DEPRESSION AND PSYCHOLOGICAL WELL-BEING AMONG PEOPLE HAVING COVID-19 UNDERGONE QUARANTINE

Aims Of Study: Coronavirus is a potentially deadly disease that mostly affects human lung tissue. Multiple cases of unexplained respiratory tract infections were reported to the World Health Organization China in December 2019. The focus of this research was to assess association between depression and PWB in patients who had been isolated due to covid-19. Methodology: A sample of 250 people with covid-19 post quarantine, with age ranges from 25 to 45 were selected by using purposive sampling methods. Depression, anxiety, stress scale and psychological well-being scales were administered. Regression analysis and t-test were employed for statistical analysis. Results: Obtained results evidenced that depression is significantly predict psychological health in the covid-19 population [B= -.015, -.019, F (7.915) sig= .000]. Gender difference is also observed in variable of depression also [(4.78) =.030, p.000]. Limitations and future implications: Future research may base on longitudinal analyses focusing on familial and social factors that may influence the psychological well-being of people living in quarantine. Originality: I certify that the intellectual substance of this article is the result of my own effort and that all assistance and sources used in the preparation of this article have been acknowledged. Conclusion: It was concluded that Depression affects people with COVID-19 and PWB after quarantine. future research may focus more on the influence of the interaction between quarantine and adults’ mental health to fully comprehend the link. 

Addressing Linear Regression Models With Correlated Regressors: Some Package Development in R

Regression analysis is widely used in manyelds such as economics,nance, technology and social sciences. A linear regression model describes the relationship between dependent variable and one or more regressors. A problem named multicollinearity is the existence/presence of nearly linear dependency among regressors. The existence of severe multicollinearity questions the accuracy of the parameters estimate in a linear regression model, because the variance of the ordinary least square estimator (OLSE) would be large enough. Therefore, detection of multicollinearity can be considered asrst step for giving solution of this problem. There are several indicators (diagnostic measures) for the quanti cation of collinearity among regressors available in the literature. Widely used and the most suggested diagnostics are values of pair-wise correlations among regressors, overall R2, VIF and tolerance limit, eigenvalues, condition number and condition indices etc. However, there is no unique method that can detect and measure the existence and strength of multicollinearity in data. For remedy or reduction of collinearity among regressors, biased estimators are suggested such as the ridge and Liu estimators in literature and are alternative to the OLSE. These alternative methods also improve the accuracy of the parameter estimates of linear regression models. The biased methods resemble each other and are based on similar principals. In this study, we propose two new detection methods for indication of collinearity vi existence and developed 3 packages in R language namely mctest, lmridge and liureg. The package mctest computes already existing collinearity diagnostic measures and our proposed measures. We also compare existing and our proposed collinearity diagnostics measures for the detection of collinearity existence among regressors. Our proposed collinearity diagnostics not only perform well for simulated but also for real collinear data sets. For computation and detection of existence of collinearity mctest package can be used. The package contains functions omcdiag for computation of overall and imcdiag for individual collinearity diagnostics as described in Chapter 3, Section 3.5. For estimation and testing of ridge coe cients, the ridge package lmridge can be used to compute ridge coe cients, di erent existing biasing parameters available in literature and testing of ridge coe cients with 15 ridge related statistics such as R2, adjusted-R2, mean square error and e ective degrees of freedom etc. Similarly, estimation and testing of Liu coe cient can be done using package liureg package. It computes biasing parameters from (Liu, 1993) and the Liu related statistics. In addition, numerical comparisons between existing estimator of ridge and Liu are also done by using existing collinear data set from Hald (1952), Longley (1967) and Malinvaud (1968).