diff --git a/Day 7 Homework.md b/Day 7 Homework.md
index 461d6a1..c5fc226 100644
--- a/Day 7 Homework.md	
+++ b/Day 7 Homework.md	
@@ -50,6 +50,53 @@ From the histogram shown above, we can say that jitter is expected to be more fl
 
 ![Jitter & Duration relationship](https://gogs.gtsplab.com:5432/j/python-bootcamp/src/Branch_Karen/jitter%20relationship.png)
 
+```
+import numpy as np
+
+# Durations to measure in seconds
+durations = [0.01, 0.1, 1.0]
+
+# Number of repetitions
+num_repetitions = 100
+
+# Lists to store jitter and duration for each measurement
+jitter = []
+duration = []
+
+# Function to measure jitter
+def measure_jitter(duration):
+    start_time = time.perf_counter_ns()
+    time.sleep(duration)
+    end_time = time.perf_counter_ns()
+    return end_time - start_time
+
+# Measure jitter for each duration and repeat for num_repetitions times
+for dur in durations:
+    for _ in range(num_repetitions):
+        jitter.append(measure_jitter(dur))
+        duration.append(dur)
+
+# Convert lists to NumPy arrays
+jitter = np.array(jitter)
+duration = np.array(duration)
+
+# Fit a polynomial curve of degree 2
+coeffs = np.polyfit(duration, jitter, 2)
+poly_func = np.poly1d(coeffs)
+
+# Generate data points for the curve
+duration_range = np.linspace(min(duration), max(duration), 100)
+jitter_fit = poly_func(duration_range)
+
+# Plot the measured data points and the polynomial curve
+plt.scatter(duration, jitter, color='b', alpha=0.5, label='Measured Data')
+plt.plot(duration_range, jitter_fit, color='r', label='Polynomial Fit (Degree 2)')
+plt.xlabel('Sleep Duration (s)')
+plt.ylabel('Jitter (ns)')
+plt.title('Polynomial Relationship between Sleep Duration and Jitter')
+plt.legend()
+plt.show()
+```
 
 **2. Create a dict with 1 million integer keys, each with 64 bytes of random values.**