This study examined the probability distribution that best described the quarterly economic growth rate of Nigeria between 1960- 2015. The study collected secondary data from Central Bank of Nigeria (CBN) Statistical Bulletin 2015 on Gross Domestic Product to compute the economic growth rate of Nigeria. Six theoretical statistical distributions were fitted via Normal Distribution, Logistic Distribution, Laplace Distribution, Cauchy Distribution, Gumbel (Largest Extreme Value) Distribution and Generalized Logistic Distribution. The Laplace Distribution fitted the data as confirmed by Kolmogorov Simonov goodness of fit test, Akaike Information Criteria and Bayes Information Criteria. The probabilities of economic growth rate behaviours were obtained from the best fit distribution. The analysis showed that the chance of obtaining a negative quarterly economic growth rate is 28%. The chance of an economic recession is 8%. Also, the probability of having a positive single digit quarterly economic growth rate is 46%. In addition, having a double digit positive quarterly economic growth rate is 26%.  

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A unified method for solving that incorporate a computational formula that relate the coefficients of the depressed equation and the coefficients of the standard polynomial equation is proposed in this study. This is to ensure that this method is valid for all   It shall apply the undetermined parameter method of auxiliary function to obtain solutions to these polynomial equations of degree less than five in one variable.  In particular, the result of our work is a unification and improvement on the work of several authors in the sense that only applicable for the case of polynomial equation of degree one. Finally, our results improve and generalize the result by applying standard formula methods for solving higher degree polynomials. It is recommended that the effort should be made toward providing other variant methods that are simpler and friendly.

In this study, we considered various transformation problems for a left-truncated normal distribution recently announced by several researchers and then possibly seek to establish a unified approach to such transformation problems for certain type of random variable and their associated probability density functions in the generalized setting. The results presented in this research, actually unify, improve and as well trivialized the results recently announced by these researchers in the literature, particularly for a random variable that follows a left-truncated normal distribution. Furthermore, we employed the concept of approximation theory to establish the existence of the optimal value y_max in the interval denoted by (σ_a,σ_b) ((σ_p,σ_q)) corresponding to the so-called interval of normality estimated by these authors in the literature using the Monte carol simulation method.