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*.mp4 filter=lfs diff=lfs merge=lfs
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*.tar.gz filter=lfs diff=lfs merge=lfs
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**/~*.*

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git status .
@pause
git add .
git commit -m"update anit961,"
start git push

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#!/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"

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{
"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
}

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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
1 bodyfat age weight height bmi
2 11.8 27 168 71.25 23.2645614
3 22.2 69 177.75 68.5 26.63077415
4 10.6 57 147.75 65.75 24.02654368
5 47.5 51 219 64 37.58715821
6 24.2 40 202.25 70 29.01668367
7 23.3 52 167 67.75 25.57721164
8 26 54 230 72.25 30.97472492
9 9 47 184.25 74.5 23.3372821
10 4 47 127.5 66.75 20.1169886
11 22.1 43 150 69.25 21.98907845
12 9.4 26 152.25 69 22.48093888
13 16.7 40 158 69.25 23.1618293
14 29.9 65 189.75 65.75 30.85642412
15 11.7 23 198.25 73.5 25.79846361
16 25.8 61 178 67 27.87569615
17 21.5 81 161.25 70.25 22.97007384
18 15.1 34 140 70.5 19.80182083
19 25.2 26 223 70.25 31.76636567
20 18.7 50 194.75 70.75 27.35142154
21 17.5 46 167 67 26.15304076
22 23.6 41 232.75 74.25 29.67919373
23 8.8 55 146.75 68.75 21.82669752
24 9.4 23 159.75 72.25 21.51396655
25 21 27 200.25 73.5 26.05872553
26 15.9 42 193.5 70.5 27.36894523
27 8.6 40 167.5 71.5 23.03340017
28 26.7 58 161.75 67.25 25.14288084
29 15.6 31 140.25 68.25 21.16668679
30 13.1 37 151 67 23.64736021
31 34.3 35 228.25 69.5 33.21976088
32 30.2 69 215.5 70.5 30.48065993
33 18 43 165.5 68.5 24.7954606
34 5.7 29 160.25 71.25 22.19134503
35 26.6 67 167 67.5 25.76702332
36 3.9 42 136.25 67.5 21.02249657
37 22.9 32 209.25 71 29.18126364
38 19.7 43 170.75 68.5 25.58202355
39 6.3 54 155.25 69.25 22.75869619
40 29.9 37 241.25 71.5 33.17497188
41 21.4 40 168.5 69.25 24.70106479
42 9.4 31 151.25 72.25 20.36924846
43 25.2 55 198.5 74.25 25.31179358
44 16 28 183.75 67.75 28.14259065
45 31.5 54 202.5 70.75 28.43986066
46 29.8 56 178.75 68.5 26.78059566
47 10 28 182.5 72.25 24.57777086
48 12.5 55 126.5 66.75 19.9592083
49 3.7 27 133.25 64.75 22.34307777
50 18.2 44 179.75 69.5 26.16101651
51 38.1 42 244.25 76 29.72779605
52 11.3 50 162.5 66.5 25.83243824
53 5.3 25 143.75 72.5 19.22592152
54 28.7 43 200.5 71.5 27.57132378
55 11.8 61 143 65.75 23.25411673
56 20.5 35 177.25 71 24.71865701
57 24.9 40 176.75 71 24.64892878
58 21.2 30 205.25 71.25 28.42292398
59 20.5 41 202.25 72.5 27.05003567
60 27.9 52 206.5 74.5 26.15548849
61 24.9 46 192.5 71.75 26.28707402
62 12.3 23 154.25 67.75 23.62446045
63 26.1 50 157 66.75 24.77150753
64 13.6 45 135.75 68.5 20.33827055
65 27.2 49 216.25 74.5 27.39043286
66 17.5 40 170.5 74.25 21.74136426
67 32.3 57 205.5 70 29.48295918
68 20.8 32 180.5 69.5 26.27017235
69 22.1 35 187.75 69.5 27.32534548
70 6 44 184 74 23.62162162
71 21.8 35 166.25 68 25.27546496
72 33.6 72 201 69.75 29.04443674
73 12.5 30 136.5 68.75 20.3021752
74 20.4 41 210.5 72 28.54581405
75 27 70 170.75 70 24.49739796
76 18.4 64 190.25 72.75 25.27050933
77 14.1 48 176 73 23.21786451
78 3.7 27 159.25 71.5 21.89891926
79 18.5 61 148.25 67.5 22.87401921
80 19.2 35 217 73.75 28.04729675
81 34.5 45 262.75 68.75 39.0798281
82 6.1 22 173.25 72.25 23.33204823
83 20.9 24 210.25 74.75 26.45263476
84 13.9 43 164.25 73.25 21.52015749
85 20.4 49 212.75 75 26.58902222
86 22.3 49 196.75 73.75 25.42997989
87 21.5 54 151.5 70.75 21.27722908
88 13.5 55 125 64 21.45385743
89 14.6 33 196 73 25.85625821
90 5.2 55 142.25 67.25 22.11174528
91 32.8 47 195 72.5 26.0803805
92 11.9 32 182 73.75 23.52353921
93 9.6 38 188.75 73.25 24.73016575
94 10.2 47 158.25 72.25 21.31195747
95 22.2 47 197 72 26.71508488
96 17.3 28 171.5 75.25 21.29150893
97 19.5 49 168.25 71.75 22.97558548
98 12.4 54 153.25 70.5 21.67592173
99 16.9 39 234.75 74.5 29.73366065
100 28 43 183.25 70 26.2907653
101 29.4 43 187.75 74 24.10304054
102 15.2 68 155.5 69.25 22.79534465
103 25.8 40 191 74 24.52027027
104 22.5 38 187.25 69.25 27.4496996
105 10.9 55 179.75 68.75 26.73491571
106 27.3 34 218.75 72 29.66459298
107 25.3 44 185.25 71.5 25.47425302
108 31.6 48 217 70 31.13285715
109 25.3 22 154 66.25 24.66631541
110 9.6 47 160.5 70.25 22.86323628
111 24.7 42 224.75 74.75 28.2769544
112 15 53 154.5 69.25 22.6487508
113 25.3 36 226.75 71.75 30.96412485
114 17.4 43 152.25 67.75 23.31814654
115 18.3 52 203.25 74.25 25.91749141
116 35 65 224.5 68.25 33.88179098
117 17.4 53 224.5 77.75 26.10783594
118 8.5 56 160.75 73.75 20.77697213
119 24.6 61 179.75 65.75 29.23026211
120 8.3 46 176.75 72.5 23.63952438
121 27.2 42 177.5 68.75 26.40026446
122 22.6 54 198 72 26.85069444
123 20.3 35 224.75 72.25 30.26769316
124 8.8 29 160.75 69 23.73603234
125 16.5 35 172.75 69.5 25.14222866
126 14.8 55 169.5 68.25 25.5811295
127 26.1 62 216 73.25 28.30048108
128 13.9 51 179 72 24.27411266
129 29.6 25 206.5 69.75 29.839185
130 21.3 42 163 70.25 23.21936146
131 30 55 183.5 67.5 28.31286694
132 22.5 31 177.25 71.5 24.37415032
133 7.9 34 131.5 67.5 20.2896022
134 22.4 40 168.25 71.25 23.29918129
135 32 41 212 71.5 29.1527214
136 21.3 41 218.5 71 30.47123587
137 23.1 64 160 65.75 26.01859215
138 18.6 62 168.75 67.5 26.03703704
139 40.1 49 191.75 65 31.90538461
140 11 70 134.25 67 21.02422589
141 19.2 26 181 69.75 26.15444303
142 30.7 54 193.25 70.25 27.52847608
143 32.3 41 247.25 73.5 32.17488084
144 22 42 156.25 69 23.0715711
145 26.8 64 150.25 67.25 23.35528807
146 10.8 40 133.5 67.5 20.5981893
147 13.6 51 149.25 69.75 21.56657803
148 7.5 51 154.5 70 22.1660204
149 0.7 35 125.75 65.5 20.6053843
150 29.3 72 186.75 66 30.13894628
151 17.3 43 194 75.5 23.9256173
152 22.9 31 148 67.5 22.83544581
153 8.5 47 165.25 70.5 23.37322067
154 32.6 50 203 67 31.79082201
155 30.4 66 234.25 72 31.76654128
156 13.8 55 154.75 71.5 21.2801115
157 18.3 40 173.25 69.5 25.21499922
158 19.5 50 172.75 73 22.78912554
159 12.4 64 155.25 69.5 22.59525904
160 19.3 43 200.25 73.5 26.05872553
161 12.1 40 159.25 69.75 23.01157488
162 20.4 58 181.5 68 27.59396626
163 25.4 43 177 69.25 25.94711256
164 21.2 49 198.5 73.5 25.83099634
165 12.2 43 178.25 70.25 25.39172503
166 28.7 24 184.25 71.25 25.5148538
167 14.9 56 174.5 69.5 25.39692563
168 6.6 40 139.25 69 20.56138416
169 28.4 50 196.75 68.25 29.69372996
170 6.3 49 152.75 73.5 19.87750474
171 18.1 44 187.5 72.25 25.25113444
172 16.5 33 211.75 73.5 27.55523161
173 7.7 39 125.25 68 19.04211722
174 14.9 72 157.75 67.25 24.52110944
175 22.7 40 171.25 70.5 24.22187012
176 16.6 44 208.75 73 27.53823419
177 29 67 199.5 68.5 29.88939208
178 7.8 27 216 76 26.28947368
179 14.7 40 160.25 68.75 23.83460496
180 16.5 27 156.75 67.25 24.36566659
181 28 62 201.25 69.5 29.29015061
182 24.5 52 199.25 71.75 27.20882856
183 10.8 47 159.75 70.75 22.43589007
184 27 72 168 69.25 24.62776786
185 26.6 39 219.25 74.25 27.95773674
186 32.6 67 227.75 72.75 30.25155584
187 34.8 44 223 69.75 32.22342981
188 12.9 55 156.75 71.5 21.55513717
189 5.6 39 148.5 71.25 20.56421052
190 20.1 41 172.75 71.25 23.92233918
191 22.8 42 162.75 72.75 21.61774188
192 20.5 46 177 70 25.39408163
193 13 33 184.25 68.75 27.40421818
194 4.1 25 191 74 24.52027027
195 22.1 47 178.25 70 25.57341837
196 21.8 39 166.75 70.75 23.41899636
197 11.5 54 161.75 67.5 24.95698217
198 16.1 57 182.25 71.75 24.88737267
199 11.5 40 145.75 67.25 22.65579525
200 14.9 42 165.25 69.75 23.87857299
201 16 47 151.5 66.75 23.90371586
202 11.4 41 153 69.25 22.42886001
203 7.1 26 186.25 74.5 23.59060403
204 18.1 49 171.75 71.5 23.61782972
205 24.4 41 185 68.25 27.92040682
206 17.7 32 148.75 70 21.34107143
207 8 51 137.25 67.75 21.0207922
208 9.9 37 145.25 69.25 21.29275763
209 24.4 41 168.25 69.5 24.48729362
210 20.4 48 173.75 72 23.56216243
211 27.3 63 219.15 69.5 31.89533668
212 24.8 62 191.5 72.25 25.78982531
213 15.4 58 175.5 71.5 24.13350286
214 13.8 50 161 66.5 25.59398496
215 31.4 67 163.75 67.75 25.07945153
216 27.1 44 186 69.75 26.87694146
217 14.2 24 156 70.75 21.90922598
218 10.4 26 184.75 72.25 24.88078447
219 20.1 48 177.25 72.75 23.54374653
220 29 34 195.75 71 27.29860147
221 16.9 36 176.25 71.5 24.23663749
222 19.1 28 179 68 27.21388408
223 26 72 190.75 70.5 26.97998089
224 32.9 44 166 65.5 27.20074587
225 7.1 49 140.5 68 21.36061851
226 3 35 152.25 67.75 23.31814654
227 17 65 127.5 65.75 20.73356561
228 19.2 24 208.5 72.75 27.69461863
229 10.1 27 146 72.25 19.66221669
230 15.2 28 200.5 69.75 28.97218689
231 25.5 42 180 68.25 27.16580124
232 23.6 47 197 73.25 25.81108691
233 31.2 28 205.75 69 30.38064483
234 6.6 42 167.25 72.75 22.21546746
235 25.8 60 157.75 67.5 24.33980796
236 24.3 62 167.5 71.5 23.03340017
237 10.3 23 188.15 77.5 22.02196878
238 26.7 48 175.25 71.75 23.93147907
239 19.6 26 241.75 74.5 30.62028737
240 12.4 25 176 72.5 23.53921522
241 17.8 46 156.5 68.25 23.61915496
242 14 28 151.25 67.75 23.16498958
243 20.8 40 192.25 73.25 25.18873836
244 18.8 66 171.25 69.25 25.10419789
245 8.8 57 162.5 69.5 23.65043217
246 31.9 74 207.5 70 29.76989796
247 23.6 43 170.75 67.5 26.34562414
248 20.9 35 162.75 66 26.26566804
249 17 56 167.75 68.5 25.132559
250 17.7 42 168 71.5 23.10215658