diff --git a/project1/Project1_A3c_least_squares_approximation.png b/project1/Project1_A3c_least_squares_approximation.png
index 874382b..f6d6f8c 100644
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diff --git a/project1/Project1_A3d_least_squares_approximation.png b/project1/Project1_A3d_least_squares_approximation.png
index 4ec3c5f..3368e41 100644
Binary files a/project1/Project1_A3d_least_squares_approximation.png and b/project1/Project1_A3d_least_squares_approximation.png differ
diff --git a/project1/Project1_A3e_coefficient_error.png b/project1/Project1_A3e_coefficient_error.png
index b24d0fb..243f1b0 100644
Binary files a/project1/Project1_A3e_coefficient_error.png and b/project1/Project1_A3e_coefficient_error.png differ
diff --git a/project1/Project1_Q3.py b/project1/Project1_Q3.py
index 474dcda..2125899 100644
--- a/project1/Project1_Q3.py
+++ b/project1/Project1_Q3.py
@@ -73,18 +73,18 @@ def f1(t: float) -> float:
     """
     return 1.0 / (1.0 + t**2)
 
-def build_A(N: int, n: int, f: callable) -> tuple[np.ndarray, np.ndarray]:
+def build_A(N: int, n: int, f: callable, f_range: tuple) -> tuple[np.ndarray, np.ndarray]:
     """
     Build the matrix A for the least squares problem.
     
     Parameters:
     n + 1 (int): The number of columns in the matrix A.
-    
+    f_range (tuple): The range of the function f.
     Returns:
     np.ndarray: The constructed matrix A.
     """
     """
-    {t_j}j=0->N, t_0=-5, t_N=5, t_j divides [-5, 5] equally
+    {t_j}j=0->N, t_0=f_range[0], t_N=f_range[1], t_j divides [f_range[0], f_range[1]] equally
     A = [[1, t_0, t_0^2, t_0^3, ..., t_0^n],
         [1, t_1, t_1^2, t_1^3, ..., t_1^n],
         ...
@@ -95,7 +95,7 @@ def build_A(N: int, n: int, f: callable) -> tuple[np.ndarray, np.ndarray]:
     Ax = b, x is the coeffs of the polynomial
     """
     A = np.zeros((N + 1, n + 1), dtype=float)
-    t = np.linspace(-5, 5, N + 1)
+    t = np.linspace(f_range[0], f_range[1], N + 1)
     for i in range(N + 1):
         A[i, :] = [t[i]**j for j in range(n + 1)]
     b = np.array([f(t_i) for t_i in t])
@@ -113,7 +113,7 @@ def question_3c():
         N = Ns[i]
         n = ns[i]
         A = np.zeros((N + 1, n + 1), dtype=float)
-        A, b = build_A(N, n, f1)
+        A, b = build_A(N, n, f1, (-5, 5))
         x = householder_lstsq(A, b)
         p_t = sum([x[j] * t**j for j in range(n + 1)])
         plt.subplot(1, 3, i + 1)
@@ -145,7 +145,7 @@ def question_3d():
         N = Ns[i]
         n = ns[i]
         A = np.zeros((N + 1, n + 1), dtype=float)
-        A, b = build_A(N, n, f1)
+        A, b = build_A(N, n, f1, (-5, 5))
         x = householder_lstsq(A, b)
         p_t = sum([x[j] * t**j for j in range(n + 1)])
         plt.subplot(1, 3, i + 1)
@@ -169,7 +169,7 @@ def question_3e():
     losses = []
     for n in ns:
         N = 2 * n
-        A, b = build_A(N, n, lambda t: f2(n, t))
+        A, b = build_A(N, n, lambda t: f2(n, t), (0, 1))
         x = householder_lstsq(A, b)
         true_coeffs = np.array([1.0 for _ in range(n + 1)])
         loss = np.linalg.norm(x - true_coeffs)