Accurate effort prediction of software projects is of concern to portfolio managers, customers, vendors as well as project managers. Ordinary least squares (OLS) regression is widely used to create software prediction models, and it seems to perform just as well or better than most other, non-regression, prediction models. Software data sets may however exhibit certain characteristics that do n...

This study intends to examine the effects of political unrest and government infrastructure spending on economic growth in Democratic Republic Congo between 1980 2020. is being done assess relationship public DRC as well impact that instability has relationship. According findings an econometric regression employed estimation techniques ranging from most basic reliable, such ordinary least squa...

Managers are typically interested in using equations that provide accurate volumes for large, merchantable trees while modelers are often more interested in equations that meet statistical assumptions. Therefore, modelers often use weighted (WLS) rather than ordinary least squares (OLS) to fit individual tree volume equations since the random error variance is not constant. Since OLS and WLS pr...

The objective of this study was to evaluate the impact SEB public policy innovation on average supply tariff (AST) from its implementation in year 2000 until 2020, considering moderation foreseen RD&I SEB, which is aligned with SDG 7 Agenda 2030. methodological procedures included (a) a sample consisting 40% market (seven one hundred and eleven electricity distributors) (in consumers billin...

A function based nonlinear least squares estimation (FNLSE) method is proposed and investigated in parameter estimation of Jelinski–Moranda software reliability model. FNLSE extends the potential fitting functions of traditional least squares estimation (LSE), and takes the logarithm transformed nonlinear least squares estimation (LogLSE) as a special case. A novel power transformation function...

Existing dimension reduction (DR) methods such as ordinary least squares (OLS) and sliced inverse regression (SIR) often perform poorly in the presence of outliers. Ellipsoidal trimming can be used to create outlier resistant DR methods that can also give useful results when the assumption of linearly related predictors is violated. Theory for SIR and OLS is reviewed, and it is shown that sever...

In designing Monte Carlo simulation studies for analyzing …nite sample properties of econometric inference methods, one can use either IID drawings in each replication for any series of exogenous explanatory variables or condition on just one realization of these. The results will usually di¤er, as do their interpretations. Conditional and unconditional limiting distributions are often equivale...

In this paper, the main idea is to compute the robust regression model, derived by experimentation, in order to achieve a model with minimum effects of outliers and fixed variation among different experimental runs. Both outliers and nonequality of residual variation can affect the response surface parameter estimation. The common way to estimate the regression model coefficients is the ordinar...