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The Pearson correlation equals the ''beta'' coefficient in SPSS which is the regression estimate from using one standardized predictor to predict a standardized outcome. In addition, the t-test in the regression for the unstandardized coefficient, B, is equivalent to testing if a correlation equals zero. The regression model is assumed to contain the intercept as well as the predictor. The Pearson correlation equals the ''beta'' coefficient in SPSS which is the regression estimate from using one standardized predictor to predict a standardized outcome (see for example Cohen and Cohen, (1983), pp 41-44) . In addition, the t-test in the regression for the unstandardized coefficient, B, is equivalent to testing if a correlation equals zero. The regression model is assumed to contain the intercept as well as the predictor.
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The following syntax for regression and correlations in SPSS give equivalent tests of whether a correlation between x and y is zero. The following syntax for regression and correlations in SPSS give equivalent tests of whether a Pearson correlation between two continuous variables, x and y, is zero.

The relationship between the Pearson correlation and a simple regression coefficient

The Pearson correlation equals the beta coefficient in SPSS which is the regression estimate from using one standardized predictor to predict a standardized outcome (see for example Cohen and Cohen, (1983), pp 41-44) . In addition, the t-test in the regression for the unstandardized coefficient, B, is equivalent to testing if a correlation equals zero. The regression model is assumed to contain the intercept as well as the predictor.

The following syntax for regression and correlations in SPSS give equivalent tests of whether a Pearson correlation between two continuous variables, x and y, is zero. 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT y
  /METHOD=ENTER x  .

CORRELATIONS
  /VARIABLES=y x
  /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE .

Reference

Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd edition). Hillsdale, NJ: Erlbaum.

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