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
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
![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.
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![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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F1aa0e339-425e-4015-8fd4-734198a049c6%2F21042593-da48-4179-a57d-56ac8939daef%2Fwxp5qde_processed.jpeg&w=3840&q=75)
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