commit d71bfac9e2f36936e4fc1b96e61a08bb533bff49
Author: louiscklaw <louiscklaw@users.noreply.gmail.com>
Date:   Sat Feb 1 01:59:53 2025 +0800

    update,

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..d54c38a
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,31 @@
+*.mp4 filter=lfs diff=lfs merge=lfs
+*.zip filter=lfs diff=lfs merge=lfs
+*.7z filter=lfs diff=lfs merge=lfs
+*.tar.gz filter=lfs diff=lfs merge=lfs
+*.jpg filter=lfs diff=lfs merge=lfs
+*.png filter=lfs diff=lfs merge=lfs
+*.avif filter=lfs diff=lfs merge=lfs
+*.webm filter=lfs diff=lfs merge=lfs
+*.mkv filter=lfs diff=lfs merge=lfs
+
+# Documents
+*.doc	diff=astextplain
+*.DOC	diff=astextplain
+*.docx	diff=astextplain
+*.DOCX	diff=astextplain
+*.dot	diff=astextplain
+*.DOT	diff=astextplain
+*.pdf	diff=astextplain
+*.PDF	diff=astextplain
+*.rtf	diff=astextplain
+*.RTF	diff=astextplain
+
+*.gif filter=lfs diff=lfs merge=lfs
+*.GIF filter=lfs diff=lfs merge=lfs
+*.bmp filter=lfs diff=lfs merge=lfs
+*.BMP filter=lfs diff=lfs merge=lfs
+*.tiff filter=lfs diff=lfs merge=lfs
+*.TIFF filter=lfs diff=lfs merge=lfs
+*.wav filter=lfs diff=lfs merge=lfs
+*.WAV filter=lfs diff=lfs merge=lfs
+*.log filter=lfs diff=lfs merge=lfs
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..67a5a14
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+**/~*.*
\ No newline at end of file
diff --git a/gitUpdate.bat b/gitUpdate.bat
new file mode 100644
index 0000000..a6629e2
--- /dev/null
+++ b/gitUpdate.bat
@@ -0,0 +1,7 @@
+git status .
+
+@pause
+
+git add .
+git commit -m"update anit961,"
+start git push
\ No newline at end of file
diff --git a/gitUpdate.sh b/gitUpdate.sh
new file mode 100755
index 0000000..c96734f
--- /dev/null
+++ b/gitUpdate.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+set -ex
+
+git config --global http.version HTTP/1.1
+git config --global lfs.allowincompletepush true
+git config --global lfs.locksverify true
+git config --global http.postBuffer 5368709120
+
+git add .
+
+git commit -m 'update,'
+
+git push
+
+echo "done"
diff --git a/meta.md b/meta.md
new file mode 100644
index 0000000..e69de29
diff --git a/task1/Assignment2-1.docx b/task1/Assignment2-1.docx
new file mode 100644
index 0000000..a9e8122
Binary files /dev/null and b/task1/Assignment2-1.docx differ
diff --git a/task1/Assignment2_template.ipynb b/task1/Assignment2_template.ipynb
new file mode 100644
index 0000000..5afb6f5
--- /dev/null
+++ b/task1/Assignment2_template.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c9eac7d0-4f4c-49e7-a335-b8ca8de34f20",
+   "metadata": {},
+   "source": [
+    "# Assignment 2:\n",
+    "In this assignment, you are given a dataset about bodyfat measure collected from a clinic. As a nutritionist, you want to use regression model to check whether your patients are in danger of high bodyfat.\n",
+    "The data contains physical measurements of patients:\n",
+    "- explantory variables: Age (in year), Weight (in pounds), Height (in inches), and BMI (Body Mass Index).\n",
+    "- response variable: bodyfat (in %)\n",
+    "\n",
+    "Complete the given tasks below. (Notice each of the missing code to be filled is a single line command, more than one command line will be downgraded)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f79e7b9-1fff-4bef-9fe8-8e3eb54c68cb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<h1 style=\"color:purple\">Author</h1>\n",
+    "\n",
+    "  \n",
+    "- Name:                      \n",
+    "- Student ID: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4725f61-cb83-4e3c-bc31-1a3992dadd28",
+   "metadata": {},
+   "source": [
+    "## Pre-loaded packages and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7e90f45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### Pandas is for using data structures\n",
+    "import pandas as pd\n",
+    "# statsmodels contain modules for regression and time series analysis\n",
+    "import statsmodels.api as sm\n",
+    "# numpy is for numerical computing of array and matrix\n",
+    "import numpy as np\n",
+    "# Matplotlib, Seaborn: plotting package\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns \n",
+    "# matplotlib Showing the plot right after the current code  \n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "# basic statistics package\n",
+    "import scipy.stats as stats\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8983cbf-0937-443f-ba43-d18956ad4a3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def outlier(dataframe,model,Type='all'):\n",
+    "    A = dataframe.copy()\n",
+    "    A = A.dropna()\n",
+    "    A.index = range(1,A.shape[0]+1)\n",
+    "    #A.index = range(0,A.shape[0])\n",
+    "    studentized_residuals = model.get_influence().resid_studentized_internal\n",
+    "    if Type == 'neg':\n",
+    "        return(A[studentized_residuals<-2])\n",
+    "    elif Type == 'posi':\n",
+    "        return(A[studentized_residuals>2])\n",
+    "    else:\n",
+    "        return(A[np.abs(studentized_residuals)>2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b47cb1ec-efe7-4124-a653-a9e942ae5e54",
+   "metadata": {
+    "id": "7NIZKrveWDbd"
+   },
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "def getvif(X):\n",
+    "    X = sm.add_constant(X)\n",
+    "    vif = pd.DataFrame()\n",
+    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
+    "    vif[\"Predictors\"] = X.columns\n",
+    "    return(vif.drop(index = 0).round(2)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bd86624-4cc8-4790-85d5-826a91ef02aa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "760c8202-fcaa-4e5f-bbb1-35f972fb4fd6",
+   "metadata": {},
+   "source": [
+    "## Getting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16326102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"https://drive.google.com/uc?export=download&id=1r6Za0azxHvJpjUA6lgMJyBqYIWRljmz8\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fd1d118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9496e740-2f7c-45cc-9409-a029d242a732",
+   "metadata": {},
+   "source": [
+    "## Preliminary study"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52d51f09-1993-47c5-83c8-4d874d0ddaf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scatter plot matrix of the whole data\n",
+    "sns_plot = sns.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "931e81f6-e2f6-4f07-ab1c-485f598f8a93",
+   "metadata": {},
+   "source": [
+    "### Task 1: Compute the correlation matrix; which variable has the strongest correlation with bodyfat? [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fe53540-95ce-4286-a0b4-0c35437dbe0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "$$$Code_Here$$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5d3504-6a3c-486f-afa2-639a4f85e015",
+   "metadata": {},
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a155616-4e5b-4992-8a04-467c85ae2977",
+   "metadata": {},
+   "source": [
+    "### Answer:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7688f2a9-8c5e-4fae-83f3-8b4b1ef7a799",
+   "metadata": {},
+   "source": [
+    "### Task 2: Split the data into train and test set by random (use 25 as the random seed/state) [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ae78c9d-deaa-4998-8928-5437c437e285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = $$$Code_Here$$$\n",
+    "test = $$$Code_Here$$$\n",
+    "train.shape, test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d82fae0-ce3c-4e4b-92f8-69657b421f74",
+   "metadata": {},
+   "source": [
+    "## Model Building"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d20382-fd4c-4030-9d43-677f267b447b",
+   "metadata": {},
+   "source": [
+    "### Task 3: Using the train set, fit a simple regression model for bodyfat by the variable from Task1 (with max correlation) [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4994daf5-b40b-41b3-93fd-ab3f90568c91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = train[\"bodyfat\"]\n",
+    "X = $$$Code_Here$$$\n",
+    "SLR = $$$Code_Here$$$\n",
+    "print(SLR.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba21618-7c3a-4baf-861f-cbdbb7cc7f05",
+   "metadata": {},
+   "source": [
+    "### Task 4: Using the trian set, fit a multiple regression model for bodyfat by all of the explanatory variables (i.e. age, weight, height and bmi) [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "500ee96e-c8db-4053-ad0b-4097ae291696",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = train[\"bodyfat\"]\n",
+    "X = $$$Code_Here$$$\n",
+    "MLR_all = $$$Code_Here$$$\n",
+    "print(MLR_all.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af58180f-45a0-4cc9-b042-3845996d683e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VIF of all predictors\n",
+    "getvif(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2cebb64-f6e0-41b6-96be-09f218ba05a3",
+   "metadata": {},
+   "source": [
+    "### Task 5: Multicollinearity is reflected from above. Does bmi cause the problem? Briefly explain using your understanding about bmi.[5pts]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87def1eb-ba5c-49a9-bfdf-48b3af5e1a81",
+   "metadata": {},
+   "source": [
+    "### Answer: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc60e40f-72b8-43a5-8b70-40fec8cc4f4d",
+   "metadata": {},
+   "source": [
+    "### Task 6: Using the train set, fit a multiple regression model for bodyfat by all of the explanatory variables except bmi [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73499ab4-b937-4b30-a647-aeedc20d929e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = train[\"bodyfat\"]\n",
+    "X = $$$Code_Here$$$\n",
+    "MLR = $$$Code_Here$$$\n",
+    "print(MLR.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "883671ff-79ce-4c80-941c-957b0c8bcde2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "getvif(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "742ef24b-3b03-439d-9d18-47c1626efe05",
+   "metadata": {},
+   "source": [
+    "## Compare the predictive power between SLR and MLR using the test set. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "708e9eed-0b88-49d1-93a1-fba94254b597",
+   "metadata": {},
+   "source": [
+    "### Task 7: Compute the RMSE for MLR [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "469672cd-2db3-49eb-b137-c6d63033a55f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Test_X_SLR = test['bmi']\n",
+    "Test_X_MLR = $$$Code_Here$$$\n",
+    "\n",
+    "Test_Y_SLR = SLR.predict(sm.add_constant(Test_X_SLR))\n",
+    "Test_Y_MLR = $$$Code_Here$$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f077e2cf-4cad-4578-90bb-a63c32ddda18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Test_Y = test[\"bodyfat\"]\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "rmse_SLR = np.sqrt(mean_squared_error(Test_Y, Test_Y_SLR))\n",
+    "rmse_MLR = $$$Code_Here$$$\n",
+    "print(\"RMSE for test set (SLR): \", rmse_SLR)\n",
+    "print(\"RMSE for test set (MLR): \", rmse_MLR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1659a34e-8080-4e92-be34-717acf81c82c",
+   "metadata": {},
+   "source": [
+    "## Final Model and application"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89256526-a134-4e7d-9a84-1f7dc331bfc2",
+   "metadata": {},
+   "source": [
+    "### Task 8: Refit the better model from Task7 using the full dataset. [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84da494e-36bf-4f96-9376-3fb43e8df704",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = data[\"bodyfat\"]\n",
+    "X = $$$Code_Here$$$\n",
+    "Final = $$$Code_Here$$$\n",
+    "print(Final.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "539487cb-0e72-4a2b-85cd-6dbac1d2e321",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Task 9:  Predict the body fat for a new patient using the final model from Task 8: age=35, weight=170 pounds. Height=72 inches, BMI=23 [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71aacafa-0cf1-40ed-8ee9-d5d09ebf26ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "$$$Code_Here$$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37f640ce-dfd6-42c9-90d5-13b05769448f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Task 10: by using residuals (from final model) outlier analysis, report those patients are in danger (i.e. % of fat is much higher than what we expected from the final model) [5pts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f06aa2bd-9836-46c2-8e8d-4a876c9ae253",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "$$$Code_Here$$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "149424ee-f928-479b-8b07-19c3ae88f751",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/task1/google_drive.csv b/task1/google_drive.csv
new file mode 100644
index 0000000..2031e5f
--- /dev/null
+++ b/task1/google_drive.csv
@@ -0,0 +1,250 @@
+bodyfat,age,weight,height,bmi
+11.8,27,168,71.25,23.2645614
+22.2,69,177.75,68.5,26.63077415
+10.6,57,147.75,65.75,24.02654368
+47.5,51,219,64,37.58715821
+24.2,40,202.25,70,29.01668367
+23.3,52,167,67.75,25.57721164
+26,54,230,72.25,30.97472492
+9,47,184.25,74.5,23.3372821
+4,47,127.5,66.75,20.1169886
+22.1,43,150,69.25,21.98907845
+9.4,26,152.25,69,22.48093888
+16.7,40,158,69.25,23.1618293
+29.9,65,189.75,65.75,30.85642412
+11.7,23,198.25,73.5,25.79846361
+25.8,61,178,67,27.87569615
+21.5,81,161.25,70.25,22.97007384
+15.1,34,140,70.5,19.80182083
+25.2,26,223,70.25,31.76636567
+18.7,50,194.75,70.75,27.35142154
+17.5,46,167,67,26.15304076
+23.6,41,232.75,74.25,29.67919373
+8.8,55,146.75,68.75,21.82669752
+9.4,23,159.75,72.25,21.51396655
+21,27,200.25,73.5,26.05872553
+15.9,42,193.5,70.5,27.36894523
+8.6,40,167.5,71.5,23.03340017
+26.7,58,161.75,67.25,25.14288084
+15.6,31,140.25,68.25,21.16668679
+13.1,37,151,67,23.64736021
+34.3,35,228.25,69.5,33.21976088
+30.2,69,215.5,70.5,30.48065993
+18,43,165.5,68.5,24.7954606
+5.7,29,160.25,71.25,22.19134503
+26.6,67,167,67.5,25.76702332
+3.9,42,136.25,67.5,21.02249657
+22.9,32,209.25,71,29.18126364
+19.7,43,170.75,68.5,25.58202355
+6.3,54,155.25,69.25,22.75869619
+29.9,37,241.25,71.5,33.17497188
+21.4,40,168.5,69.25,24.70106479
+9.4,31,151.25,72.25,20.36924846
+25.2,55,198.5,74.25,25.31179358
+16,28,183.75,67.75,28.14259065
+31.5,54,202.5,70.75,28.43986066
+29.8,56,178.75,68.5,26.78059566
+10,28,182.5,72.25,24.57777086
+12.5,55,126.5,66.75,19.9592083
+3.7,27,133.25,64.75,22.34307777
+18.2,44,179.75,69.5,26.16101651
+38.1,42,244.25,76,29.72779605
+11.3,50,162.5,66.5,25.83243824
+5.3,25,143.75,72.5,19.22592152
+28.7,43,200.5,71.5,27.57132378
+11.8,61,143,65.75,23.25411673
+20.5,35,177.25,71,24.71865701
+24.9,40,176.75,71,24.64892878
+21.2,30,205.25,71.25,28.42292398
+20.5,41,202.25,72.5,27.05003567
+27.9,52,206.5,74.5,26.15548849
+24.9,46,192.5,71.75,26.28707402
+12.3,23,154.25,67.75,23.62446045
+26.1,50,157,66.75,24.77150753
+13.6,45,135.75,68.5,20.33827055
+27.2,49,216.25,74.5,27.39043286
+17.5,40,170.5,74.25,21.74136426
+32.3,57,205.5,70,29.48295918
+20.8,32,180.5,69.5,26.27017235
+22.1,35,187.75,69.5,27.32534548
+6,44,184,74,23.62162162
+21.8,35,166.25,68,25.27546496
+33.6,72,201,69.75,29.04443674
+12.5,30,136.5,68.75,20.3021752
+20.4,41,210.5,72,28.54581405
+27,70,170.75,70,24.49739796
+18.4,64,190.25,72.75,25.27050933
+14.1,48,176,73,23.21786451
+3.7,27,159.25,71.5,21.89891926
+18.5,61,148.25,67.5,22.87401921
+19.2,35,217,73.75,28.04729675
+34.5,45,262.75,68.75,39.0798281
+6.1,22,173.25,72.25,23.33204823
+20.9,24,210.25,74.75,26.45263476
+13.9,43,164.25,73.25,21.52015749
+20.4,49,212.75,75,26.58902222
+22.3,49,196.75,73.75,25.42997989
+21.5,54,151.5,70.75,21.27722908
+13.5,55,125,64,21.45385743
+14.6,33,196,73,25.85625821
+5.2,55,142.25,67.25,22.11174528
+32.8,47,195,72.5,26.0803805
+11.9,32,182,73.75,23.52353921
+9.6,38,188.75,73.25,24.73016575
+10.2,47,158.25,72.25,21.31195747
+22.2,47,197,72,26.71508488
+17.3,28,171.5,75.25,21.29150893
+19.5,49,168.25,71.75,22.97558548
+12.4,54,153.25,70.5,21.67592173
+16.9,39,234.75,74.5,29.73366065
+28,43,183.25,70,26.2907653
+29.4,43,187.75,74,24.10304054
+15.2,68,155.5,69.25,22.79534465
+25.8,40,191,74,24.52027027
+22.5,38,187.25,69.25,27.4496996
+10.9,55,179.75,68.75,26.73491571
+27.3,34,218.75,72,29.66459298
+25.3,44,185.25,71.5,25.47425302
+31.6,48,217,70,31.13285715
+25.3,22,154,66.25,24.66631541
+9.6,47,160.5,70.25,22.86323628
+24.7,42,224.75,74.75,28.2769544
+15,53,154.5,69.25,22.6487508
+25.3,36,226.75,71.75,30.96412485
+17.4,43,152.25,67.75,23.31814654
+18.3,52,203.25,74.25,25.91749141
+35,65,224.5,68.25,33.88179098
+17.4,53,224.5,77.75,26.10783594
+8.5,56,160.75,73.75,20.77697213
+24.6,61,179.75,65.75,29.23026211
+8.3,46,176.75,72.5,23.63952438
+27.2,42,177.5,68.75,26.40026446
+22.6,54,198,72,26.85069444
+20.3,35,224.75,72.25,30.26769316
+8.8,29,160.75,69,23.73603234
+16.5,35,172.75,69.5,25.14222866
+14.8,55,169.5,68.25,25.5811295
+26.1,62,216,73.25,28.30048108
+13.9,51,179,72,24.27411266
+29.6,25,206.5,69.75,29.839185
+21.3,42,163,70.25,23.21936146
+30,55,183.5,67.5,28.31286694
+22.5,31,177.25,71.5,24.37415032
+7.9,34,131.5,67.5,20.2896022
+22.4,40,168.25,71.25,23.29918129
+32,41,212,71.5,29.1527214
+21.3,41,218.5,71,30.47123587
+23.1,64,160,65.75,26.01859215
+18.6,62,168.75,67.5,26.03703704
+40.1,49,191.75,65,31.90538461
+11,70,134.25,67,21.02422589
+19.2,26,181,69.75,26.15444303
+30.7,54,193.25,70.25,27.52847608
+32.3,41,247.25,73.5,32.17488084
+22,42,156.25,69,23.0715711
+26.8,64,150.25,67.25,23.35528807
+10.8,40,133.5,67.5,20.5981893
+13.6,51,149.25,69.75,21.56657803
+7.5,51,154.5,70,22.1660204
+0.7,35,125.75,65.5,20.6053843
+29.3,72,186.75,66,30.13894628
+17.3,43,194,75.5,23.9256173
+22.9,31,148,67.5,22.83544581
+8.5,47,165.25,70.5,23.37322067
+32.6,50,203,67,31.79082201
+30.4,66,234.25,72,31.76654128
+13.8,55,154.75,71.5,21.2801115
+18.3,40,173.25,69.5,25.21499922
+19.5,50,172.75,73,22.78912554
+12.4,64,155.25,69.5,22.59525904
+19.3,43,200.25,73.5,26.05872553
+12.1,40,159.25,69.75,23.01157488
+20.4,58,181.5,68,27.59396626
+25.4,43,177,69.25,25.94711256
+21.2,49,198.5,73.5,25.83099634
+12.2,43,178.25,70.25,25.39172503
+28.7,24,184.25,71.25,25.5148538
+14.9,56,174.5,69.5,25.39692563
+6.6,40,139.25,69,20.56138416
+28.4,50,196.75,68.25,29.69372996
+6.3,49,152.75,73.5,19.87750474
+18.1,44,187.5,72.25,25.25113444
+16.5,33,211.75,73.5,27.55523161
+7.7,39,125.25,68,19.04211722
+14.9,72,157.75,67.25,24.52110944
+22.7,40,171.25,70.5,24.22187012
+16.6,44,208.75,73,27.53823419
+29,67,199.5,68.5,29.88939208
+7.8,27,216,76,26.28947368
+14.7,40,160.25,68.75,23.83460496
+16.5,27,156.75,67.25,24.36566659
+28,62,201.25,69.5,29.29015061
+24.5,52,199.25,71.75,27.20882856
+10.8,47,159.75,70.75,22.43589007
+27,72,168,69.25,24.62776786
+26.6,39,219.25,74.25,27.95773674
+32.6,67,227.75,72.75,30.25155584
+34.8,44,223,69.75,32.22342981
+12.9,55,156.75,71.5,21.55513717
+5.6,39,148.5,71.25,20.56421052
+20.1,41,172.75,71.25,23.92233918
+22.8,42,162.75,72.75,21.61774188
+20.5,46,177,70,25.39408163
+13,33,184.25,68.75,27.40421818
+4.1,25,191,74,24.52027027
+22.1,47,178.25,70,25.57341837
+21.8,39,166.75,70.75,23.41899636
+11.5,54,161.75,67.5,24.95698217
+16.1,57,182.25,71.75,24.88737267
+11.5,40,145.75,67.25,22.65579525
+14.9,42,165.25,69.75,23.87857299
+16,47,151.5,66.75,23.90371586
+11.4,41,153,69.25,22.42886001
+7.1,26,186.25,74.5,23.59060403
+18.1,49,171.75,71.5,23.61782972
+24.4,41,185,68.25,27.92040682
+17.7,32,148.75,70,21.34107143
+8,51,137.25,67.75,21.0207922
+9.9,37,145.25,69.25,21.29275763
+24.4,41,168.25,69.5,24.48729362
+20.4,48,173.75,72,23.56216243
+27.3,63,219.15,69.5,31.89533668
+24.8,62,191.5,72.25,25.78982531
+15.4,58,175.5,71.5,24.13350286
+13.8,50,161,66.5,25.59398496
+31.4,67,163.75,67.75,25.07945153
+27.1,44,186,69.75,26.87694146
+14.2,24,156,70.75,21.90922598
+10.4,26,184.75,72.25,24.88078447
+20.1,48,177.25,72.75,23.54374653
+29,34,195.75,71,27.29860147
+16.9,36,176.25,71.5,24.23663749
+19.1,28,179,68,27.21388408
+26,72,190.75,70.5,26.97998089
+32.9,44,166,65.5,27.20074587
+7.1,49,140.5,68,21.36061851
+3,35,152.25,67.75,23.31814654
+17,65,127.5,65.75,20.73356561
+19.2,24,208.5,72.75,27.69461863
+10.1,27,146,72.25,19.66221669
+15.2,28,200.5,69.75,28.97218689
+25.5,42,180,68.25,27.16580124
+23.6,47,197,73.25,25.81108691
+31.2,28,205.75,69,30.38064483
+6.6,42,167.25,72.75,22.21546746
+25.8,60,157.75,67.5,24.33980796
+24.3,62,167.5,71.5,23.03340017
+10.3,23,188.15,77.5,22.02196878
+26.7,48,175.25,71.75,23.93147907
+19.6,26,241.75,74.5,30.62028737
+12.4,25,176,72.5,23.53921522
+17.8,46,156.5,68.25,23.61915496
+14,28,151.25,67.75,23.16498958
+20.8,40,192.25,73.25,25.18873836
+18.8,66,171.25,69.25,25.10419789
+8.8,57,162.5,69.5,23.65043217
+31.9,74,207.5,70,29.76989796
+23.6,43,170.75,67.5,26.34562414
+20.9,35,162.75,66,26.26566804
+17,56,167.75,68.5,25.132559
+17.7,42,168,71.5,23.10215658