Based on a sample of n = 15, the least-squares method was used to develop the prediction line Y; = 6+ 2X;. In addition, Syx = 1.5, X = 3 and E (X; - X)² = 15. i= 1 Complete parts (a) and (b) below. Click here to view page 1 of the table of the critical values of t. Click here to view page 2 of the table of the critical values of t.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Related questions
Question
Lbpertal areas
Degrees of
freedom
0.25
0.10
0.05
0.025
0.01
0.005
40
50
0.6795
0.6794
1.2901
1.2987
1.6766
1.6759
2.0096
2.0086
24049
2.4033
2.6800
Upper-tail areas
2.6778
Degrees of
freedom
51
52
53
54
55
0.6793
0.6792
0.6791
0.6791
0.6790
1.2984
1.2980
1.2977
1.2974
1.2971
1.6753
1.6747
1.6741
1.6736
1.6730
2.0076
2.0066
2.0057
2.0049
2.0040
2.4017
24002
2.3988
2.3974
2.3961
2.6757
0.25
0.10
0.05
0.025
0.01
0.005
2.6737
2.6718
1.0000
08165
07649
07407
3.0777
1.8856
12.7062
6.3138
2.9200
2.3534
2.1318
31.8207
6.9646
4.5407
3.7469
3.3649
63.6574
9.9248
5.8400
4.6041
2.6700
2.6682
1.6377
1.5332
4.3027
3.1824
2.7764
56
57
58
50
60
0.6789
0.6788
0.6787
0.6787
0.6786
1.2969
1.2966
1.2963
1.2961
1.2958
1.6725
1.6720
1.6716
1.6711
1.6706
2.0032
2.0025
2.0017
2.0010
2.0003
2.3948
2.3936
2.3024
2.3012
2.3901
2.6665
2.6649
2.6633
2.6618
2.6603
4
07267
14759
2.0150
2.5706
4.0322
07176
1.4398
1.9432
2.4460
3.1427
3.7074
07111
07064
0.7027
0.6998
1.4149
1.3968
1.3830
1.3722
1.8946
1.8595
1.8331
1.8125
2.3646
2.3060
2.9080
2.8965
2.8214
2.7638
3.4905
3.3554
3.2408
3.1603
61
62
63
64
65
0.6785
0.6785
0.6784
0.6783
0.6783
1.2956
1.2954
1.2951
1.2949
1.2947
1.6702
1.6608
1.6604
1.6600
1.6686
1.9996
1.9990
1.9983
1.0077
1.9971
1.000
2.3890
2.3880
2.3870
2.3860
2.3851
2.6589
2.2622
2.6575
2.6561
2.6549
2.6536
10
2.2281
2.2010
2.1788
2.1604
2.1448
2.1315
11
12
0.6074
1.3634
1.3562
1.3502
1.7950
2.7181
3.1058
1.7823
1.7700
13
14
15
0.6038
0,6924
0.6912
2.6810
2.6503
2.6245
2.6025
3.0545
3.0123
2.9768
2.9467
1.2945
1.2043
1.2941
1.2939
1.2938
1.6683
1.6679
1.6676
1.6672
1.6669
2.3842
2.3833
2.3824
2.3816
2.3808
2.6524
2.6512
26501
2.6490
2.6470
66
0.6782
0.6782
0.6781
0.6781
0.6780
1.9066
1.0060
1.0055
1.0040
1.9944
1.3450
1.3406
1.7613
1.7531
67
68
60
70
16
17
18
19
0.6901
0.6892
0.6884
0.6876
1.3368
1.3334
1.3304
1.3277
1.7459
1.7396
1,7341
1.7291
2.1199
2.1098
2.1009
2.0030
2.5835
2.5669
2.5524
25395
2.9208
2.8982
2.8784
2.8600
71
72
73
74
75
0.6780
0.6779
0.6779
0.6778
0.6778
1.2936
1.2934
1.2933
1.2931
1.2929
1.6666
1.6663
1.6660
1.6657
1.6654
1.0030
1.9935
1.9030
1.9925
1.9921
2.3800
2.3793
2.3785
2.3778
2.3771
2.6460
2.6459
2.6449
2.6430
2.6430
20
0.6870
1.3253
1.7247
2.0860
25280
2.8453
2.8314
28188
2.8073
2.7960
21
0.6864
0.6858
0.6853
1.3232
1.3212
1.3195
1.3178
1.7207
1.7171
1.7139
1.7100
2.0796
2.0739
2.0687
2.0639
25177
22
23
2.5083
2.4999
2.4022
76
77
78
79
80
0.6777
0.6777
0.6776
0.6776
0.6776
1.2928
1.2926
1.2925
1.2924
1.2922
1.6652
1.6640
1.6646
1.6644
1.6641
1.9917
1.9013
1.9908
1.0005
1.9001
2.3764
2.3758
2.3751
2.3745
2.3739
2.6421
2.6412
2.6403
2.6395
2.6387
24
0.6848
0.6844
25
1.3163
1.7081
2.0595
2.4851
2.7874
26
27
0.6840
0.6837
1.3150
1.3137
1.3125
1.7056
1.7033
1.7011
1.6991
1.6973
2.0555
2.0518
27787
2.7707
2.7633
2.4786
2.4727
81
82
83
84
85
0.6775
0.6775
0.6775
0.6774
0.6774
1.2921
1.2920
1.2918
1.2917
1.2916
1.6639
1.6636
1.6634
1.6632
1.6630
1.9897
1.9893
1.9890
1.986
1.0883
2.3733
2.3727
28
29
30
0.6834
0.6830
0.6828
2.0484
2.0452
2.0423
2.4671
2.4620
2.4573
2.6379
2.6371
2.6364
2.6356
2.6340
13114
2.7564
2.3721
1.3104
2.7500
2.3716
2.3710
31
32
33
34
35
0.6825
0.6822
0.6820
0.6818
0.6816
1.3005
1.3086
1.3077
1.3070
1.3062
1.605s
1.6939
1.6924
1.6909
1.6896
2.0395
2.0369
2.0345
2.0322
2.0301
2.4528
2.4487
2.4448
2.4411
24377
2.7740
2.7385
2.7333
2.7284
2.7238
0.6774
0.6773
0.6773
0.6773
0.6772
86
1.2915
1.2914
1.6620
1.6626
1.6624
1.6622
1.6620
1.9879
1.0876
1.0873
1.0870
1.9867
2.3705
2.3700
2.3605
2.3600
2.3685
2.6342
2.6335
2.6329
2.6322
2.6316
87
88
80
90
1.2912
1.2911
1.2910
36
37
38
0.6814
0.6812
0.6810
0.6808
1.3055
1.3049
1.3042
1.3036
1.3031
1.6883
1.6871
1.6860
1.6849
1.6839
2.0281
2.0262
2.0244
2.4345
2.4314
2.4286
2.7195
2.7154
2.7116
2.7079
2.7045
01
92
93
94
95
0.6772
0.6772
0.6771
0.6771
1.2000
1.2908
1.2907
1.2906
1.2905
1.6618
1.6616
1.6614
1.6612
1.6611
1.0864
1.9861
1.9858
1.9855
1.9853
2.3680
2.3676
2.3671
2.3667
2.3662
2.6300
2.6303
2.6297
2.6291
2.6286
39
40
2.0227
2.0211
2.4258
2.4233
0.6807
0.6771
0.6805
06804
41
42
43
44
45
1.3025
1.3020
1.3016
1.3011
1.3006
1.6829
1.6820
1.6811
1.6802
1.6794
2.0195
2.4208
24185
2.4163
24141
2.4121
2.7012
2.6081
2.6951
2.6923
2.6896
06
07
98
99
100
0.6771
0.6770
0.6770
0.6770
0.6770
1.2904
1.2903
1.2902
1.2902
1.2901
1.6600
1.6607
1.6606
1.6604
1.6602
2.3658
2.3654
2.3650
2.3646
2.3642
2.3607
2.6280
2.6275
2.6269
2.6264
2.6259
2.0181
0.6802
0.6801
0.6800
2.0167
2.0154
2.0141
1.0847
1.9845
1.9842
1.9840
0.6700
0.6797
46
1.3022
1.6787
2.0129
2.0117
2.0106
24102
2.6870
110
0.6767
1.2003
1.6588
1.9818
2.6213
47
48
Degrees of
freedom
1.2998
1.2904
1.6779
2.4083
2.6846
120
0.6765
1.2886
1.6577
1.9790
2.3578
2.6174
0.6796
1.6772
2.4066
2.6822
0.6745
1.2816
16449
19600
2.3263
2.5758
Degrees of
freedom
0.25
0.10
0.05
0.025
0.01
0.005
0.25
0.10
0.05
0.025
0.01
0.005
Lnnertail anmas
Pper-tail areas
Transcribed Image Text:Lbpertal areas Degrees of freedom 0.25 0.10 0.05 0.025 0.01 0.005 40 50 0.6795 0.6794 1.2901 1.2987 1.6766 1.6759 2.0096 2.0086 24049 2.4033 2.6800 Upper-tail areas 2.6778 Degrees of freedom 51 52 53 54 55 0.6793 0.6792 0.6791 0.6791 0.6790 1.2984 1.2980 1.2977 1.2974 1.2971 1.6753 1.6747 1.6741 1.6736 1.6730 2.0076 2.0066 2.0057 2.0049 2.0040 2.4017 24002 2.3988 2.3974 2.3961 2.6757 0.25 0.10 0.05 0.025 0.01 0.005 2.6737 2.6718 1.0000 08165 07649 07407 3.0777 1.8856 12.7062 6.3138 2.9200 2.3534 2.1318 31.8207 6.9646 4.5407 3.7469 3.3649 63.6574 9.9248 5.8400 4.6041 2.6700 2.6682 1.6377 1.5332 4.3027 3.1824 2.7764 56 57 58 50 60 0.6789 0.6788 0.6787 0.6787 0.6786 1.2969 1.2966 1.2963 1.2961 1.2958 1.6725 1.6720 1.6716 1.6711 1.6706 2.0032 2.0025 2.0017 2.0010 2.0003 2.3948 2.3936 2.3024 2.3012 2.3901 2.6665 2.6649 2.6633 2.6618 2.6603 4 07267 14759 2.0150 2.5706 4.0322 07176 1.4398 1.9432 2.4460 3.1427 3.7074 07111 07064 0.7027 0.6998 1.4149 1.3968 1.3830 1.3722 1.8946 1.8595 1.8331 1.8125 2.3646 2.3060 2.9080 2.8965 2.8214 2.7638 3.4905 3.3554 3.2408 3.1603 61 62 63 64 65 0.6785 0.6785 0.6784 0.6783 0.6783 1.2956 1.2954 1.2951 1.2949 1.2947 1.6702 1.6608 1.6604 1.6600 1.6686 1.9996 1.9990 1.9983 1.0077 1.9971 1.000 2.3890 2.3880 2.3870 2.3860 2.3851 2.6589 2.2622 2.6575 2.6561 2.6549 2.6536 10 2.2281 2.2010 2.1788 2.1604 2.1448 2.1315 11 12 0.6074 1.3634 1.3562 1.3502 1.7950 2.7181 3.1058 1.7823 1.7700 13 14 15 0.6038 0,6924 0.6912 2.6810 2.6503 2.6245 2.6025 3.0545 3.0123 2.9768 2.9467 1.2945 1.2043 1.2941 1.2939 1.2938 1.6683 1.6679 1.6676 1.6672 1.6669 2.3842 2.3833 2.3824 2.3816 2.3808 2.6524 2.6512 26501 2.6490 2.6470 66 0.6782 0.6782 0.6781 0.6781 0.6780 1.9066 1.0060 1.0055 1.0040 1.9944 1.3450 1.3406 1.7613 1.7531 67 68 60 70 16 17 18 19 0.6901 0.6892 0.6884 0.6876 1.3368 1.3334 1.3304 1.3277 1.7459 1.7396 1,7341 1.7291 2.1199 2.1098 2.1009 2.0030 2.5835 2.5669 2.5524 25395 2.9208 2.8982 2.8784 2.8600 71 72 73 74 75 0.6780 0.6779 0.6779 0.6778 0.6778 1.2936 1.2934 1.2933 1.2931 1.2929 1.6666 1.6663 1.6660 1.6657 1.6654 1.0030 1.9935 1.9030 1.9925 1.9921 2.3800 2.3793 2.3785 2.3778 2.3771 2.6460 2.6459 2.6449 2.6430 2.6430 20 0.6870 1.3253 1.7247 2.0860 25280 2.8453 2.8314 28188 2.8073 2.7960 21 0.6864 0.6858 0.6853 1.3232 1.3212 1.3195 1.3178 1.7207 1.7171 1.7139 1.7100 2.0796 2.0739 2.0687 2.0639 25177 22 23 2.5083 2.4999 2.4022 76 77 78 79 80 0.6777 0.6777 0.6776 0.6776 0.6776 1.2928 1.2926 1.2925 1.2924 1.2922 1.6652 1.6640 1.6646 1.6644 1.6641 1.9917 1.9013 1.9908 1.0005 1.9001 2.3764 2.3758 2.3751 2.3745 2.3739 2.6421 2.6412 2.6403 2.6395 2.6387 24 0.6848 0.6844 25 1.3163 1.7081 2.0595 2.4851 2.7874 26 27 0.6840 0.6837 1.3150 1.3137 1.3125 1.7056 1.7033 1.7011 1.6991 1.6973 2.0555 2.0518 27787 2.7707 2.7633 2.4786 2.4727 81 82 83 84 85 0.6775 0.6775 0.6775 0.6774 0.6774 1.2921 1.2920 1.2918 1.2917 1.2916 1.6639 1.6636 1.6634 1.6632 1.6630 1.9897 1.9893 1.9890 1.986 1.0883 2.3733 2.3727 28 29 30 0.6834 0.6830 0.6828 2.0484 2.0452 2.0423 2.4671 2.4620 2.4573 2.6379 2.6371 2.6364 2.6356 2.6340 13114 2.7564 2.3721 1.3104 2.7500 2.3716 2.3710 31 32 33 34 35 0.6825 0.6822 0.6820 0.6818 0.6816 1.3005 1.3086 1.3077 1.3070 1.3062 1.605s 1.6939 1.6924 1.6909 1.6896 2.0395 2.0369 2.0345 2.0322 2.0301 2.4528 2.4487 2.4448 2.4411 24377 2.7740 2.7385 2.7333 2.7284 2.7238 0.6774 0.6773 0.6773 0.6773 0.6772 86 1.2915 1.2914 1.6620 1.6626 1.6624 1.6622 1.6620 1.9879 1.0876 1.0873 1.0870 1.9867 2.3705 2.3700 2.3605 2.3600 2.3685 2.6342 2.6335 2.6329 2.6322 2.6316 87 88 80 90 1.2912 1.2911 1.2910 36 37 38 0.6814 0.6812 0.6810 0.6808 1.3055 1.3049 1.3042 1.3036 1.3031 1.6883 1.6871 1.6860 1.6849 1.6839 2.0281 2.0262 2.0244 2.4345 2.4314 2.4286 2.7195 2.7154 2.7116 2.7079 2.7045 01 92 93 94 95 0.6772 0.6772 0.6771 0.6771 1.2000 1.2908 1.2907 1.2906 1.2905 1.6618 1.6616 1.6614 1.6612 1.6611 1.0864 1.9861 1.9858 1.9855 1.9853 2.3680 2.3676 2.3671 2.3667 2.3662 2.6300 2.6303 2.6297 2.6291 2.6286 39 40 2.0227 2.0211 2.4258 2.4233 0.6807 0.6771 0.6805 06804 41 42 43 44 45 1.3025 1.3020 1.3016 1.3011 1.3006 1.6829 1.6820 1.6811 1.6802 1.6794 2.0195 2.4208 24185 2.4163 24141 2.4121 2.7012 2.6081 2.6951 2.6923 2.6896 06 07 98 99 100 0.6771 0.6770 0.6770 0.6770 0.6770 1.2904 1.2903 1.2902 1.2902 1.2901 1.6600 1.6607 1.6606 1.6604 1.6602 2.3658 2.3654 2.3650 2.3646 2.3642 2.3607 2.6280 2.6275 2.6269 2.6264 2.6259 2.0181 0.6802 0.6801 0.6800 2.0167 2.0154 2.0141 1.0847 1.9845 1.9842 1.9840 0.6700 0.6797 46 1.3022 1.6787 2.0129 2.0117 2.0106 24102 2.6870 110 0.6767 1.2003 1.6588 1.9818 2.6213 47 48 Degrees of freedom 1.2998 1.2904 1.6779 2.4083 2.6846 120 0.6765 1.2886 1.6577 1.9790 2.3578 2.6174 0.6796 1.6772 2.4066 2.6822 0.6745 1.2816 16449 19600 2.3263 2.5758 Degrees of freedom 0.25 0.10 0.05 0.025 0.01 0.005 0.25 0.10 0.05 0.025 0.01 0.005 Lnnertail anmas Pper-tail areas
Based on a sample of n= 15, the least-squares method was used to develop the prediction line Ý; = 6+ 2X;. In addition, Syx = 1.5, X= 3 and E (X; -X)² = 15.
i= 1
Complete parts (a) and (b) below.
Click here to view page 1 of the table of the critical values of t.
Click here to view page 2 of the table of the critical values of t.
a. Construct a 90% confidence interval estimate of the population mean response for X = 2.
|SHyx=2
(Round to two decimal places as needed.)
b. Construct a 90% prediction interval of an individual response for X = 2.
OsYx=2O
(Round to two decimal places as needed.)
Transcribed Image Text:Based on a sample of n= 15, the least-squares method was used to develop the prediction line Ý; = 6+ 2X;. In addition, Syx = 1.5, X= 3 and E (X; -X)² = 15. i= 1 Complete parts (a) and (b) below. Click here to view page 1 of the table of the critical values of t. Click here to view page 2 of the table of the critical values of t. a. Construct a 90% confidence interval estimate of the population mean response for X = 2. |SHyx=2 (Round to two decimal places as needed.) b. Construct a 90% prediction interval of an individual response for X = 2. OsYx=2O (Round to two decimal places as needed.)
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