STATISTICS

This study focused on factorial experiments and its applications in industries. In this study, we examined the application ofa two-factor factorial design to determine the significant difference in the mean yield ofonion with respect to the effect offertilizers and plant densities. Primary data (yield ofonion) was collected from the Department ofCrop science, Faculty ofAgriculture, University of Benin. This research work covers only two factors which are fertilizers at three levels (PM,OM and NPK) and plant densities at two levels (LOW and HIGH). The analysis techniques employed was a 2*3 replicated factorial design with 6 replicates per cell. Data collected was analyzed using SPSS version 22. The hypothesis tests were carried out at a (5%) significance level and the decision rule was to reject the null hypothesis ifthe calculated significance value (p-value) is less than the α (5%).Results from the analyses revealed among others that there is significant difference in the fertilizer effect and plant densities effect on the yield ofonion with a significance level of0.0001 and 0.016 respectively. In addition, there is no significant interaction effect between fertilizers and plant densities with significance value of 0.840 on the yield ofonion.

In this project work, we look at how the least-square polynomial regression model is used to fit a non-linear relationship between a response variable and an explanatory variable in curve fitting. Finding a mathematical equation or model that best fits a noisy data has experience some drawback in curve fitting. i.e. finding an appropriate fit that best depicts the behaviour of the data. The purpose of this project is to show how the polynomial regression model can be used to show the relationship that exist between two variables; where the linear regression model is inadequate in describing such a relationship. The method of curve fitting used in this study is the least square polynomial regression method. It is designed in a way that, the model parameters are estimated by minimizing the residual term of the polynomial regression model; and then used the model to find the line that best fits the data points of the data set. This method was validated by modelling a data extracted from Nigeria Stock Exchange; and the model was able to predict over 80% of the relationship that existed in the data. It was discovered that the inadequacies of the simple linear regression model in describing the relationship that existed in a data set could be easily tackled by fitting a polynomial regression line. This is done by increasing the power of the independent variable to a higher power until we get a best fit.

This research work examined the rate of unemployment in Nigeria. The data used was a secondary data obtained from World Bank on twenty -three observations. The research studied the relationship between unemployment rate and population growth in Nigeria using the chi square analysis, it also examined the impact of inflation, population growth and unemployment rate on GDP using the kruskal Wallis analysis. The Analysis shows that unemployment does not depend on population growth and also population growth and unemployment rate show a significant effect on GDP while inflation does not exhibit statistically significant relationship in the analysis, and unemployment rate show an increasing trend. Based on the outcome of the research it was recommended that government should support and provide incentives for entrepreneurship, encouraging others to start businesses and create job opportunities for others.