Statistics

A design of experiment is a plan to collect measurement or observation according to a pre arrange plan in such a way as to provide the basic for valid inference. This work was carried out to examine the research station effect on the yield of Cowpea varieties. The station are four locations in Nigeria (Kaduna, Shika, Mokwa and Kano). Eight different varieties of Cowpea were considered (Tg 1910-8F, Tg 1844 – 1E, Tg1019 – 2E, Tg1904 – 6F, Tg1910 – 2F, Tg1448 – 2E, Tg1908 – 1F, and Tg1740 – 2F). The data are secondary data, collected from International Institute of Tropical Agriculture (IITA) Ibadan, Oyo State. The result showed that research locations has no significant effect on the yields of cowpea varieties. The use of Randomized Complete Block Design (RCBD) design in Kaduna station, Shika station, Mokwa station and Kano station had 27.2%, 109.9%, 63.04% and 53.7% gain in experimental precision respectively.

One of the important models in tunnel design and underground structures are determining the stresses and stress concentrations around them. The method of analysis is usually based the theory of elasticity. Therefore; it has the advantages of accuracy and uniqueness respect to the numerical models. The stress conditions in subsurface or underground domains are usually in the form of both normal and shear stresses. Those are because of the geological features such as bedding, jointing, folding and nonuniformity of petrology. Therefore; the directions of principal stresses are not parallel to the original Cartesian coordinates and they make specific angles to the x and y-axis. The analysis is two-dimensional for circular tunnel and it is applied for plane stress and plane strain conditions. The analysis can be applied for the case of supporting pressure pi. The radial and tangential deformations could also be determined at the roof and walls of tunnel.

This study examined and compared six basic time series forecasting models (Exponential model, Double Exponential model, Holt-Winter models, Time Series linear regression model, the ad-hoc Bootstrapping model and the Self Adjusting model) with application to twenty-four Months Nigeria’s CPI inflation sample data, from January 2009 to December 2010 inflation data. With the aids of five different standard forecasting accuracy measures (MSE, MAE, RMSE, SSE, and MAPE), results from the out-of-sample forecasts shows that the double exponential model with a smoothening constant of 0.68 is the best forecasting model for the Nigeria inflation rate data among the other ad-hoc model considered.

Stock market is a mechanism through which the transaction of financial assets with life span of greater than one year takes place. Financial assets may take different forms ranging from the long-term government bonds to ordinary shares of various companies. This study was carried out to examine the role which the stock market plays in the growth process of the Nigerian economy. The Co-efficient of Determination (R2) was used to measure the goodness of fit of the model. The F-statistics was used to test the overall significance of a model. The Student t-test was used to determine the statistical significance of parameter estimates. Jacque-Bera Residual Normality Test was conducted to assert if the error term follows a normal distribution. The result of the t-test revealed that the coefficient for market capitalization, investment rate and real exchange rate are all statistical significant at 5 percent level of significance. But the coefficients of real interest rate were not statistically significant at 5 percent level of significance. The R2 which is the coefficient of multiple determinations also revealed that 99 percent of the variation in the dependent variable is caused by the variation in the explanatory variables. The F test result showed that the model is fit.

In this study an attempt has been made to describe the analysis of epidemiological study by Bayesian methods and apply this methodology to district level child mortality of Uttar Pradesh to assign rank to each district for rural and urban separately. The specific objectives of this study are to analysis of epidemiological study by use of fixed effect modeling and random effect modeling in Bayesian setup. To assign rank to each district by this suggest applying strategies to reduce child mortality in those district for those ranks are poor in Uttar Pradesh. The modified retrospective cohort study design used here. For fixed effect modeling beta-binomial modeling approach is used and for random effect modeling logit link function is used. The posterior estimates came in both cases under squared error loss function.

Auxiliary variable is extensively used in survey sampling to improve the precision of estimates. Whenever there is availability of auxiliary information, we want to utilize it in the method of estimation to obtain the most efficient estimator. In this paper using multi-auxiliary information we have proposed estimators based on arithmetic, geometric and harmonic mean. It was also shown that estimator based on harmonic and geometric means are more biased than estimator based on arithmetic mean under certain conditions. However, the MSE of all three estimators are same up to the first order of approximation.

In this study, an extension of the class of state-dependent model (SDM) for which optimal forecasts may be computed using a recursive examination procedure referred to as the Kalman filter is developed for the analysis of Inflationary rates in Nigeria. The SDM formulation yields a practical means of estimation for the complex time varying dynamical process and provided a generic flexible framework for inflationary rate modelling and inference. A straight forward implementation was achieved in the study by the use of R software package.

This study investigates the predicted effects of climatic factors during convenient intervals (in days) on cotton flower and boll production compared with daily observations. Also, covers the statistical relationship between climatic variables and aspects of cotton production and the effects of climatic factors prevailing prior to flowering or subsequent to boll setting on flower and boll production and retention in cotton. Further, cotton flower and boll production as affected by climatic factors and soil moisture status has been considered. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. The least important variables were found to be surface soil temperature at 0600 h and minimum temperature. The five-day interval was found to be more adequately and sensibly related to yield parameters. Evaporation; minimum humidity and sunshine duration were the most effective climatic factors during preceding and succeeding periods on boll production and retention. There was a negative correlation between flower and boll production and either evaporation or sunshine duration, while that correlation with minimum relative humidity was positive. The soil moisture status showed low and insignificant correlation with flower and boll production. Higher minimum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation.

Rapid development of wind energy as an alternative source of power is providing rich environment for wind energy related research. Several mathematical models have been used to study wind data and the models are mainly physical and statistical models. In this study, a signal Modeling approach is developed to predict wind speed data in Nigeria. The signal modeling approach is based on the Markov property, which implies that given the present wind speed state, the future of the system is independent of its past. A Markov process is in a sense the probabilistic analog of causality and can be specified by defining the conditional distribution of the random process.

This paper deals with the problem of estimating the population variance when some information on two auxiliary variables is available. It is shown that the proposed estimator is more efficient than the usual mean estimator and other existing estimators. The study is also extended to two-phase sampling. Theoretical results are supported by an empirical study.