From 77bd1b86f635dd8db7067ad41bbcbc5f9792c8c2 Mon Sep 17 00:00:00 2001
From: muhammetozkan <149254887+muhammetozkancs@users.noreply.github.com>
Date: Fri, 5 Jun 2026 16:56:59 +0300
Subject: [PATCH 1/3] Create info_muhammet_ozkan.py

---
 Week01/info_muhammet_ozkan.py | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 Week01/info_muhammet_ozkan.py

diff --git a/Week01/info_muhammet_ozkan.py b/Week01/info_muhammet_ozkan.py
new file mode 100644
index 00000000..ad666dfb
--- /dev/null
+++ b/Week01/info_muhammet_ozkan.py
@@ -0,0 +1,2 @@
+student_id = "230315033"
+full_name = "Muhammet Özkan"

From b06f0eccded988d50ebedbd9fb26590a5e6ab53f Mon Sep 17 00:00:00 2001
From: muhammetozkan <149254887+muhammetozkancs@users.noreply.github.com>
Date: Fri, 5 Jun 2026 16:58:48 +0300
Subject: [PATCH 2/3] Create weighted_muhammet_ozkan.py

---
 Week02/weighted_muhammet_ozkan.py | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 Week02/weighted_muhammet_ozkan.py

diff --git a/Week02/weighted_muhammet_ozkan.py b/Week02/weighted_muhammet_ozkan.py
new file mode 100644
index 00000000..3c63098c
--- /dev/null
+++ b/Week02/weighted_muhammet_ozkan.py
@@ -0,0 +1,5 @@
+import random
+def weighted_srs(data: list, n: int, weights: list, with_replacement: bool = False):
+    if with_replacement:
+        return random.choices(data, weights=weights, k=n)
+    return random.sample(data, k=n, counts=weights)

From b2054d378d0332319fc0ba311a0d7a16d8dc953c Mon Sep 17 00:00:00 2001
From: muhammetozkan <149254887+muhammetozkancs@users.noreply.github.com>
Date: Fri, 5 Jun 2026 16:59:42 +0300
Subject: [PATCH 3/3] Create shifted_muhammet_ozkan.py

---
 Week03/shifted_muhammet_ozkan.py | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 Week03/shifted_muhammet_ozkan.py

diff --git a/Week03/shifted_muhammet_ozkan.py b/Week03/shifted_muhammet_ozkan.py
new file mode 100644
index 00000000..66289768
--- /dev/null
+++ b/Week03/shifted_muhammet_ozkan.py
@@ -0,0 +1,6 @@
+import math
+import statistics
+def shifted(sample):
+    mean = sum(sample) / len(sample)
+    median = statistics.median(sample)
+    return 24.4 * math.log(1 + abs(mean - median))