Irrigation regime Very Medium Heavy heavy None Light 415 572 705 821 968 360 722 839 942 862 No P4 508 805 751 732 748 487 638 733 759 809 410 518 488 874 831 347 684 748 923 539 With P4 373 440 676 541 659 926 492 647 859 696 Polymer
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Golden Torch Cacti Lengths. In their study of Golden Torch cacti, Feldman and Cross white investigated the total length of cuttings at the end of 16 months. Here are the data, in millimeters.
a. the response variable
b. the factors
c. the levels of each factor
d. the number of treatment combinations
e. the experimental units
f. the number of observations for each treatment combination
g. the experimental factors, if any
h. the classification factors, if any
i. whether the study is a designed experiment or an observational study
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- Price Age Mileage13590 6 6148513775 6 5434422991 1 824615303 4 2485616388 3 2210016600 3 2370216987 4 4740118489 2 1688818859 3 3538019857 3 2963411877 9 5579214989 3 4618315900 3 3700916500 4 455219440 9 8690212988 5 7724115777 6 5964710490 9 932418938 10 4822111988 8 42408 The accompanying table shows a portion of data consisting of the price, the age, and the mileage for 20 used sedans. Price Age Mileage 13590 6 61485 13775 6 54344 ⋮ ⋮ ⋮ 11988 8 42408 Determine the sample regression equation that enables us to predict the price of a sedan on the basis of its age and mileage. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) [If you are using R to obtain the output, then first enter the following command at the prompt: options(scipen=10). This will ensure that the output is not in scientific…Boats (tens of thousands) Manatees 7171 5353 7171 3939 6969 3434 6969 4949 7272 4040 7272 6161 7373 5353 7878 6565 8181 8484 8484 8080 9393 8282 9595 9797 9393 7171 9696 6767 9797 8181 9797 9292 101101 7272 9696 8888 9898 9595 9090 8383 8787 8888 9090 8383 8787 7373 8787 6969 Use the accompanying paired data consisting of registered boats (tens of thousands) and manatee fatalities from boat encounters. Let x represent the number of registered boats and let y represent the corresponding number of manatee deaths. Use the given number of registered boats and the given confidence level to construct a prediction interval estimate of manatee deaths. Use xequals=8888 (for 88880,000 registered boats) with a 9999% confidence level.…Cost of Materials 78713 42774 27222 37040 12030 13674 19130 33521 19612 5557 163 314 2622 4219 5357 1061 707 10421 4140 7125 8994 5504 716 8926 11121 2283 364 1813 71 1321 12376 9661 19285 18632 2170 7290 8135 12980 4011 5101 3755 2694 3279 20596 10604 24634 28963 8483 6940 8863 2823 29572 3811 1047 2055 1098 11298 34596 27187 19932 9849 46935 11130 13085 132880 6182 4446 7091 496 3805 7195 57264 1865 163 1682 85 395 255 177 943 3527 4254 2282 1387 1183 17010 565 5432 45696 6913 11184 5735 31892 4696 2790 9364 8720 3527 31527 4909 21531 6232 1689 19273 12954 11858 23474 14343 16515 18543 60857 21819 13897 5523 12657 12578 11065 7621 29591 44486 13398 223639 42367 7760 4363 1674 8120 5476 8760 18028 16787 1020 8114 382 3646 506 6604 1789 997 8530 Value Added by Mfg. 23518 15724 24506 21667 20712 12640 4258 35210 20548 23442 287 1508 624 2471 4307 673 817 8986 3145 4076 3806 4276 1239 9423 11045 1916 599 2063 34 1445 10603 5775 10404 13274 1909 4606 5518 12464 5447 2290 4182 2818 2201…
- number of drinks per week382152200452701530254169531210611211966670411101922211610105231444362852716403111Method End-of-Training Proficiency Method coded Study Time Proficiency*Method coded classroom 14 94 0 22 0 classroom 17 98 0 18 0 classroom 19 96 0 12 0 classroom 26 105 0 25 0 classroom 28 110 0 10 0 online 34 84 1 8 84 classroom 35 102 0 16 0 classroom 36 111 0 21 0 classroom 38 100 0 9 0 online 38 80 1 23 80 classroom 40 102 0 14 0 classroom 41 109 0 15 0 classroom 42 111 0 20 0 online 43 90 1 11 90 online 43 97 1 24 97 online 46 104 1 22 104 online 52 101 1 18 101 online 53 96 1 12 96 classroom 55 92 0 25 0 classroom 55 99 0 10 0 online 55 110 1 8 110 classroom 57 107 0 16 0 online 61 97 1 21 97 online 63 112 1 9 112 classroom 66 130 0 23 0 online 74 118 1 14 118 online 76 120 1 15 120 online 79 120 1 20 120 online 81 118 1 11 118 online 93 115 1 24 115 Develop the multiple regression model that best predicts the score on the end-of-training exam. Consider the following options:Model 1: all independent variables…Program Per-Year Tuition ($) / Mean Starting Salary Upon Graduation ($)65,028 153,10868,882 152,64766,427 145,53568,299 141,71665,493 138,89966,663 155,51465,232 149,25368,670 150,84163,973 133,81961,996 145,81867,714 146,80761,139 147,07660,836 141,65357,270 140,26255,157 122,91654,127 116,71355,480 126,08652,808 127,11753,414 129,22949,587 122,17045,738 111,27746,552 111,11949,982 110,67345,091 108,29238,565 77,95948,642 82,78947,604 101,57249,252 78,46939,510 87,78632,884 74,47344,782 75,43442,554 54,54750,301 64,97835,013 101,61823,004 55,03040,819 80,37137,604 53,428
- Marital StatusHispanic Non-Hispanic TotalCurrently Married319738 1057Divorced or Separated130329 459Widowed88402 490Never Married or Living Asan Unmarried Couple4195 136Total5781564 2142Source: Steven S. Coughlin, Robert J. Uhler, Thomas Richards, and KatherineM. Wilson, “Breast and Cervical Cancer Screening Practices Among Hispanicand Non-Hispanic Women Residing Near the United States–Mexico Border,1999–2000,” Family and Community Health, 26 (2003), 130–139.(a) We select at random a subject who had a mammogram. What is the probability that she isdivorced or separated?(b) We select at random a subject who had a mammogram and learn that she is Hispanic. With thatinformation, what is the probability that she is married?(c) We select at random a subject who had a mammogram. What is the probability that she is nonHispanic and divorced or separated?(d) We select at random a subject who had a mammogram. What is the probability that she isHispanic or she is widowed?(e) We select at random a…Electricity_Sales Number_of_Customers Price Degree_Days1,144,781 690,044.7 6.7651 547.43381,143,784 693,866.5 6.8928 -26.32671,184,600 697,890.9 6.8626 -1.68351,139,054 701,234.3 6.2738 12.41161,204,495 704,746.5 6.1591 606.30471,179,366 709,583.7 6.2017 148.18261,085,489 713,389.1 6.5480 -2.00321,160,943 717,401.4 5.9404 -83.56021,158,592 721,355.7 5.8960 66.95031,193,556 724,228.1 6.0853 24.28791,202,514 727,191.2 6.2554 -0.94671,174,335 729,230.4 6.3808 -56.38711,174,335 731,584.4 6.2768 -360.98421,161,770 734,456.2 6.5243 -192.40871,142,863 737,848.2 6.4216 -2.85731,196,627 739,084.7 6.2837 -168.64071,236,468 740,332.7 6.1659 551.90681,188,673 741,904.4 6.0801 55.77211,181,075 743,467.6 6.9015 -2.50411,203,114 743,895.9 6.4296 -159.82191,168,515 745,209.2 6.9283 -610.34381,224,423…Height Weight GPA Study TV Computer FamilySz Floors stairs friends female Acctg Mgmt Mktg Fin IntBus Other 67 138 4 6 2 1 5 2 26 6 1 0 0 0 0 0 1 73 175 3.47 5 0 5 4 3 26 5 0 0 1 0 0 0 0 62 112 4 6 2 1 4 3 34 3 1 0 1 0 0 0 0 64.5 122 3.5 3.4 1 0 5 3 47 4 1 0 1 0 0 0 0 69 135 4 4 2 1 4 1 13 5 1 0 0 1 0 0 0 61 129 3.8 4 2 3 5 2 17 5 1 0 0 0 0 0 1 64 135 3.4 3 2 0 5 1 0 2 1 0 1 0 0 0 0 68 150 4 10 1 0 4 2 31 4 1 1 0 0 0 0 0 70 130 3.8 4 1 2 4 2 26 8 0 0 1 0 0 0 0 65 172 3.6 2.5 1.5 0.5 4 2 26 8 0 0 1 0 0 0 0 74 235 2.6 1 1 1 4 3 52 8 0 0 1 0 0 0 0 69 150 3 2 3 3 4 3 26 5 0 0 0 1 0 0 0 72 152 2.8 2.5 0.5 3 5 3 30 10 0 0 0 0 0 1 0 73 155 3 3 1 2 4 2 26 5 0 1 0 0 0 0 0 74 170 3.75 2 1.5 1.5 3 2 15 5 0 0 1 0 0 0 0 68 150 3 2.5 2 1 4 3 26 10 0 1 0 0 0 0 0 63 115 2.5 3 1 2.5 3 2 13 8 1 0 1 0 0 0 0 71 180 2.1 1 3 3 4 2 13 10 0 1 0 0 0 0 0 72 167 2.7 1 2 4 4 2 18 3 0 0 1 0 0 0 0 72 180 3.8 2 1.5 0.5 3 1 300 2 0 0 0 0 1 0 0…
- Height Weight GPA Study TV Computer FamilySz Floors stairs friends female Acctg Mgmt Mktg Fin IntBus Other 67 138 4 6 2 1 5 2 26 6 1 0 0 0 0 0 1 73 175 3.47 5 0 5 4 3 26 5 0 0 1 0 0 0 0 62 112 4 6 2 1 4 3 34 3 1 0 1 0 0 0 0 64.5 122 3.5 3.4 1 0 5 3 47 4 1 0 1 0 0 0 0 69 135 4 4 2 1 4 1 13 5 1 0 0 1 0 0 0 61 129 3.8 4 2 3 5 2 17 5 1 0 0 0 0 0 1 64 135 3.4 3 2 0 5 1 0 2 1 0 1 0 0 0 0 68 150 4 10 1 0 4 2 31 4 1 1 0 0 0 0 0 70 130 3.8 4 1 2 4 2 26 8 0 0 1 0 0 0 0 65 172 3.6 2.5 1.5 0.5 4 2 26 8 0 0 1 0 0 0 0 74 235 2.6 1 1 1 4 3 52 8 0 0 1 0 0 0 0 69 150 3 2 3 3 4 3 26 5 0 0 0 1 0 0 0 72 152 2.8 2.5 0.5 3 5 3 30 10 0 0 0 0 0 1 0 73 155 3 3 1 2 4 2 26 5 0 1 0 0 0 0 0 74 170 3.75 2 1.5 1.5 3 2 15 5 0 0 1 0 0 0 0 68 150 3 2.5 2 1 4 3 26 10 0 1 0 0 0 0 0 63 115 2.5 3 1 2.5 3 2 13 8 1 0 1 0 0 0 0 71 180 2.1 1 3 3 4 2 13 10 0 1 0 0 0 0 0 72 167 2.7 1 2 4 4 2 18 3 0 0 1 0 0 0 0…Height Weight GPA Study TV Computer FamilySz Floors stairs friends female Acctg Mgmt Mktg Fin IntBus Other 67 138 4 6 2 1 5 2 26 6 1 0 0 0 0 0 1 73 175 3.47 5 0 5 4 3 26 5 0 0 1 0 0 0 0 62 112 4 6 2 1 4 3 34 3 1 0 1 0 0 0 0 64.5 122 3.5 3.4 1 0 5 3 47 4 1 0 1 0 0 0 0 69 135 4 4 2 1 4 1 13 5 1 0 0 1 0 0 0 61 129 3.8 4 2 3 5 2 17 5 1 0 0 0 0 0 1 64 135 3.4 3 2 0 5 1 0 2 1 0 1 0 0 0 0 68 150 4 10 1 0 4 2 31 4 1 1 0 0 0 0 0 70 130 3.8 4 1 2 4 2 26 8 0 0 1 0 0 0 0 65 172 3.6 2.5 1.5 0.5 4 2 26 8 0 0 1 0 0 0 0 74 235 2.6 1 1 1 4 3 52 8 0 0 1 0 0 0 0 69 150 3 2 3 3 4 3 26 5 0 0 0 1 0 0 0 72 152 2.8 2.5 0.5 3 5 3 30 10 0 0 0 0 0 1 0 73 155 3 3 1 2 4 2 26 5 0 1 0 0 0 0 0 74 170 3.75 2 1.5 1.5 3 2 15 5 0 0 1 0 0 0 0 68 150 3 2.5 2 1 4 3 26 10 0 1 0 0 0 0 0 63 115 2.5 3 1 2.5 3 2 13 8 1 0 1 0 0 0 0 71 180 2.1 1 3 3 4 2 13 10 0 1 0 0 0 0 0 72 167 2.7 1 2 4 4 2 18 3 0 0 1 0 0 0 0 72 180 3.8 2 1.5 0.5 3 1 300 2 0 0 0 0 1 0 0…STUDENT ID HEIGHT (m) WEIGHT (kg) AGE 221063641 1.65 60 18 221026045 1.45 41 17 220050244 1.71 65 18 221082107 1.55 45 17 221003142 1.57 53 19 221137823 1.68 64 18 221056904 1.60 52 20 221051686 1.64 60 21 221009965 1.68 58 22 221120580 1.50 64 21 221007148 1.65 56 22 221139850 1.55 56 20 220036489 1.65 54 20 221067817 1.68 60 21 221047220 1.79 65 18 220040028 1.70 61 18 221010386 1.50 51 18 221079017 1.65 50 19 220040028 1.56 56 19 221078673 1.65 65 21 219060045 1.68 68 21 220049408 1.66 65 19 21915970 1.56 56 19 220090246 1.65 60 19 220119066 1.73 68 21 219057222 1.58 58 19 220119287 1.65 65 18 220076499 1.78 65 19 220119805 1.68 68 19 Question 1 Use graphical descriptive techniques (Pie…