You conducted a Augmented Dickey Fuller test. The hypothesis of this test are $H_0$: "Process has unit root" vs. $H_1$: "Process has no unit root". The test statistic is $-37.22113$. Now you need to compare this with the critical values under $H_0$. The critical values are given with:

$ 1\%: -3.435299 \\ 5\%: -2.863613 \\ 10\%: -2.567923.$

Since your test statistic is much lower than all of the critical values you can reject $H_0$ at a significance level $ \ \ <1\% $. So you can conclude with a very low probability of making an error that your time series has no unit root. You could also look at the p-value. The p-value shows the probabilty of making an error when rejecting $H_0$. Here, $p = 0.000$. So, you really should reject $H_0$.

You conducted a Augmented Dickey Fuller test. The hypothesis of this test are $H_0$: "Process has unit root" vs. $H_1$: "Process has no unit root". The test statistic is $-37.22113$. Now you need to compare this with the critical values under $H_0$. The critical values are given with:

$ 1\%: -3.435299 \\ 5\%: -2.863613 \\ 10\%: -2.567923.$

Since your test statistic is much lower than all of the critical values you can reject $H_0$ at a significance level $ \ \ <1\% $. So you can conclude with a very low probability of making an error that your time series has no unit root. So, you can reject $H_0$.

You conducted a Augmented Dickey Fuller test. The hypothesis of this test are $H_0$: "Process has unit root" vs. $H_1$: "Process has no unit root". The test statistic is $-37.22113$. Now you need to compare this with the critical values under $H_0$. The critical values are given with:

$ 1\%: -3.435299 \\ 5\%: -2.863613 \\ 10\%: -2.567923.$

Since your test statistic is much lower than all of the critical values you can reject $H_0$ at a significance level $ \ \ >1\% $. So you can conclude with a very low probability of making an error that your time series has no unit root. You could also look at the p-value. The p-value shows the probabilty of making an error when rejecting $H_0$. Here, $p = 0.000$. So, you really should reject $H_0$.

You conducted a Augmented Dickey Fuller test. The hypothesis of this test are $H_0$: "Process has unit root" vs. $H_1$: "Process has no unit root". The test statistic is $-37.22113$. Now you need to compare this with the critical values under $H_0$. The critical values are given with:

$ 1\%: -3.435299 \\ 5\%: -2.863613 \\ 10\%: -2.567923.$

Since your test statistic is much lower than all of the critical values you can reject $H_0$ at a significance level $ \ \ <1\% $. So you can conclude with a very low probability of making an error that your time series has no unit root. You could also look at the p-value. The p-value shows the probabilty of making an error when rejecting $H_0$. Here, $p = 0.000$. So, you really should reject $H_0$.