Lated below are the overhead widthe in om) of seale mesured om pholographe and the weights (in kg) of the seals. Construct a scatlerplot, find the value of the lineer correlation ooefficientr, and find the orfical velus of ruing a0.05. le Perefcient evidence to conclude that there is a linear comelafion between overhead widthe of seals from photographs and the weights of the seals? 9.4 203 8.2 0 184 87 Overhead Widh Weight 7.0 111 7.8 208 245 201 CKNeR A er al e commlaliun coeicont Construt a sotterplot Choose the corect graph below OB. OA Oc. OD. Awlgh ) 300 Awolght g) 300 200 300 L 100 100 10 with (om) 10 width (om) 100 width tom The linear comelation coefficient is r Round to three decimal places as needed.) e critical values arer ound to three decimal places as needed. Use a comma to separate answers as needed.) se the absolute value of the linear correlation ooefficient is V than the positive critical value, there tthe weights of the seals for a significance level of a 0.05. V sufficient evidence to support the claim that there is a linear correlation between overhead widths of seals from photographs

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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6 For the last part Fill in the blank Because the absolute value of the linear correlation coefficient is greater or less than the positive critical value there is not or is sufficient evidence to support the claim that there is a linear correlation between overhead width of feels from photographs and the weights of the seats or a significance Level of a equals 0.05
Liaed be e to conolude that there is a linear comrelation between overhead widthe of seals from photographs and the weights of the snale?
8.2
184
8.7
201
7.8
9.4
Overhead Width 7.0
Weight
208
245
203
CKNER Ra kaies A abie ler aes he commlahon coefcen
Construct a soaterplot. Choose the corect graph below
OB.
Oc.
OA
OD.
Awolht ka)
100
10
width (om)
width (om)
The lintar comelation coefficient is r=
(Round to three decimal places as needed.)
The critical values arer
(Round to three decimal places as needed. Use a comma to separate answers as needed.)
Because the absolute value of the linear correlation coefficient is
and the weights of the seals for a significance level of a0.05.
V than the positive critical value, there
V sufficient evidence to support the claim that there is a linear correlation between overhead widths of seals from photographs
ck to select vour answeris.
Exit Honorloci
MacBook Air
80
B99
F6
F7
&
*
5
9.
U
0 P
* CO
Transcribed Image Text:Liaed be e to conolude that there is a linear comrelation between overhead widthe of seals from photographs and the weights of the snale? 8.2 184 8.7 201 7.8 9.4 Overhead Width 7.0 Weight 208 245 203 CKNER Ra kaies A abie ler aes he commlahon coefcen Construct a soaterplot. Choose the corect graph below OB. Oc. OA OD. Awolht ka) 100 10 width (om) width (om) The lintar comelation coefficient is r= (Round to three decimal places as needed.) The critical values arer (Round to three decimal places as needed. Use a comma to separate answers as needed.) Because the absolute value of the linear correlation coefficient is and the weights of the seals for a significance level of a0.05. V than the positive critical value, there V sufficient evidence to support the claim that there is a linear correlation between overhead widths of seals from photographs ck to select vour answeris. Exit Honorloci MacBook Air 80 B99 F6 F7 & * 5 9. U 0 P * CO
t Distribution: Critical t Values
Area in One Tail
0.025
0.01
0.05
0.005
0.10
Area in Two Tails
Degrees of
Freedom
0.01
0.02
0.05
0.10
0.20
31.821
12.706
63.657
9.925
5.841
4.604
4,032
3.707
6.314
1
6.965
4.303
3.078
2.920
2.353
1.886
1.638
3.182
4.541
3.747
2.776
2.132
4.
3.365
2,571
1.533
2.015
1.943
3.143
2.447
1.476
1,440
2.998
2.365
3.499
3.355
3.250
1.895
1.415
2.306
2.262
2.896
1.860
8.
1,397
2.821
1.833
9
1.383
1.372
1.363
3.169
2.764
2.228
1,812
10
3.106
2.718
2.201
1.796
11
12
3.055
2.681
2.179
1.782
1.356
1.350
2.650
3.012
2.977
2.947
13
2.160
1.771
14
2.624
2.145
1.761
1.345
15
2.602
2.131
1.753
1.341
16
2.921
2.583
2.120
1.746
1.337
1.333
17
2.898
2.567
2.110
1.740
2,552
2.101
2,093
18
2.878
1.734
1.330
2.861
2.845
19
2.539
1.729
1.328
20
2.528
2.086
1.725
1.325
21
2.831
2.518
2.080
1.721
1.323
22
2.819
2.508
2074
1.717
1.321
2.807
2797
23
2.500
2.069
1.714
1.319
24
2.492
2.064
1.711
1.318
25
2787
2.485
2.060
1.708
1.316
26
2.779
2.479
2.056
1.706
1.315
27
2.771
2.473
2052
1.703
1.314
28
2.763
2.467
2.048
1.701
1.313
29
2.756
2.462
2.045
1.699
1.311
30
2.750
2.457
2.042
1.697
1.310
31
2744
2.453
2.040
1.696
1.309
32
2.738
2.449
2.037
1.694
1.309
33
2733
2.035
2.032
2.445
1.692
1.308
34
2.728
2.441
1.691
1.307
35
2.724
2.438
2.030
1.690
1.306
36
2,719
2.434
2.028
1.688
1.306
37
2,715
2.431
2.026
1.687
1.305
38
2.712
2.708
2.429
2.024
1.686
1.304
39
1.304
2.426
2.023
1.685
40
2.704
1.303
1.684
1.679
2.423
2.021
45
2.690
1.301
2.412
2.014
50
2,678
2.403
2.009
1.676
1.299
60
2.660
1.671
1.296
2000
1.994
2.390
70
2.648
1.667
1.294
2.381
80
2,639
1.292
1.664
1.662
2.374
1.990
90
1.291
2,632
2.368
1.987
100
200
2626
1.290
2.364
1.984
1.660
1.286
2,601
2.592
2.588
2.586
2.581
2.578
2576
1.653
1.650
1.649
1.648
1.972
1.968
300
2.345
1.284
2.339
2.336
400
1.284
1.283
500
1,966
2.334
1.965
1.962
1.961
1000
1.282
1.282
2.330
1.646
2000
Large
1.646
2.328
1.282
2.326
1.960
1.645
Transcribed Image Text:t Distribution: Critical t Values Area in One Tail 0.025 0.01 0.05 0.005 0.10 Area in Two Tails Degrees of Freedom 0.01 0.02 0.05 0.10 0.20 31.821 12.706 63.657 9.925 5.841 4.604 4,032 3.707 6.314 1 6.965 4.303 3.078 2.920 2.353 1.886 1.638 3.182 4.541 3.747 2.776 2.132 4. 3.365 2,571 1.533 2.015 1.943 3.143 2.447 1.476 1,440 2.998 2.365 3.499 3.355 3.250 1.895 1.415 2.306 2.262 2.896 1.860 8. 1,397 2.821 1.833 9 1.383 1.372 1.363 3.169 2.764 2.228 1,812 10 3.106 2.718 2.201 1.796 11 12 3.055 2.681 2.179 1.782 1.356 1.350 2.650 3.012 2.977 2.947 13 2.160 1.771 14 2.624 2.145 1.761 1.345 15 2.602 2.131 1.753 1.341 16 2.921 2.583 2.120 1.746 1.337 1.333 17 2.898 2.567 2.110 1.740 2,552 2.101 2,093 18 2.878 1.734 1.330 2.861 2.845 19 2.539 1.729 1.328 20 2.528 2.086 1.725 1.325 21 2.831 2.518 2.080 1.721 1.323 22 2.819 2.508 2074 1.717 1.321 2.807 2797 23 2.500 2.069 1.714 1.319 24 2.492 2.064 1.711 1.318 25 2787 2.485 2.060 1.708 1.316 26 2.779 2.479 2.056 1.706 1.315 27 2.771 2.473 2052 1.703 1.314 28 2.763 2.467 2.048 1.701 1.313 29 2.756 2.462 2.045 1.699 1.311 30 2.750 2.457 2.042 1.697 1.310 31 2744 2.453 2.040 1.696 1.309 32 2.738 2.449 2.037 1.694 1.309 33 2733 2.035 2.032 2.445 1.692 1.308 34 2.728 2.441 1.691 1.307 35 2.724 2.438 2.030 1.690 1.306 36 2,719 2.434 2.028 1.688 1.306 37 2,715 2.431 2.026 1.687 1.305 38 2.712 2.708 2.429 2.024 1.686 1.304 39 1.304 2.426 2.023 1.685 40 2.704 1.303 1.684 1.679 2.423 2.021 45 2.690 1.301 2.412 2.014 50 2,678 2.403 2.009 1.676 1.299 60 2.660 1.671 1.296 2000 1.994 2.390 70 2.648 1.667 1.294 2.381 80 2,639 1.292 1.664 1.662 2.374 1.990 90 1.291 2,632 2.368 1.987 100 200 2626 1.290 2.364 1.984 1.660 1.286 2,601 2.592 2.588 2.586 2.581 2.578 2576 1.653 1.650 1.649 1.648 1.972 1.968 300 2.345 1.284 2.339 2.336 400 1.284 1.283 500 1,966 2.334 1.965 1.962 1.961 1000 1.282 1.282 2.330 1.646 2000 Large 1.646 2.328 1.282 2.326 1.960 1.645
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