Below, we visualize this data using a scatter plot, where each of the 600 patients is plotted as a data point, where the

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answerhappygod
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Below, we visualize this data using a scatter plot, where each of the 600 patients is plotted as a data point, where the

Post by answerhappygod »

Below We Visualize This Data Using A Scatter Plot Where Each Of The 600 Patients Is Plotted As A Data Point Where The 1
Below We Visualize This Data Using A Scatter Plot Where Each Of The 600 Patients Is Plotted As A Data Point Where The 1 (110.8 KiB) Viewed 32 times
dataset-one.csv:
exercise,cholesterol
113.8885,88.9410
114.8435,101.0020
109.7504,83.3392
106.1830,92.7467
100.7218,102.1816
106.1602,104.1231
105.8115,95.0488
104.3183,98.0411
109.8394,88.7099
99.1940,94.7666
84.2585,120.3633
103.4885,91.2636
110.9076,78.8085
99.6618,99.0146
116.0334,94.3566
102.4327,100.2414
86.7192,99.2744
98.0927,103.0129
112.9505,95.5516
96.1924,101.6700
87.5651,102.3939
94.8319,118.9622
94.8474,102.0550
93.8902,111.6074
87.8359,110.6598
95.0360,107.6998
92.2139,99.4378
101.3151,101.7137
101.5397,100.5991
94.2322,103.2029
93.9738,103.4838
88.1469,99.6465
99.8341,97.3249
90.1089,113.1634
93.7681,106.5992
105.6115,95.3006
103.6910,87.5775
100.4238,94.7338
91.7960,104.1109
97.9956,102.4016
94.9460,111.4238
97.8613,91.2758
117.2265,96.1794
107.6991,91.0286
96.1567,111.2994
101.4711,107.1729
104.9257,101.9802
105.0180,99.9782
106.3883,106.2398
102.3187,100.5238
108.5508,81.9191
94.4436,112.4110
98.5765,115.1670
94.4326,100.2821
118.8317,91.6372
116.4090,89.9977
100.6633,112.8429
100.9420,104.7322
106.1500,92.7539
107.6027,98.9183
98.7748,93.4513
106.7983,102.5806
94.5596,104.3823
103.4611,109.6151
106.1944,96.6868
96.4624,107.3507
95.3443,104.8808
97.8954,106.8877
103.3407,95.1864
98.9356,93.3353
91.0087,112.0983
103.4237,101.0666
120.1906,86.4879
97.4946,110.4039
89.0632,107.6729
105.5750,106.5401
91.8119,102.3443
96.9581,98.3504
104.1484,88.2153
90.3382,106.5659
103.3003,110.3435
107.0230,93.5961
94.3201,111.6504
87.4658,101.6110
109.5212,92.7199
107.6383,94.6154
103.7570,91.6396
95.0966,109.7230
91.8811,103.2430
96.8407,103.2829
92.6357,97.6419
87.5880,96.6902
98.7569,89.9148
92.3753,107.9936
100.2259,110.6849
91.8023,110.0860
95.5924,96.1479
103.0936,95.6934
108.3689,97.4541
120.0217,89.4290
121.9652,105.6688
99.0641,110.5668
111.6488,104.2798
109.8748,107.8672
117.3462,107.5664
99.9783,110.2398
100.9650,123.3511
99.8016,116.6146
105.5976,114.0307
96.9759,124.3590
114.0171,106.6082
108.1461,112.5427
103.0191,97.3757
125.2096,97.5488
104.0045,113.2310
113.0808,106.0982
102.9400,131.6582
112.8252,114.3885
110.5384,120.3254
114.1773,110.1278
106.8910,121.6731
119.4805,109.7250
103.8581,112.7406
122.5434,89.9611
113.0582,114.9808
112.7521,111.3700
108.4838,114.1329
106.6238,125.1009
106.7597,108.4308
113.0831,103.6591
95.8040,114.6733
114.3415,104.5301
112.8650,114.7558
99.4488,115.3845
107.6121,113.0551
108.6875,109.2839
109.1849,110.0564
102.0373,112.2140
108.7913,104.9090
100.7121,117.0796
116.8638,119.0936
108.3519,118.3179
111.0799,100.5130
111.2821,100.3466
93.3405,130.9469
99.1783,124.0103
111.0307,120.7041
115.9525,100.8415
100.8438,110.5713
105.8910,112.1266
107.9753,113.1328
115.3270,107.1455
99.5133,110.3200
117.2111,97.9137
103.5449,112.7697
95.2776,121.3426
108.8016,115.5850
114.9522,105.0745
117.7911,104.6128
110.4581,110.6799
107.2559,109.9521
104.1187,107.9050
108.4718,104.5058
115.0693,103.1668
112.0960,111.3960
108.9959,99.8981
111.6489,114.5749
99.3618,123.3767
90.4356,122.3341
104.4158,103.8695
97.8277,111.7649
122.2347,98.9266
113.2241,105.2014
101.7858,106.7840
105.2441,109.3713
120.1008,107.9680
123.3845,102.6439
101.8216,110.2878
116.6808,105.8386
98.7514,123.7623
115.9679,104.4488
105.8360,108.4531
102.6009,123.0580
106.9888,111.3603
124.6723,100.1679
113.1244,107.8801
126.8277,105.3317
118.6311,105.4859
95.6263,125.2485
109.4122,111.2522
106.9821,93.6454
106.9286,114.9799
101.6894,108.2916
117.1234,104.8344
104.2463,117.9798
115.8282,104.2092
115.9375,104.6158
103.1061,108.6955
110.0793,111.2946
115.7861,103.4000
96.7230,124.7321
123.7083,116.3577
95.5456,136.4675
103.3890,126.7358
118.8703,119.6472
133.4777,117.0873
123.9516,120.3672
107.5832,124.3785
125.5024,118.6427
123.3835,109.5309
109.9525,138.9431
114.1734,126.2769
119.4373,120.8033
124.8228,120.1603
125.4914,111.6276
128.6385,117.9320
113.3837,133.6266
108.6736,133.3683
116.2002,115.5630
111.3121,119.7366
125.2657,107.8856
105.4170,130.0463
116.5074,118.3710
110.9393,126.5018
113.9541,124.1564
125.5576,120.2826
119.1365,137.0104
113.4621,131.8622
112.8990,128.1599
115.4228,114.4745
108.2543,127.9014
132.6326,111.0947
136.0664,117.0496
120.4209,113.0946
121.4959,120.5596
125.3073,110.4930
122.0577,126.2351
124.2943,114.7868
113.3542,125.5352
121.6334,120.8521
115.6047,134.3094
132.1172,120.9350
122.7038,118.6781
125.4886,112.9999
119.7738,117.1505
116.1147,112.9552
128.1568,117.6565
125.3696,127.3324
113.9769,121.9041
107.7577,142.0105
120.1020,117.2736
126.0307,112.2745
106.1014,130.1166
123.1808,120.3422
131.4685,115.8732
134.7101,121.8626
132.3968,100.5416
116.6120,133.8455
123.4333,130.6540
118.4233,120.1973
116.2977,119.7005
113.5379,135.8405
130.6268,117.6297
117.5251,120.7494
118.7326,120.0500
114.3212,123.9126
133.6397,111.6124
113.4554,119.7311
130.6618,118.0869
125.2697,122.7874
117.2960,116.8154
128.5051,110.2797
130.8055,123.1851
126.9904,114.4177
113.4435,138.6683
118.0292,131.6517
120.2258,113.5491
124.5583,124.0526
118.8455,114.5093
120.0192,110.7021
116.5439,125.7116
135.5421,99.6203
112.2164,123.2412
131.4843,113.3180
126.4330,107.0208
130.3142,112.2368
122.1093,113.6854
109.8629,122.7032
115.7920,114.4834
108.1900,127.2321
132.2824,116.3854
118.1180,119.7534
110.7686,129.1449
116.3395,117.6980
123.6871,113.9516
124.1743,129.1116
117.9759,110.7225
121.6160,112.8243
123.0921,120.4846
116.3945,127.2430
135.9791,124.8038
137.5441,122.3002
128.6276,135.2185
119.6299,135.2526
142.3927,117.9612
126.9549,126.4669
142.2804,129.7083
128.9272,129.1199
134.0896,133.3068
132.6992,131.2618
125.6364,130.1729
127.1027,132.6468
118.5758,122.9762
128.3882,121.1585
127.0303,138.4374
145.3058,123.9218
134.0135,134.4807
125.5705,126.7528
118.2912,136.2307
124.3965,132.9199
122.1774,138.4074
132.0014,117.5401
140.3548,113.4699
133.5644,134.7221
128.7651,128.9197
133.2260,122.8746
120.9291,131.5659
138.0050,128.4572
144.3490,119.2111
128.9836,128.8858
129.9974,131.3707
130.7338,122.5775
125.9698,130.3723
131.0641,130.2768
114.3980,149.1186
127.7941,134.9361
127.1350,133.7114
134.5946,127.2018
115.9038,146.6915
130.6006,125.8931
132.0162,130.6852
118.9026,144.3698
129.9160,131.9384
136.0167,134.5946
127.0269,127.6976
119.2022,138.7657
113.1972,139.6875
124.1967,127.1971
125.0654,122.8099
136.1936,133.1032
129.0253,125.0344
124.0329,137.8918
133.1486,116.8930
130.5144,129.1524
127.9148,140.5851
128.9152,132.3716
118.7083,137.6362
126.4069,135.7525
116.6885,138.3241
114.8338,145.8209
123.2201,141.2673
133.6257,121.8318
130.1709,131.5854
141.8670,123.1325
124.1401,127.6653
129.3944,128.6647
138.6538,119.2524
130.0835,133.1667
119.3176,123.5361
131.9499,128.3757
129.3434,141.6491
130.5062,124.6444
133.5444,130.4511
119.3407,138.2433
136.8228,116.7466
134.9083,126.5516
132.6065,129.3076
129.8614,133.3178
114.9520,134.4547
145.9233,118.9273
115.5325,138.4393
135.9545,130.4854
127.3402,118.3815
135.2767,128.3080
129.2250,127.2001
120.9243,139.1434
131.0532,125.1466
132.3633,127.9462
122.4847,126.1902
127.0288,137.5325
121.7635,140.1799
140.0079,114.0528
123.8267,134.8635
128.2293,130.0130
131.0989,125.9773
147.6605,125.8761
133.4425,126.6445
136.5558,119.3838
118.9651,148.9881
132.6278,131.0431
152.0320,132.3446
143.8548,124.4666
151.5858,126.7125
147.3713,133.0730
144.3070,139.1165
146.6427,137.9485
153.4626,130.1232
149.1392,129.4661
145.6174,137.1856
145.8537,133.8650
120.9035,146.2295
132.8810,163.2643
149.3597,143.8002
155.9337,127.6475
135.0115,145.2503
138.8495,147.3334
144.5539,140.7778
138.3995,149.2022
139.5880,133.4953
127.7106,139.0496
131.8272,148.3177
131.3069,146.4542
129.1963,144.3992
146.6226,144.3335
141.3536,148.8400
139.9689,137.7113
132.7139,148.9081
154.6847,133.7778
141.9025,142.7911
146.4431,133.7509
131.0393,142.7084
140.9219,143.5583
139.2588,149.0124
131.4553,141.2927
129.3140,150.9569
138.3285,142.9114
160.2695,130.1906
137.9255,141.3763
134.8571,145.2901
135.6117,142.3900
131.4518,144.1015
138.7289,148.2532
141.9557,138.5661
147.7029,137.9119
133.3441,156.0508
132.8998,149.1007
137.6934,139.1556
137.8791,140.7107
130.4022,149.0167
137.6272,132.8679
151.2630,126.2779
125.2787,152.1454
145.9402,150.0222
139.8796,141.6097
139.8209,150.7270
137.6801,144.7815
137.4643,151.7826
132.7361,138.7044
144.2955,145.1325
142.2309,140.6400
149.4398,132.8300
130.6592,136.8902
149.3747,135.9615
136.8511,136.8057
135.3435,140.4009
148.0690,137.5726
133.7549,144.9526
142.9054,140.1492
147.0162,137.5160
141.1059,147.0185
142.2483,142.9796
135.2421,134.1497
134.1570,153.5917
135.8510,137.3441
150.7861,128.7179
139.6168,136.6042
140.0882,133.8349
145.2051,131.7037
128.1418,150.0625
134.5681,147.8618
132.0401,139.1844
137.1231,142.8564
114.0073,152.1131
125.0870,150.6329
141.9224,140.3044
145.8119,144.6748
125.1723,144.6867
136.4417,131.4042
148.0390,136.8954
148.0981,127.4421
125.5276,147.7954
142.7998,132.8828
140.4637,128.4538
150.0751,123.9918
133.9333,139.6030
158.0775,125.7838
130.3402,143.3733
138.6472,139.6517
135.3300,135.2257
140.7572,141.4989
146.5270,155.8042
141.9602,169.3408
154.4848,147.5052
155.4624,147.5794
133.2094,170.1854
141.0022,151.5916
150.0668,151.6939
149.2228,152.3310
142.5680,151.8323
149.4637,152.5737
155.9360,147.9302
152.0880,142.0983
142.4174,151.2788
146.0989,151.2690
142.8202,156.8483
138.3621,148.8728
148.4492,149.4405
146.6700,146.8743
154.5609,148.5259
148.6357,146.0911
148.7289,155.7604
153.8727,148.0716
149.5606,151.0008
150.0637,139.6504
157.6634,136.2431
141.5556,155.4836
153.8043,143.7441
148.2321,157.9557
146.5050,155.2378
155.8020,152.5456
164.8405,132.0377
141.3895,173.8448
147.1335,156.7385
143.8208,147.4426
145.5679,152.8909
155.1764,148.8411
152.8730,147.3788
139.6597,150.6076
152.4245,142.9977
150.5875,148.1235
147.2857,153.9319
157.5062,150.7350
153.0245,149.6901
159.3520,142.2050
146.4519,141.2483
142.9484,142.5299
148.8187,148.0259
155.1753,149.7677
153.7026,147.1773
156.7133,160.6591
149.7630,149.3956
141.8302,155.1247
157.8399,147.9300
143.9349,156.9753
135.4124,163.3504
136.8649,163.9836
150.4812,149.8503
150.4598,137.9384
155.5038,143.4693
150.8690,142.5700
151.0389,142.0350
155.0275,155.7578
142.5475,160.9687
142.7003,156.8725
138.2622,154.8542
154.9507,144.3681
143.0448,148.3620
154.3902,146.6160
146.7660,147.3105
139.0194,159.3880
138.4043,147.1097
157.9274,142.4678
142.6599,165.5403
142.8222,148.0231
145.8269,154.3875
148.3646,144.1032
152.4188,144.2524
143.9338,153.8868
149.2442,155.0134
159.1537,144.6468
153.8344,146.9697
146.7498,155.6508
163.8080,139.4521
157.7198,146.1635
156.5214,155.3351
158.8618,128.0527
153.9445,147.5923
157.1482,144.7260
147.8328,153.4848
136.9801,163.0705
138.8375,151.6272
149.0599,138.5208
145.1528,161.2291
142.0059,160.1883
143.9840,156.5905
145.7357,153.8107
144.8442,148.7862
162.6677,132.9471
148.9883,146.7779
160.2445,150.2613
Below We Visualize This Data Using A Scatter Plot Where Each Of The 600 Patients Is Plotted As A Data Point Where The 2
Below We Visualize This Data Using A Scatter Plot Where Each Of The 600 Patients Is Plotted As A Data Point Where The 2 (61.08 KiB) Viewed 32 times
Below, we visualize this data using a scatter plot, where each of the 600 patients is plotted as a data point, where the x-axis represents exercise level and the y-axis represents cholesterol level. cholesterol 160 140- 120 100- 80- 90 100 110 120 130 140 150 160 exercise Here, we observe that there appears to be a positive correlation between exercise and cholesterol: the more exercise that one performs, the higher your cholesterol level. (Most people would consider this unexpected and counter-intuitive).
1. Suppose that we learn a linear model y = ax + b to predict a patient's cholesterol level (y) given their exercise level (x). What is the resulting co-efficient a based on the dataset dataset-one.csv? Does the co-efficient suggest positive correlation or negative correlation between x and y? (Note that if you use the linear regression module in the python scikit-learn module, this coefficient appears in the coef_ member variable).
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