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The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income computed from the Polynomial function with degree 5 for the Trapezium, Simpson 1/3 and Simpson 3/8 methods using the relative errors showed that the trapezium rule, Simpson’s 1/3 rule and Simpson’s 3/8 rule show negative biases with the Simpson 1/3 rule yielding the lowest absolute relative true error of 4.230711 %.

Today, several factors has contributed to the profitability of Banks. Data collected on these factors often has several variables. It is a non-trivial exercise to determine which of the factors that significantly influences the profits of Banks. This paper adopts the use of Principal Component Analysis (PCA) on the several variables expected to influence the working capital management on the profits of banks of the Ghana Stock Exchange (GSE). Fifteen of the several variables captured by the GSE which affect working capital management on profit were grouped into four factors using the principal component analysis. Results of the PCA identifies Convertibility factors, Risk Factor, Short term Liquidity, Operational factors, and Credit Risk factors to be the determinants of bank profits. Consequently, these factors are used to fit a linear regression model in identifying the most significant factors. Apart from credit risk factors, all the other factors were found to be significant predictors of the profit of Ghanaian banks. Investors, stakeholders, and managers of banks can use these factors to monitor and evaluate their working capital in generating profits.

Aims: This paper estimates working capital management on profit using logistic regression and discriminant analysis on manufacturing and industrial firms in Ghana. Study Design: Research Paper. Place and Duration of Study: Ghana, Secondary data for 2009 to 2014. Methodology: Data in the form of ratios were computed from the audited annual financial reports of 13 manufacturing and industrial firms listed on the Ghana Stock Exchange covering the period from 2009 to 2014.The ratios were used to determine the profitability of the firms. Results: The results showed that the logistic regression of the dependent variable (Profit) on the independent variables such as the Average Collection Period, the Inventory Conversion Period, the Average Payment Period, the Growth rate, the Debt Ratio, the Current Ratio and the Company Size were found to be significant and that there was no difference in variances for two firm classifications. This result implies that the linear discriminant function is effective in discriminating between a firm which effectively managed its working capital from one which did not. Conclusion: This study showed that the binary logistic regression model estimates correctly at least 75% of firm’s likelihood of managing working capital on profit while correctly discriminating the firms as having an effective management.

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