How to interpret Correlation along with Coefficients of multiple linear regression ?£ I have 10000 samples. There are 4 independent variables and 1 dependent variable.
The independent variables are all centered with 0 mean.

I found the correlation coefficients between each of these variables which are as below:

I used linear regression model and below is the summary of that model:

Now, based on the coefficients of the predictor variables in the linear regression model, I have been asked to find the significant predictor (s).
Based on just the correlation values, I was thinking X 4 will be the significant predictor but its regression coefficient says a different story altogether. (x4 has the least coefficient value in lm summary output). Can you help me understand what exactly is the correct way to identify the significant predictor (s)?
Also, Additionally even if I remove the x4 variable from the lm model, the Residual standard error remains the same which kinda re-iterates the fact the it is not a significant predictor? Is
my understanding correct here?
Also, I ran the VarImp function available in R which again returned a smaller value for x4.
&gt; varImp(lm_df, scale = TRUE)
     Overall
x1 33.673993
x2 34.858260
x3 33.820908
x4  1.969445
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.