The paper discusses about the effect of outliers and trends on the response surface design fitted to the experiments results. The common way to analyze the response surface is to fit the polynomial regression to the response variable by ordinary least square method and to find the significant controllable variables by ANOVA. In this case, the outliers can have confusing effect on the regression model, which derives the experiment results and lead to wrong interpretation of the data. The proposed moving average iterative method (MAIW) of this paper is a robust approach to decrease the effect of these faulty points by considering the previous data to detect the outliers or detect the probable trends in residuals. The iterative weighting method is used to estimate the coefficients of the regression model and a numerical example illustrates the proposed approach.

This paper investigates the relation between stock liquidity and firm performance. Liquidity plays an important role on performance of firms listed in Stock Exchange. When there is a good flow of trading stocks, people could expect more financing through absorbing investors on the market. This study examines the relationship between stock market liquidity and firm performance. The sample of the study was the continuously NSE listed top ten indices over the period 2005-2014. To check the relationship between stock market liquidity and firm performance, the ordinary least sequence and general linear models were applied on Gretl and SPSS, respectively. The results of this study showed positive relationship between independent variables, return and age on dependent variable Tobin’s Q. Further relationship between stock market liquidity and firm performance was also check and it was found that stock market liquidity was correlated with higher firm performance as measured by Tobin’s Q.