How to do the test:

State your Null Hypothesis in the form:
"The dependent variable is not related to the independent variable in a linear fashion" 
Choose a Critical Significance Level (α: alpha):
This is typically α = 0.05. 
Calculate the test statistic:
Use a spreadsheet to calculate your results, or access a helpsheet for SPSS and check here for data entry into SPSS. 
Reject or accept your Null Hypothesis.
Either
Compare the calculated statistic with critical values (Critical values table):
 If F ≥ F_{critical} → reject H_{0} → significant result.
 If F < F_{critical} → accept H_{0} → nonsignificant result.

Look at the calculated probability (P value):
 If P ≤ α → reject H_{0} → significant result.
 If P > α → accept H_{0} → nonsignificant result.

Compare the calculated statistic with critical values (Critical values table):

Report your results:
Plot your data using the appropriate graphs.
(Regression test: F_{df regression, df error} = …, P = …) 
If your regression test is significant, you can use your model for prediction, using a linear model of the form:
y = bx + c
(where y is the dependent variable, x is the independent variable, b is the slope of the line and c is the constant or intercept)
Use the coefficient of determination, R^{2}, to indicate how much of the variability in y is explained by the regression.