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A taxi company is trying to decide whether to purchase brand A or brand B tires for its fleet of taxis. To estimate the difference in the two brands, an experiment is conducted using 12 of each brand. The tires are run until they wear out. The results are given in the table below. Compute a 99% confidence interval for μA - HB assuming the populations to be approximately normally distributed. You may not assume that the variances are equal. Brand A x₁ = 35,100 kilometers X2 37,200 kilometers Brand B S₁ = 4800 kilometers $2 = 6100 kilometers Click here to view page 1 of the table of critical values of the t-distribution. Click here to view page 2 of the table of critical values of the t-distribution. The confidence interval is

A taxi company is trying to decide whether to purchase brand A or brand B tires for its fleet of taxis. To estimate the difference in the two brands, an experiment is conducted using 12 of each brand. The tires are run until they wear out. The results are given in the table below. Compute a 99% confidence interval for μA - HB assuming the populations to be approximately normally distributed. You may not assume that the variances are equal. Brand A x₁ = 35,100 kilometers X2 37,200 kilometers Brand B S₁ = 4800 kilometers $2 = 6100 kilometers Click here to view page 1 of the table of critical values of the t-distribution. Click here to view page 2 of the table of critical values of the t-distribution. The confidence interval is

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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Critical Values of the t-Distribution Critical Values of the t-Distribution a a 0.40 0.30 0.20 0.15 0.10 0.05 0.025 0.02 0.015 0.01 0.0075 0.005 0.0025 0.0005 1 0.325 0.727 1.376 1.963 3.078 6.314 12.706 1 15.894 21.205 31.821 42.433 63.656 127.321 636.578 2 0.289 0.617 1.061 1.386 1.886 2.920 4.303 2 4.849 5.643 6.965 8.073 9.925 14.089 31.600 3 0.277 0.584 0.978 1.250 1.638 2.353 3.182 3 3.482 3.896 4.541 5.047 5.841 7.453 12.924 4 0.271 0.569 0.941 1.190 1.533 2.132 2.776 4 2.999 3.298 3.747 4.088 4.604 5.598 8.610 5 0.267 0.559 0.920 1.156 1.476 2.015 2.571 5 2.757 3.003 3.365 3.634 4.032 4.773 6.869 в 0.265 0.553 0.906 1.134 1.440 1.943 2.447 6 2.612 2.829 3.143 3.372 3.707 4.317 5.959 7 0.263 0.549 0.896 1.119 1.415 1.895 2.365 7 2.517 2.715 2.998 3.203 3.499 4.029 5.408 8 0.262 0.546 0.889 1.108 1.397 1.860 2.306 8 2.449 2.634 2.896 3.085 3.355 3.833 5.041 9 0.261 0.543 0.883 1.100 1.383 1.833 2.262 9 2.398 2.574 2.821 2.998 3.250 3.690 4.781 10 0.260 0.542 0.879 1.093 1.372 1.812 2.228 10 2.359 2.527 2.764 2.932 3.169 3.581 4.587 11 0.260 0.540 0.876 1.088 1.363 1.796 2.201 11 2.328 2.491 2.718 2.879 3.106 3.497 4.437 12 0.259 0.539 0.873 1.083 1.356 1.782 2.179 12 2.303 2.461 2.681 2.836 3.055 3.428 4.318 13 0.259 0.538 0.870 1.079 1.350 1.771 2.160 13 2.282 2.436 2.650 2.801 3.012 3.372 4.221 14 0.258 0.537 0.868 1.076 1.345 1.761 2.145 14 2.264 2.415 2.624 2.771 2.977 3.326 4.140 15 0.258 0.536 0.866 1.074 1.341 1.753 2.131 15 2.249 2.397 2.602 2.746 2.947 3.286 4.073 16 0.258 0.535 0.865 1.071 1.337 1.746 2.120 16 2.235 2.382 2.583 2.724 2.921 3.252 4.015 17 0.257 0.534 0.863 1.069 1.333 1.740 2.110 17 2.224 2.368 2.567 2.706 2.898 3.222 3.965 18 0.257 0.534 0.862 1.067 1.330 1.734 2.101 18 2.214 2.356 2.552 2.689 2.878 3.197 3.922 19 0.257 0.533 0.861 1.066 1.328 1.729 2.093 2.205 2.346 2.539 2.674 2.861 3.174 3.883 20 0.257 0.533 0.860 1.064 1.325 1.725 2.086 20 2.197 2.336 2.528 2.661 2.845 3.153 3.850 21 0.257 0.532 0.859 1.063 1.323 1.721 2.080 21 2.189 2.328 2.518 2.649 2.831 3.135 3.819 22 0.256 0.532 0.858 1.061 1.321 1.717 2.074 22 2.183 2.320 2.508 2.639 2.819 3.119 3.792 23 0.256 0.532 0.858 1.060 1.319 1.714 2.069 23 2.177 2.313 2.500 2.629 2.807 3.104 3.768 24 0.256 0.531 0.857 1.059 1.318 1.711 2.064 24 2.172 2.307 2.492 2.620 2.797 3.091 3.745 25 0.256 0.531 0.856 1.058 1.316 1.708 2.060 25 2.167 2.301 2.485 2.612 2.787 3.078 3.725 26 0.256 0.531 0.856 1.058 1.315 1.706 2.056 27 0.256 0.531 0.855 1.057 1.314 1.703 2.052 28 0.256 0.530 0.855 1.056 1.313 1.701 2.048 - 29 0.256 0.530 0.854 1.055 1.311 1.699 2.045 30 0.256 0.530 0.854 1.055 1.310 1.697 2.042 40 0.255 0.529 0.851 1.050 1.303 1.684 2.021 60 0.254 0.527 0.848 1.045 1.296 1.671 2.000 120 0.254 0.526 0.845 1.041 1.289 1.658 1.980 8" 0.253 0.524 0.842 1.036 1.282 1.645 1.960 e 0.40 0.30 0.20 0.15 0.10 - <> 120 888 988 26 2.162 2.296 2.479 2.605 2.779 3.067 3.707 27 2.158 2.291 2.473 2.598 2.771 3.057 3.689 28 2.154 2.286 2.467 2.592 2.763 3.047 3.674 2.150 2.282 2.462 2.586 2.756 3.038 3.660 30 2.147 2.278 2.457 2.581 2.750 3.030 3.646 40 2.123 2.250 2.423 2.542 2.704 2.971 3.551 60 2.099 2.223 2.390 2.504 2.660 2.915 3.460 2.076 2.196 2.358 2.468 2.617 2.860 3.373 2.054 2.170 2.326 2.432 2.576 2.807 3.290 0.05 0.025 0.02 0.015 0.01 0.0075 0.005 0.0025 0.0005 a A a C
Transcribed Image Text:Critical Values of the t-Distribution Critical Values of the t-Distribution a a 0.40 0.30 0.20 0.15 0.10 0.05 0.025 0.02 0.015 0.01 0.0075 0.005 0.0025 0.0005 1 0.325 0.727 1.376 1.963 3.078 6.314 12.706 1 15.894 21.205 31.821 42.433 63.656 127.321 636.578 2 0.289 0.617 1.061 1.386 1.886 2.920 4.303 2 4.849 5.643 6.965 8.073 9.925 14.089 31.600 3 0.277 0.584 0.978 1.250 1.638 2.353 3.182 3 3.482 3.896 4.541 5.047 5.841 7.453 12.924 4 0.271 0.569 0.941 1.190 1.533 2.132 2.776 4 2.999 3.298 3.747 4.088 4.604 5.598 8.610 5 0.267 0.559 0.920 1.156 1.476 2.015 2.571 5 2.757 3.003 3.365 3.634 4.032 4.773 6.869 в 0.265 0.553 0.906 1.134 1.440 1.943 2.447 6 2.612 2.829 3.143 3.372 3.707 4.317 5.959 7 0.263 0.549 0.896 1.119 1.415 1.895 2.365 7 2.517 2.715 2.998 3.203 3.499 4.029 5.408 8 0.262 0.546 0.889 1.108 1.397 1.860 2.306 8 2.449 2.634 2.896 3.085 3.355 3.833 5.041 9 0.261 0.543 0.883 1.100 1.383 1.833 2.262 9 2.398 2.574 2.821 2.998 3.250 3.690 4.781 10 0.260 0.542 0.879 1.093 1.372 1.812 2.228 10 2.359 2.527 2.764 2.932 3.169 3.581 4.587 11 0.260 0.540 0.876 1.088 1.363 1.796 2.201 11 2.328 2.491 2.718 2.879 3.106 3.497 4.437 12 0.259 0.539 0.873 1.083 1.356 1.782 2.179 12 2.303 2.461 2.681 2.836 3.055 3.428 4.318 13 0.259 0.538 0.870 1.079 1.350 1.771 2.160 13 2.282 2.436 2.650 2.801 3.012 3.372 4.221 14 0.258 0.537 0.868 1.076 1.345 1.761 2.145 14 2.264 2.415 2.624 2.771 2.977 3.326 4.140 15 0.258 0.536 0.866 1.074 1.341 1.753 2.131 15 2.249 2.397 2.602 2.746 2.947 3.286 4.073 16 0.258 0.535 0.865 1.071 1.337 1.746 2.120 16 2.235 2.382 2.583 2.724 2.921 3.252 4.015 17 0.257 0.534 0.863 1.069 1.333 1.740 2.110 17 2.224 2.368 2.567 2.706 2.898 3.222 3.965 18 0.257 0.534 0.862 1.067 1.330 1.734 2.101 18 2.214 2.356 2.552 2.689 2.878 3.197 3.922 19 0.257 0.533 0.861 1.066 1.328 1.729 2.093 2.205 2.346 2.539 2.674 2.861 3.174 3.883 20 0.257 0.533 0.860 1.064 1.325 1.725 2.086 20 2.197 2.336 2.528 2.661 2.845 3.153 3.850 21 0.257 0.532 0.859 1.063 1.323 1.721 2.080 21 2.189 2.328 2.518 2.649 2.831 3.135 3.819 22 0.256 0.532 0.858 1.061 1.321 1.717 2.074 22 2.183 2.320 2.508 2.639 2.819 3.119 3.792 23 0.256 0.532 0.858 1.060 1.319 1.714 2.069 23 2.177 2.313 2.500 2.629 2.807 3.104 3.768 24 0.256 0.531 0.857 1.059 1.318 1.711 2.064 24 2.172 2.307 2.492 2.620 2.797 3.091 3.745 25 0.256 0.531 0.856 1.058 1.316 1.708 2.060 25 2.167 2.301 2.485 2.612 2.787 3.078 3.725 26 0.256 0.531 0.856 1.058 1.315 1.706 2.056 27 0.256 0.531 0.855 1.057 1.314 1.703 2.052 28 0.256 0.530 0.855 1.056 1.313 1.701 2.048 - 29 0.256 0.530 0.854 1.055 1.311 1.699 2.045 30 0.256 0.530 0.854 1.055 1.310 1.697 2.042 40 0.255 0.529 0.851 1.050 1.303 1.684 2.021 60 0.254 0.527 0.848 1.045 1.296 1.671 2.000 120 0.254 0.526 0.845 1.041 1.289 1.658 1.980 8" 0.253 0.524 0.842 1.036 1.282 1.645 1.960 e 0.40 0.30 0.20 0.15 0.10 - <> 120 888 988 26 2.162 2.296 2.479 2.605 2.779 3.067 3.707 27 2.158 2.291 2.473 2.598 2.771 3.057 3.689 28 2.154 2.286 2.467 2.592 2.763 3.047 3.674 2.150 2.282 2.462 2.586 2.756 3.038 3.660 30 2.147 2.278 2.457 2.581 2.750 3.030 3.646 40 2.123 2.250 2.423 2.542 2.704 2.971 3.551 60 2.099 2.223 2.390 2.504 2.660 2.915 3.460 2.076 2.196 2.358 2.468 2.617 2.860 3.373 2.054 2.170 2.326 2.432 2.576 2.807 3.290 0.05 0.025 0.02 0.015 0.01 0.0075 0.005 0.0025 0.0005 a A a C
A taxi company is trying to decide whether to purchase brand A or brand B tires for its fleet of taxis. To estimate the difference in the two brands, an experiment is conducted using 12 of each brand. The tires are run until they wear out. The results are given in the table below. Compute a 99% confidence interval for μд- μ assuming the populations to be approximately normally distributed. You may not assume that the variances are equal. Brand A X₁ = 35,100 kilometers X2 37,200 kilometers Brand B S₁ = 4800 kilometers $2 = 6100 kilometers Click here to view page 1 of the table of critical values of the t-distribution. Click here to view page 2 of the table of critical values of the t-distribution. The confidence interval is <HA HB (Round to the nearest integer as needed.)
Transcribed Image Text:A taxi company is trying to decide whether to purchase brand A or brand B tires for its fleet of taxis. To estimate the difference in the two brands, an experiment is conducted using 12 of each brand. The tires are run until they wear out. The results are given in the table below. Compute a 99% confidence interval for μд- μ assuming the populations to be approximately normally distributed. You may not assume that the variances are equal. Brand A X₁ = 35,100 kilometers X2 37,200 kilometers Brand B S₁ = 4800 kilometers $2 = 6100 kilometers Click here to view page 1 of the table of critical values of the t-distribution. Click here to view page 2 of the table of critical values of the t-distribution. The confidence interval is <HA HB (Round to the nearest integer as needed.)
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