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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.

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
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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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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