How to interpret Correlation along with Coefficients of multiple linear regression ?£
The  table of correlation coefficients shows the pairwise correlation between the variables in your data set: on a range from 0 (no correlation) to 1 (full correlation), to what extent does variation in one variable explain variation in the other variable? 

The coefficients from the regression table, on the other hand, describe the relation between y and the different x's all else being equal. For example, the coefficient estimate of x1 tells you that, provided x2 to x4 are held constant, y is expected to change by .959 units when x1 changes by 1 unit. To understand the relation between your y and the different x's, these coefficients are usually more informative than pairwise correlations.

In addition to the strength of the effect (the site is the coefficients) which can be used to assess the substantive significance of a predictor variable, the regression table gives p-values that are used to judge the statistical significance of a predictor variable. A low p-value (conventionally below .05) suggests statistical significance (meaning a low probability of observing your data under the null hypothesis).

So looking at your regression output, you can see that all four predictor variables have a statistically significant relation with y. X1 to x3 are more significant than x4, both in terms of statistical and substantive significance. The contrast between the strong pairwise correlation between x4 and y and the small regression coefficient is due to multicollinearity between the x's: x4 covaries with the other predictors, and it is this covariance which accounts for the correlation with y.