Consider the set of ordered pairs shown below. Assuming that the regression equation is y = 4.293 +0.208x and the SSE = 5.167, construct a 95% prediction interval for x = 1. x 3 7 1 3 3 y 6 Click the icon to view a portion of the student's t-distribution table. 6 5 5 3 Calculate the upper and lower limits of the prediction interval. UPL = LPL =

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 5PPS
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1 Tail
0.2
0.1
0.05
0.025
0.01
0.005
2 Tail
0.4
0.2
0.1
0.05
0.02
0.01
Conf Lev
0.6
0.8
0.9
0.95
0.98
0.99
df
1
1.376
3.078
6.314
12.706
31.821
63.657
2
1.061
1.886
2.92
4.303
6.965
9.925
3
0.978
1.638
2.353
3.182
4.541
5.841
4
0.941
1.533
2.132
2.776
3.747
4.604
0.92
1.476
2.015
2.571
3.365
4.032
6
0.906
1.44
1.943
2.447
3.143
3.707
7
0.896
1.415
1.895
2.365
2.998
3.499
8.
0.889
1.397
1.86
2.306
2.896
3.355
0.883
1.383
1.833
2.262
2.821
3.25
10
0.879
1.372
1.812
2.228
2.764
3.169
11
0.876
1.363
1.796
2.201
2.718
3.106
12
0.873
1.356
1.782
2.179
2.681
3.055
13
0.87
1.35
1.771
2.16
2.65
3.012
14
0.868
1.345
1.761
2.145
2.624
2.977
15
0.866
1.341
1.753
2.131
2.602
2.947
16
0.865
1.337
1.746
2.12
2.583
2.921
17
0.863
1.333
1.74
2.11
2.567
2.898
18
0.862
1.33
1.734
2.101
2.552
2.878
19
0.861
1.328
1.729
2.093
2.539
2.861
20
0.86
1.325
1.725
2.086
2.528
2.845
21
0.859
1.323
1.721
2.08
2.518
2.831
22
0.858
1.321
1.717
2.074
2.508
2.819
23
0.858
1.319
1.714
2.069
2.5
2.807
24
0.857
1.318
1.711
2.064
2.492
2.797
25
0.856
1.316
1.708
2.06
2.485
2.787
26
0.856
1.315
1.706
2.056
2.479
2.779
Transcribed Image Text:1 Tail 0.2 0.1 0.05 0.025 0.01 0.005 2 Tail 0.4 0.2 0.1 0.05 0.02 0.01 Conf Lev 0.6 0.8 0.9 0.95 0.98 0.99 df 1 1.376 3.078 6.314 12.706 31.821 63.657 2 1.061 1.886 2.92 4.303 6.965 9.925 3 0.978 1.638 2.353 3.182 4.541 5.841 4 0.941 1.533 2.132 2.776 3.747 4.604 0.92 1.476 2.015 2.571 3.365 4.032 6 0.906 1.44 1.943 2.447 3.143 3.707 7 0.896 1.415 1.895 2.365 2.998 3.499 8. 0.889 1.397 1.86 2.306 2.896 3.355 0.883 1.383 1.833 2.262 2.821 3.25 10 0.879 1.372 1.812 2.228 2.764 3.169 11 0.876 1.363 1.796 2.201 2.718 3.106 12 0.873 1.356 1.782 2.179 2.681 3.055 13 0.87 1.35 1.771 2.16 2.65 3.012 14 0.868 1.345 1.761 2.145 2.624 2.977 15 0.866 1.341 1.753 2.131 2.602 2.947 16 0.865 1.337 1.746 2.12 2.583 2.921 17 0.863 1.333 1.74 2.11 2.567 2.898 18 0.862 1.33 1.734 2.101 2.552 2.878 19 0.861 1.328 1.729 2.093 2.539 2.861 20 0.86 1.325 1.725 2.086 2.528 2.845 21 0.859 1.323 1.721 2.08 2.518 2.831 22 0.858 1.321 1.717 2.074 2.508 2.819 23 0.858 1.319 1.714 2.069 2.5 2.807 24 0.857 1.318 1.711 2.064 2.492 2.797 25 0.856 1.316 1.708 2.06 2.485 2.787 26 0.856 1.315 1.706 2.056 2.479 2.779
Consider the set of ordered pairs shown below. Assuming that the regression equation is y = 4.293 +0.208x and the
SSE = 5.167, construct a 95% prediction interval for x = 1.
x 3 7 1 3 3
y
6
5
Click the icon to view a portion of the student's t-distribution table.
Calculate the upper and lower limits of the prediction interval.
UPL =
LPL =
(Round to three decimal places as needed.)
Transcribed Image Text:Consider the set of ordered pairs shown below. Assuming that the regression equation is y = 4.293 +0.208x and the SSE = 5.167, construct a 95% prediction interval for x = 1. x 3 7 1 3 3 y 6 5 Click the icon to view a portion of the student's t-distribution table. Calculate the upper and lower limits of the prediction interval. UPL = LPL = (Round to three decimal places as needed.)
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