According to the correlation test the relationship between independent variable i.e, age groups and all dependent variables are interpreted as follows:• The independent variable (age groups) has a positive and mild relationship with resilience (r=.264).• The independent variable (age groups) has a mild negative relationship with narcissism (r=-.108).• The independent variable (age groups) has a mild negative relationship with authority (r=-.

079).• The independent variable (age groups) has a low positive relationship with self-sufficiency (r=.042).• The independent variable (age groups) has a mild negative relationship with superiority (r=-.175).

• The independent variable (age groups) has a mild negative relationship with exhibitionism (r=–.128).• The independent variable (age groups) has almost no relationship with exploitativeness (r=.004).• The independent variable (age groups) has a very low positive relationship with vanity (r=.024).• The independent variable (age groups) has a low positive relationship with entitlement (r=.

063).4.3 Analysis of variance of the impact of Independent variable on Dependent variable (Regression Model)Regression analysis is carried out for estimating the relationship between dependent variable and predictor or predictors.

It helps us to understand how the value of dependent variable changes when the independent variables are varied, while the other independent variables are held fixed. This model is most commonly used for forecasting and prediction about the relation between dependent and independent variable and on the basis of t value we accept or reject hypothesis. The conclusion from the above regression analysis shows that there is a positive and mild relationship (R=.264) between predictor age group and dependent variable resilience, R square value shows that 6.

90% variability or change in resilience is explained by independent variable age group, the value of adjusted R squared (.063) shows that there is inconsiderable variation which in succession tells us that the results can be generalized beyond the sample to the population. In Analysis of variance (ANOVA) table, the F value (11.053) is greater than 3.90 reveals that the overall fitness of the model is logically good and the value of P i.e, .

001 which is less than 95% (.05) level of significance. In Coefficient table (Beta=.264, t=3.325, p=0.001) which reveals that pragmatic effect of predictor on outcome (dependent variable) that’s why this hypothesis is accepted.The conclusion we derive from the above Regression analysis table that there is a mild positive relationship (R=.

108) between independent variable age group and dependent variable narcissism, R square value (.012) shows that 1.20% change in narcissism is explained by independent variable age group, the Adjusted R square value (.005) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (1.732) is less than 2.

90 reveals that the overall fitness of the model is logically not good and the value of P i.e, .190 which is greater than 95% (.05) level of significance. In Coefficient table the Beta value (-.

108) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-1.316) and p (.190) is greater than level of significance so we accept our null hypothesis.The conclusion we derive from the above Regression analysis table that there is a low positive relationship (R=.079) between independent variable age group and dependent variable authority, R square value (.006) shows that .

06% change in authority is explained by independent variable age group, the Adjusted R square value (.000) shows that there is no variation. In Analysis of variance (ANOVA) table, the F value (.931) is less than 3.90 reveals that the overall fitness of the model is logically not good and the value of P i.

e, .336 which is greater than 95% (.05) level of significance.

In Coefficient table the Beta value (-.079) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-.965) and p (.336) is greater than significant value so the null hypothesis become accepted