Influential observation is one which either individually or together with several other observations has a demonstrably large impact on the values of various estimates regression coefficient. It been suggested by some authors that multicollinearity should be controlled before attempting to measure influence data point. In using ridge mitigate effect multicollinearity, there arises problem choos...

When Multicollinearity exists in a data set, the is considered deficient. frequently encountered observational studies. It creates difficulties when building regression models. phenomenon whereby two or more explanatory variable multiple model are highly correlated. Variable selection an important aspect of as such choice best subset among many variables to be included most difficult part analy...

One of the assumptions in ordinary least squares (OLS) estimating regression parameter is lack multicollinearity. If multicollinearity exists, Partial Least Square (PLS) and Principal Component Regression (PCR) can be used as alternative approaches to solve problem. This research intends compare those methods modeling factors that influence Human Development Index (HDI) North Sumatra Province 2...

Investment has an important role in the Gross Domestic Product component of expenditure. In this study, author took independent variables savings and mudharabah deposits influencing Product. The agreement used study is mudharabah. Researchers use secondary data for quantitative research. Based on partial simultaneous tests, each variable consisting effect GDP (dependent variable). Multicollinea...