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Item MONITORING OF MULTIPLE LINEAR PROFILES BY USING RIDGE REGRESSION ESTIMATORS(UMT, Lahore, 2023) Muhammad FaranIn many quality control studies the performance of a product or process is usually characterized by a single response variable however, in some applications of quality control, the performance of a product or a process can be best characterized by a linear relationship between a response variable and one or more explanatory variables (Noorossana et al., 2011). But, when more than one explanatory variables are involved in the profile it may indicate the presence of high collinearity among explanatory variables, which is called multicollinearity (Gujarati, 2022). It should be noted that if the multicollinearity is neglected during the profile monitoring, then the designed control charts applied in phase II monitoring, provide lack of the sufficient effectiveness in detecting shifts or out of control signals. In this thesis, the effect of the multicollinearity has been observed on the monitoring of multiple linear profiles and propose some novel control charts (EWMA, Shewhart and Shewhart_3) for Intercept, Slopes and Mean Squared Error (MSE)/Error Variance by using Ridge Regression (RR) estimators in order to provide the solution of multicollinearity. An application of wind tunnel data by NASA Langley Research Centre has been used. The performance of the proposed novel control charts have been evaluated by using the Average Run Length (ARL) criterion. The results indicated that in the presence of high multicollinearity the proposed novel control charts for Intercept, Slopes and MSE/Error Variance based on RR estimators are outperform as compare to the traditional existing control charts based on Ordinary Least Squared (OLS) estimator.