by Kimtai Leonard Masai, Moses M. Muraya and Adolphus Wagala
Original Research
The objective of design and analysis of experiments is to optimize a response variable which is influenced by several independent variables. In agriculture, many statistical studies have focused on investigating the effect of application of organic manure on the yield and yield components of crops. However, many of these studies do not try to optimize the application of the manures for maximum productivity, but select the best treatment among the treatment range used. This is mainly due to design and analysis of experiments applied. Therefore, there is a need to apply a statistical method that would establish the effect of the application of organic manures on crop production and in addition optimize the levels of application of these manures for maximum productivity. This study aimed at application of response surface methodology for optimization of the yields of common bean (Phaseolus vulgaris L.) using animal manure. The study was conducted at Chuka University Horticultural Demonstration Farm. The experiment was laid down in a Randomized Complete Block Design. The treatments consisted of three organic manure sources (cattle manure, poultry manure and goat manure) each at three levels (0, 3 and 6 tonnes per ha). Data was collected from six weeks after sowing to physiological maturity. Data was collected on the weight of the grain yield harvested in each experimental plot measured by use of a weighing scale. The data collected was analysis using the R-statistical software. The study findings indicated that animal manures had a significant effect (p < 0.05) on the yield of common beans. The results also showed that the optimum levels of application of the manures in the area of study were 2.1608 t ha-1, 12.7213 t ha-1 and 4.1417 t ha-1 cattle manure, poultry manure and goat manure, respectively. These were the optimum levels that would lead to maximum yield of common beans without an extra cost of input.
American Journal of Applied Mathematics and Statistics. 2020, 8(2), 64-68. DOI: 10.12691/ajams-8-2-5
Pub. Date: August 13, 2020
3249 Views543 Downloads
by O. C. Asogwa, N. M. Eze, C. M. Eze, C. I. Okonkwo and C. U. Onwuamaeze
Original Research
A multilayer perception algorithm based model was established in this research. The result from the test data evaluation showed that the established Artificial Neural Network model was able to correctly predict and classify the effects of COVID-19 outbreak on the well-being of Nigerian citizens with Mean Correct Classification Rate () of 98.05%. The value of the of the model which was classified as good (88.23%), also aligned with the result obtained from the Mean Correct classification rate. The architecture model also indicated a very high sensitivity and a low specificity values respectively. The study was able to show that some factors like economy, farming activities, religious activities, education, etc. were negatively affected whereas crime rates, unwanted pregnancy, relationship between parents and their children were positively influenced during the lockdown period of coronavirus pandemic outbreak in Nigeria.
American Journal of Applied Mathematics and Statistics. 2020, 8(2), 58-63. DOI: 10.12691/ajams-8-2-4
Pub. Date: July 16, 2020
3003 Views589 Downloads
by Michael Gr. Voskoglou
Original Research
Reasoning is the most important human brain operation that leads to creative methodologies, algorithms and deductions giving way to sustainable research and development. Since the basis of reasoning in the Artificial Intelligence’s expert systems is human notions and concepts, the success of those systems depends upon the correspondence between human reasoning and their formalization. The paper at hands studies human reasoning in general and scientific thinking in particular with the help of the theory of finite Markov chains. Examples are also presented illustrating the applicability of the developed Markov chain models to real life situations.
American Journal of Applied Mathematics and Statistics. 2020, 8(2), 52-57. DOI: 10.12691/ajams-8-2-3
Pub. Date: July 09, 2020
4038 Views617 Downloads
by Ramesh C Kataria and Darshana A Patel
Original Research
A comparative mathematical analysis of slider bearing made up of double porous layered slider supported by solid wall with different designed stator such as convex, parallel, exponential, inclined, and secant is presented in this research paper. The study includes the effect of slip velocity as suggested by Sparrow et al. [13] at the interface of film-porous. Effects of squeeze velocity and the oblique variable magnetic field to the lower plate are considered. General form of Reynolds type equation, non-dimensional squeeze film pressure and load carrying capacity expressions are obtained. The values of non-dimensional load carrying capacity are obtained and compared among various considered designs bearings. Overall, it could be concluded that compared to others, secant pad stator slider bearing is suggested for the superior performance of the system.
American Journal of Applied Mathematics and Statistics. 2020, 8(2), 43-51. DOI: 10.12691/ajams-8-2-2
Pub. Date: June 18, 2020
4016 Views658 Downloads14 Likes
by Noora Shrestha
Original Research
Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of the significant variables under study to be statistically insignificant. This paper discusses on the three primary techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other predictors. Furthermore, advanced regression procedures such as principal components regression, weighted regression, and ridge regression method can be used to determine the presence of multicollinearity.
American Journal of Applied Mathematics and Statistics. 2020, 8(2), 39-42. DOI: 10.12691/ajams-8-2-1
Pub. Date: June 14, 2020
10177 Views619 Downloads4 Likes