Presumably your data would look a bit like this.
| depth | weed | |
|---|---|---|
| 1 | 0.40 | 84.00 |
| 2 | 0.60 | 76.00 |
| 3 | 0.90 | 73.00 |
| 4 | 1.40 | 52.00 |
| 5 | 0.30 | 94.00 |
| 6 | 1.30 | 57.00 |
You can plot a scatter plot.
If the data look like this then they are suitable for regression analysis.
mod <- lm(weed ~ depth, data = d)
summary(mod)
##
## Call:
## lm(formula = weed ~ depth, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.592 -3.003 -0.411 6.328 13.498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.18 2.91 34.5 < 2e-16 ***
## depth -39.55 3.28 -12.0 1.4e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.84 on 28 degrees of freedom
## Multiple R-squared: 0.838, Adjusted R-squared: 0.832
## F-statistic: 145 on 1 and 28 DF, p-value: 1.37e-12
Read the notes on correlation and regression and try the examples again in SPSS.
A regression analysis (and correlation) is based on the assumption of a linear relationship. There are a lot of more advanced methods that go beyond this, but you don't know about them. So plot your data and watch out for effects like the one below. You would need to interpret it yourself.
