diff --git a/notebook/problems.ipynb b/notebook/problems.ipynb
index ff2c594b..6561e545 100644
--- a/notebook/problems.ipynb
+++ b/notebook/problems.ipynb
@@ -30,12 +30,87 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": 11,
             "id": "34720ab6",
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Normal Distribution:\n",
+                        "Mean: -0.028619142236640034\n",
+                        "Median: -0.017270072330735627\n",
+                        "Mode: ModeResult(mode=-1.789772257755604, count=1)\n",
+                        "Standard Deviation: 0.8504782461047726\n",
+                        "Variance: 0.7233132470974502\n",
+                        "Range: 4.1504060137771885\n",
+                        "Kurtosis: -0.22984275091294082\n",
+                        "Skewness: 0.1876392566353732\n",
+                        "Chi-Square Distribution (degrees of freedom = 3):\n",
+                        "Mean: 3.0351302359457906\n",
+                        "Median: 2.396361773406997\n",
+                        "Mode: ModeResult(mode=0.0826946028144708, count=1)\n",
+                        "Standard Deviation: 2.3641911718270654\n",
+                        "Varaince: 5.589399896945032\n",
+                        "Range: 12.234950149104789\n",
+                        "Kurtosis: 2.078076991312349\n",
+                        "Skewness: 1.3992822141563568\n"
+                    ]
+                }
+            ],
             "source": [
-                "# TODO"
+                "# TODO\n",
+                "import numpy as np\n",
+                "from scipy.stats import kurtosis, skew, mode\n",
+                "\n",
+                "np.random.seed(88)\n",
+                "\n",
+                "normal_distribution = np.random.normal(0, 1, 100)\n",
+                "chi_square_distribution = np.random.chisquare(3, 100)\n",
+                "\n",
+                "# normal metrics\n",
+                "normal_mean = np.mean(normal_distribution)\n",
+                "normal_median = np.median(normal_distribution)\n",
+                "normal_mode = mode(normal_distribution)\n",
+                "normal_std = np.std(normal_distribution)\n",
+                "normal_variance = np.var(normal_distribution)\n",
+                "normal_range = np.amax(normal_distribution) - np.amin(normal_distribution)\n",
+                "normal_kurtosis = kurtosis(normal_distribution)\n",
+                "normal_skewness = skew(normal_distribution)\n",
+                "\n",
+                "\n",
+                "# chi metrics\n",
+                "chi_mean = np.mean(chi_square_distribution)\n",
+                "chi_median = np.median(chi_square_distribution)\n",
+                "chi_mode = mode(chi_square_distribution)\n",
+                "chi_std = np.std(chi_square_distribution)\n",
+                "chi_variance = np.var(chi_square_distribution)\n",
+                "chi_range = np.amax(chi_square_distribution) - np.amin(chi_square_distribution)\n",
+                "chi_kurtosis = kurtosis(chi_square_distribution)\n",
+                "chi_skewness = skew(chi_square_distribution)\n",
+                "\n",
+                "\n",
+                "# results\n",
+                "print(\"Normal Distribution:\")\n",
+                "print(\"Mean:\", normal_mean)\n",
+                "print(\"Median:\", normal_median)\n",
+                "print(\"Mode:\", normal_mode)\n",
+                "print(\"Standard Deviation:\", normal_std)\n",
+                "print(\"Variance:\", normal_variance)\n",
+                "print(\"Range:\", normal_range)\n",
+                "print(\"Kurtosis:\", normal_kurtosis)\n",
+                "print(\"Skewness:\", normal_skewness)\n",
+                "\n",
+                "print(\"Chi-Square Distribution (degrees of freedom = 3):\")\n",
+                "print(\"Mean:\", chi_mean)\n",
+                "print(\"Median:\", chi_median)\n",
+                "print(\"Mode:\", chi_mode)\n",
+                "print(\"Standard Deviation:\", chi_std)\n",
+                "print(\"Varaince:\", chi_variance)\n",
+                "print(\"Range:\", chi_range)\n",
+                "print(\"Kurtosis:\", chi_kurtosis)\n",
+                "print(\"Skewness:\", chi_skewness)\n"
             ]
         },
         {
@@ -54,12 +129,37 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": 4,
             "id": "d590308e",
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Standard Deviation: 2.0\n"
+                    ]
+                }
+            ],
             "source": [
-                "# TODO"
+                "# TODO\n",
+                "\n",
+                "data = [4, 2, 5, 8, 6]\n",
+                "\n",
+                "def calc_mean(data):\n",
+                "    return sum(data) / len(data)\n",
+                "\n",
+                "def calc_std_dev(data):\n",
+                "    mean = calc_mean(data)\n",
+                "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
+                "    std_dev = variance ** 0.5\n",
+                "    return std_dev\n",
+                "\n",
+                "\n",
+                "standard_deviation = calc_std_dev(data)\n",
+                "\n",
+                "\n",
+                "print(\"Standard Deviation:\", standard_deviation)"
             ]
         }
     ],