diff --git a/pyerrors/fits.py b/pyerrors/fits.py
index a08dfa64..01a98c98 100644
--- a/pyerrors/fits.py
+++ b/pyerrors/fits.py
@@ -301,7 +301,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda my_var, **kwargs: my_var[0] / x.ravel()[0].value * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
+        result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
 
     output.fit_parameters = result + const_par
 
@@ -418,7 +418,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
+        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
 
     output.fit_parameters = result
     output.chisquare = chisqfunc(np.asarray(params))
@@ -612,7 +612,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * fit_result.x[i], list(y), man_grad=list(deriv[i])))
+        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
 
     output.fit_parameters = result + const_par
 
diff --git a/pyerrors/roots.py b/pyerrors/roots.py
index 1cb7b46f..e26b44e7 100644
--- a/pyerrors/roots.py
+++ b/pyerrors/roots.py
@@ -1,3 +1,4 @@
+import numpy as np
 import scipy.optimize
 from autograd import jacobian
 from .obs import derived_observable
@@ -33,5 +34,5 @@ def find_root(d, func, guess=1.0, **kwargs):
     da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
     deriv = - da / dx
 
-    res = derived_observable(lambda x, **kwargs: x[0] / d.value * root[0], [d], man_grad=[deriv])
+    res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])
     return res
diff --git a/tests/fits_test.py b/tests/fits_test.py
index 53a23dc9..78cb7c6b 100644
--- a/tests/fits_test.py
+++ b/tests/fits_test.py
@@ -10,6 +10,26 @@ import pytest
 np.random.seed(0)
 
 
+def test_fit_lin():
+    x = [0, 2]
+    y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
+         pe.pseudo_Obs(2, 0.1, 'ensemble')]
+
+    res = pe.fits.fit_lin(x, y)
+
+    assert res[0] == y[0]
+    assert res[1] == (y[1] - y[0]) / (x[1] - x[0])
+
+    x = y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
+         pe.pseudo_Obs(2, 0.1, 'ensemble')]
+
+    res = pe.fits.fit_lin(x, y)
+
+    m = (y[1] - y[0]) / (x[1] - x[0])
+    assert res[0] == y[1] - x[1] * m
+    assert res[1] == m
+
+
 def test_least_squares():
     dim = 10 + int(30 * np.random.rand())
     x = np.arange(dim)
@@ -32,6 +52,10 @@ def test_least_squares():
     out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
     beta = out.fit_parameters
 
+    str(out)
+    repr(out)
+    len(out)
+
     for i in range(2):
         beta[i].gamma_method(S=1.0)
         assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
@@ -136,6 +160,10 @@ def test_total_least_squares():
     out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
     beta = out.fit_parameters
 
+    str(out)
+    repr(out)
+    len(out)
+
     for i in range(2):
         beta[i].gamma_method(S=1.0)
         assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)