Out-Of-Control Variable(S) In Multivariate Control Chart on Cable Production Ezeani, Onyinye Maryjane Multivariate statistical process control charts are used for process monitoring and control of two or more variables simultaneously for quality and quality improvement. A popular multivariate control chart is used to monitor the mean vector of the process. A usual problem in the use of the multivariate control chart is the identification and interpretation of variable(s) responsible for out-of-control signal that occurred in the chart. This has brought many developed techniques from many researchers to aid in finding the variable(s) responsible for the out-of-control signal in the multivariate control chart. Thus, the work is aimed at a comparative study of some developed techniques for identifying and interpreting out-of-control signal in the multivariate control chart when applied to the cable production process. The techniques are Mason-Tracy-Young, Donganaksoy-Faltin-Tucker, Univariate -chart using Bonferroni control limits by Alt and Principal component analysis by Jackson. A performance criterion, the power of a test was used to ascertain the most satisfactory technique that explained the out-of-control signal that occurred in the multivariate control chart. From the results and discussions, Mason Tracy-Young and Doganaksoy-Faltin-Tucker techniques are the most satisfactory for identifying and interpreting out-of-control signal in the multivariate control chart for the datasets considered. Mon, 01 Jul 2019 00:00:00 GMT http://repository.unn.edu.ng/handle/123456789/9642 2019-07-01T00:00:00Z http://repository.unn.edu.ng/handle/123456789/4455 A Non-Liner Regime Switching Models in Financial Series With Two Regimes Umah, Obianuju Glory In this study, two economic series which have changes in regimes were considered. Models considered for the two series are Simple Switching Mixture (SSM) model and Markov Switching Autoregressive (MS-AR) model. Predictions of future transition regime probabilities were performed using the Hamilton filter of m-period transition matrix for MS-AR model, while, the two state ergodic m-step ahead transitions probabilities for SSM model. Subsequently, forecast evaluation measures for the two models were carried out with Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Consumer Price Index (CPI), had a better forecast with SSM model while, Nominal Effective Exchange Rate (NEER) had a better forecast with the MS-AR model. Thu, 20 Apr 2017 00:00:00 GMT http://repository.unn.edu.ng/handle/123456789/4455 2017-04-20T00:00:00Z