FEV1 data 2.259 2.531 2.887 2.578 3.329 3.847 2.813 2.278 2.65 2.561 2.017 3.058 1.72 2.631 1.649 1.744 2.85 3.774 3.078

[answerhappygod]

FEV1 data
2.259
2.531
2.887
2.578
3.329
3.847
2.813
2.278
2.65
2.561
2.017
3.058
1.72
2.631
1.649
1.744
2.85
3.774
3.078
2.304
3
1.415
2.175
3.074
2.957
1.253
2.57
3.141
3.193
2.276
2.913
4.683
5.638
3.011
3.001
1.004
1.429
3.122
3.745
2.335
2.341
3.048
2.55
1.773
2.827
2.639
2.356
3.78
3.369
2.201
2.358
2.481
4.279
2.556
2.336
2.123
1.536
2.258
3.166
2.081
2.262
2.32
2.822
2.851
1.589
2.056
2.903
1.092
1.527
2.115
2.457
2.434
1.886
2.365
2.048
3.016
3.135
2.119
1.754
4.309
3.395
1.844
2.841
3.835
2.717
1.523
3.004
3.029
2.501
1.858
3.406
2.332
3.33
3.038
3.111
1.634
1.572
2.868
1.612
2.822
2.435
2.891
3.984
1.282
1.102
2.241
2.795
2.246
2.076
3.751
1.969
3.022
3.456
4.842
2.328
2.993
2.387
1.547
2.463
1.672
1.423
1.577
4.13
4.404
2.341
3.436
4.5
1.682
2.487
1.897
2.069
1.481
4.506
2.354
1.495
4.448
3.32
3.09
2.158
3.111
2.358
1.698
2.563
1.461
2.997
2.144
2.642
2.608
1.776
2.382
3.082
2.303
5.224
3.806
1.536
2.562
2.871
2.545
2.084
3.147
3.501
2.015
1.072
1.75
3.222
3.96
2.964
4.504
2.111
2.819
3.236
2.659
3.5
2.419
2.838
3.078
3.297
1.694
4.324
2.665
2.635
3.279
3.069
3.05
2.118
1.94
1.953
2.094
1.609
2.352
2.288
2.135
2.714
2.287
3.331
1.808
3.795
2.264
1.348
1.703
1.535
2.579
2.292
1.759
1.512
1.895
3.47
1.855
3.255
3.556
3.345
2.887
3.251
1.4
2.069
1.858
1.987
1.742
1.829
2.759
3.727
1.697
2.458
4.07
2.988
1.665
2.463
3.132
1.979
1.657
3.206
2.732
3.004
3.127
2.866
2.293
2.076
4.637
2.352
1.919
3.231
2.677
2.529
2.17
3.208
1.473
1.78
2.673
1.165
3.354
1.826
1.953
2.673
1.796
3.791
3.587
2.211
1.658
2.797
1.602
2.659
3.056
2.09
2.069
2.606
3.498
4.073
2.071
3.583
2.56
1.256
2.975
1.691
2.894
2.175
3.681
2.25
4.225
2.364
3.007
3.688
4.271
2.971
1.912
2.535
2.571
2.928
2.633
1.523
1.735
3.515
1.603
3.2
2.569
4.724
3.681
1.631
4.393
2.504
3.985
2.366
1.872
4.086
1.415
3.519
2.13
2.578
2.884
3.924
1.796
1.611
2.004
1.794
1.562
3.11
3.114
2.362
3.079
4.756
1.335
2.592
2.976
1.716
2.457
2.981
4.593
0.839
3.086
1.931
2.348
2.42
3.035
2.1
1.552
1.755
2.455
2.864
3.654
1.789
3.577
2.704
2.417
2.599
2.574
1.917
1.935
2.46
3.169
1.343
2.347
2.538
2.216
2.72
1.747
2.219
2.305
2.798
3.28
2.135
2.25
2.889
1.708
1.591
1.569
3.211
1.698
1.431
2.14
3.06
1.338
2.751
3.785
1.718
2.709
4.08
4.877
2.386
2.166
3.305
3.35
1.823
3.799
1.942
2.002
2.604
2.564
2.123
3.977
2.725
1.37
2.095
3.411
2.164
2.542
2.688
2.145
1.452
3.906
1.937
0.791
1.789
2.503
3.223
2.165
1.933
1.69
1.827
2.384
3.082
1.418
2.1
2.812
3.082
3.49
3.741
2.974
3.247
2.833
2.417
2.906
2.631
4.591
1.404
3.489
1.146
2.927
2.435
1.878
3.258
4.27
4.763
3.023
2.754
3.171
3.102
3.731
4.045
4.305
3.108
2.631
2.689
3.297
3.089
3.223
1.292
2.485
1.858
2.688
4.872
2.354
2.608
2.333
2.182
2.691
2.301
2.866
2.681
2.646
3.12
1.999
3.549
4.831
3.53
2.196
1.606
2.993
2.499
2.592
1.624
2.98
2.935
1.715
2.973
1.556
2.102
2.04
3.169
1.612
2.696
2.226
3.515
1.932
3.239
3.957
2.855
1.826
1.991
4.789
3.692
2.605
2.568
1.359
1.624
0.796
2.531
3.341
2.5
2.481
3.428
1.726
2.387
2.581
2.752
1.427
2.056
2.665
3.994
1.344
3.343
2.126
3.104
1.666
4.72
2.42
2.893
2.042
3.585
4.065
2.318
1.947
4.073
2.891
1.877
2.923
2.135
5.083
3.339
1.697
2.498
2.491
2.216
3.222
1.962
2.093
1.751
2.016
2.862
3.058
1.338
2.853
4.007
1.713
2.458
3.082
3.68
2.391
3.203
2.01
2.988
2.22
2.187
3.845
3.425
1.869
1.389
4.299
1.675
1.458
1.811
5.102
1.79
2.246
2.09
2.524
3.042
3.387
2.371
2.346
4.22
1.92
2.752
3.596
2.715
2.646
2.23
3.645
1.724
4.203
1.905
2.193
2.588
2.953
2.198
2.785
2.104
3.816
2.449
3.529
4.336
3.887
3.186
2.215
3.086
5.793
1.64
2.77
2.236
1.343
1.558
2.52
Age data
9
8
15
11
10
11
10
15
9
10
5
11
7
8
7
8
9
11
13
12
9
7
10
14
11
7
9
13
13
14
12
14
17
11
12
4
8
15
13
8
8
10
7
9
11
8
10
14
11
10
10
10
15
12
8
11
5
8
10
11
6
9
11
9
5
7
16
5
6
5
9
13
9
10
9
9
9
9
9
14
14
8
12
12
9
6
15
9
11
5
17
12
15
10
11
6
6
12
7
12
8
14
13
5
4
12
16
10
9
12
9
11
10
14
8
11
10
8
9
6
6
4
11
18
10
14
15
7
9
8
8
6
15
11
7
13
11
10
7
10
8
8
11
7
14
8
10
10
5
8
12
8
12
14
6
11
9
10
7
13
12
8
3
7
12
17
9
16
7
13
11
11
17
7
10
11
13
11
11
8
15
12
11
10
8
8
8
10
7
9
8
8
12
10
13
5
10
15
6
8
6
12
10
8
8
9
11
9
13
9
19
11
10
5
9
10
8
7
7
12
15
8
8
16
11
10
11
10
6
8
11
8
9
10
11
8
9
11
10
6
12
13
9
11
13
7
8
10
7
10
7
9
8
7
12
11
8
7
9
6
9
13
8
8
11
10
11
8
11
9
5
10
6
11
8
9
10
13
10
10
16
14
12
9
7
9
11
11
8
8
11
7
10
12
17
11
7
12
10
15
7
8
18
6
19
9
7
11
12
6
7
8
8
8
10
9
11
12
13
8
9
13
9
9
16
11
4
13
8
9
8
12
10
5
5
9
10
11
4
13
13
12
13
9
7
7
9
11
5
12
14
10
10
6
7
8
9
11
7
10
12
9
9
4
15
7
6
11
13
6
12
13
6
8
12
13
11
9
10
10
10
15
9
7
9
7
8
11
9
7
7
11
9
11
10
8
5
13
10
5
7
8
9
9
9
7
7
12
18
4
9
11
12
11
14
11
11
9
11
18
9
10
3
10
5
8
10
6
11
16
14
11
11
11
11
15
13
13
11
8
11
13
13
11
8
9
10
9
16
8
16
8
9
10
9
12
8
13
11
8
13
12
12
9
9
8
12
10
6
8
12
6
9
8
6
9
14
5
10
8
11
7
9
14
10
6
8
13
12
12
10
5
8
4
13
12
8
8
14
7
11
10
12
6
9
11
13
8
12
9
11
6
12
9
9
9
14
11
11
9
12
10
7
9
9
13
11
6
11
11
13
11
8
7
9
8
10
12
6
18
11
6
11
17
14
10
10
8
9
7
8
11
11
9
4
16
8
10
10
19
7
9
9
11
9
16
7
11
18
7
12
11
9
10
9
16
8
12
7
8
9
11
15
11
6
13
13
12
13
13
12
8
10
15
7
10
14
6
9
10
Section 5 – Correlation/Regression Analysis.
Construct a linear regression model with Age as the predictor
variable and FEV1 as the response variable. State the equation in
correct algebraic format as shown in the course notes.
Create a scatter plot of the data with a plot of the least
squares line included. (StatCrunch should have generated this plot
when you calculated the model in 5a.) The plot must include an
informative title and correct labels for both axes.
Use the coefficient of determination to identify the percentage
of the variation in FEV1 explained by the variation in Age.
Identify all likely influential points (Cook's Distance greater
than 1.0). If any exist, list them as ordered pairs in the form
(Age, FEV1). If none exist, say so.
Conduct a formal hypothesis test at 𝛼𝛼 = 0.05 to determine if
there is sufficient evidence of correlation between FEV1 and Age.
Include the following:
1) The p-value and its logical relationship to 𝛼𝛼 (≤ or
>).
2) Your decision regarding the null hypothesis: reject or
fail to reject.
3) A statement regarding the sufficiency of the evidence
for a linear relationship
between FEV1 and Age.
State whether the equation in 5a satisfies the following LINE
criteria:
Linear Relationship (L): Using the scatterplot with fitted line,
determine if a linear model is appropriate based on the model's
visual fit to the data.
Independent Residuals (I): Include a statement that the residuals
are assumed to be independent.
Normally-Distributed Residuals (N): Determine if the residuals
fit a normal distribution using a residual histogram and a Q-Q
plot. (Do not use a boxplot.) Verify your assessment by conducting
a Shapiro-Wilk goodness-of-fit test for normality on the model
residuals. Use 𝛼𝛼 = 0.05. Report the p-value, its logical
relationship to 𝛼𝛼 (≤ or >), and your interpretation of the
result.
Equal Variances of the Residuals (E): Assess the residuals for
constant variance
using a plot of the residuals versus Age.
Using the results from 5e and 5f, clearly state whether the
model you built in 5a
provides valid estimates of FEV1 as a function of Age. Justify
your claim.
Provide a valid estimate of 𝑦𝑦𝑛𝑛𝑛𝑛𝑛𝑛, a new observation of FEV1,
for an individual
whose Age = 13.5. Use either the regression model you
constructed in 5a or
calculate the value using the FEV1 data column by itself,
whichever is appropriate.
If you use the regression model from 5a to calculate the
estimate in 5h, calculate a
95% prediction interval estimate of 𝑦𝑦𝑛𝑛𝑛𝑛𝑛𝑛. If the model in 5a
is not valid, include a statement that a prediction interval
estimate is not applicable.