MAT210 exam 2 hrs

Vincent666
Final_UGstats_practice_KEY.xlsx
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Answer_Sheet

DO NOT WRITE IN THIS TAB!!!!!!!
Question Sheet Part Points Answer
Q1 1A 2 D
Q1 1B 2 A
Q1 1C 2 B
Q1 1D 2 B
Q1 1E 2 C
Q1 1F 2 B
Q1 1G 2 D
Q1 1H 2 C
Q1 1I 8 Nothing to Submit
Q2 2A 2 88.7051701852
Q2 2B 2 22.5853729642
Q2 2C 2 22271203.5555556
Q2 2D 2 23491893.0332999
Q2 2E 2 84.0200005
Q2 2F 2 13028700
Q2 2G 4 1.4572451129
Q2 2H 4 SECOND
Q3 3A 2 0.0353553391
Q3 3B 2 5.6568542495
Q3 3C 4 0.0000000077
Q3 3D 4 REJECT
Q3 3E 4 0.0000000154
Q3 3F 2 2.5260074194
Q3 3G 2 0.4206044555
Q3 3H 2 0.3085375387
Q3 3I 4 0.0145398585
Q3 3J 4 0.0018284237
Q3 3K 2 495
Q3 3L 4 2400.9117629338
Q3 3M 2 N
Q3 3N 4 0.718256966
Q3 3O 4 0.0949128951
Q4 4A 2 -457129.583350942
Q4 4B 4 9492.8404176904
Q4 4C 2 -1.806835499
Q4 4D 4 9242.7902357367
Q4 4E 2 LOG
Q4 4F 2 SAME
Q4 4G 6 12591.2479687789
Q4 4H 2 0.3034774094
Q4 4I 2 N
Q4 4J 2 Y
Q4 4K 2 0.0049174795
Q4 4L 2 FEAT
Q4 4M 2 0.5290309924
Q4 4N 2 Y
Q4 4O 2 12438.8917062984
Q4 4P 2 -0.064644784
Q4 4Q 2 0.5394134353
Q4 4R 2 Y
Total 134

Instructions

Instructions
Answer all the questions on the tabs that say Q on them.
Put all answers in the red shaded cells in the Q sheets.
Follow the directions on the Q tabs AND put your answers in the red cells there as indicated.
You may create new tabs for work if you wish. I will not read them. I try to not read any of the exams. That is why I write a python program to grade them.
Answers should have 3 significant figures (3 numbers) in them. More is OK, as the computer is reading it.
I have used decimals for all %'s. Use decimals in your answers.
Do not do anything to the Answer_Sheet tab.
I will auto grade the exams using a python script.
Answer as a number with no special characters (, . ; for example) or you will lose points. Only answer with text if the question indicates to do so in the Notes column.
Use decimals in your answer unless otherwise specified.
Failure to follow directions will result in loss of points.
Failure to uplaod your correctly named file to canvas will result in loss of points. (see question 1H)
Notes

Data

Zoom Q2 on returns:
Date Adj Close Volume Return First Return Second
4/15/19 62 25764700 0.226451629 1.0035401762
4/22/19 66.220001 27100400
4/29/19 79.18 14352900
5/6/19 79.629997 9195800
5/13/19 89.980003 15273300
5/20/19 76.25 13642000
5/27/19 79.730003 6743700
6/3/19 94.050003 16513000
6/10/19 100.290001 17455300
6/17/19 100.470001 12426800
6/24/19 88.790001 18683500
7/1/19 91.879997 12481600
7/8/19 93.300003 9884300
7/15/19 93.379997 12053100
7/22/19 102.199997 9919400
7/29/19 93.660004 10028000
8/5/19 94.199997 5525300
8/12/19 92.529999 5551300
8/19/19 91.629997 4983500
8/26/19 91.669998 4709400
9/2/19 85.410004 16221100
9/9/19 78.849998 14912500
9/16/19 82.629997 9322100
9/23/19 76.040001 8013900
9/30/19 76.529999 8528600
10/7/19 71.349998 10752900
10/14/19 66.080002 27172300
10/21/19 63.450001 16605600
10/28/19 70.389999 16972000
11/4/19 69.849998 7297000
11/11/19 70.010002 8179800
11/18/19 73.160004 10340300
11/25/19 74.5 5641400
12/2/19 62.740002 22585500
12/9/19 63.540001 18622100
12/16/19 66.93 11632700
12/23/19 66.639999 4569100
12/30/19 67.279999 4957500
1/6/20 73.089996 17046400
1/13/20 75.559998 13575800
1/20/20 73 6047500
1/27/20 76.300003 8669400
2/3/20 88.639999 33808600
2/10/20 90.949997 12176000
2/17/20 101.760002 30214400
2/24/20 105 67689000
3/2/20 114.32 84418800
3/9/20 107.470001 58723400
3/16/20 130.550003 79647500
3/23/20 151.699997 87350400
3/30/20 128.199997 76791100
4/6/20 124.510002 87301400
4/13/20 150.259995 56208200
4/17/20 152.349197 8363392

OJ_Data

LOGS: Second Regression with fewer variables:
week sales1 sales2 sales3 sales4 sales5 price1 price2 price3 price4 price5 disp1 disp2 disp3 disp4 disp5 feat1 feat2 feat3 feat4 feat5 grmar1 grmar2 grmar3 grmar4 grmar5 LOGsales1 LOGsales2 LOGsales3 LOGsales4 LOGsales5 LOGprice1 LOGprice2 LOGprice3 LOGprice4 LOGprice5 feat2 disp2 LOGprice1 LOGprice2 feat2
40 6528 8448 47040 4224 2432 0.057 0.060 0.030 0.047 0.025 0.2843137255 0 0.4965986395 0 0 0 0 1 0 1 0.3443568627 0.2957 0.1079353741 0.3077 0.0101 8.7838558966 9.0416850059 10.7587535826 8.3485378254 7.7964692431 -2.8614199359 -2.8082158999 -3.5223062543 -3.063609696 -3.6951490671 0 0 -2.8614199359 -2.8082158999 0
43 6016 8352 3008 5696 2112 0.057 0.060 0.044 0.047 0.039 0 0 0 0 0 0 0 1 0 0 0.3421010638 0.2957 0.4168021277 0.3659202247 0.4281 8.7021778656 9.0302563101 8.0090306851 8.6475194531 7.6553906448 -2.8614199359 -2.8082158999 -3.1150790312 -3.063609696 -3.2466003729 0 0 -2.8614199359 -2.8082158999 0
44 6272 7776 2816 36160 896 0.057 0.060 0.044 0.040 0.039 0 0 0 0 0 0 0 0 1 0 0.319927551 0.2957 0.3725909091 0.3348178761 0.4281 8.743850562 8.9587973461 7.9430727173 10.4957088145 6.797940413 -2.8614199359 -2.8082158999 -3.1150790312 -3.2072252076 -3.2466003729 0 0 -2.8614199359 -2.8082158999 0
45 6848 7968 28352 3200 1152 0.057 0.060 0.037 0.047 0.039 0 0 0.4492099323 0 0 0 0 0 0 0 0.264671028 0.2957 0.2576124153 0.404416 0.3687 8.8317119178 8.9831887993 10.2524528534 8.0709060888 7.0492548413 -2.8614199359 -2.8082158999 -3.2875897174 -3.063609696 -3.2466003729 0 0 -2.8614199359 -2.8082158999 0
46 7424 7296 3712 11904 3072 0.057 0.060 0.044 0.047 0.039 0 0 0 0 0 0 0 0 0 0 0.2609827586 0.2957 0.363762069 0.4125806452 0.3747 8.9124732745 8.8950815318 8.2193260939 9.3846297571 8.0300840943 -2.8614199359 -2.8082158999 -3.1150790312 -3.063609696 -3.2466003729 0 0 -2.8614199359 -2.8082158999 0
47 6848 7200 11136 21376 4672 0.057 0.060 0.044 0.037 0.033 0 0 0 0.4580838323 0 0 0 0 1 1 0.2598 0.2957 0.3653954023 0.2076904192 0.255 8.8317119178 8.881836305 9.3179383826 9.9700240763 8.4493425245 -2.8614199359 -2.8082158999 -3.1150790312 -3.2875897174 -3.4217190174 0 0 -2.8614199359 -2.8082158999 0
48 7488 7968 35904 3648 1920 0.057 0.060 0.037 0.047 0.033 0 0 0.3707664884 0 0 0 0 1 0 0 0.2593 0.2957 0.2215565062 0.3441684211 0.2569 8.9210570182 8.9831887993 10.4886039889 8.2019343512 7.560080465 -2.8614199359 -2.8082158999 -3.2875897174 -3.063609696 -3.4217190174 0 0 -2.8614199359 -2.8082158999 0
49 6336 7200 2368 46848 896 0.057 0.060 0.037 0.031 0.033 0 0 1 0.8333333333 1 0 0 0 1 0 0.259 0.33858 0.2464864865 0.0136903005 0.2478 8.7540029335 8.881836305 7.769800996 10.7546635973 6.797940413 -2.8614199359 -2.8082158999 -3.2875897174 -3.4707484446 -3.4217190174 0 0 -2.8614199359 -2.8082158999 0
50 6208 11712 45824 3328 1152 0.057 0.060 0.031 0.047 0.033 0 1 0.4120111732 0 1 0 1 1 0 0 0.259 0.3820918033 0.1515786313 0.3514961538 0.2512 8.7335940619 9.3683692362 10.7325632503 8.1101268019 7.0492548413 -2.8614199359 -2.8082158999 -3.4707484446 -3.063609696 -3.4217190174 1 1 -2.8614199359 -2.8082158999 1
51 6400 10368 3008 4096 26432 0.057 0.060 0.044 0.047 0.030 0 1 0 0.40625 1 0 0 0 0 1 0.259 0.37305 0.3957574468 0.3526828125 0.2 8.7640532693 9.2464794186 8.0090306851 8.3177661667 10.1823306763 -2.8614199359 -2.8082158999 -3.1150790312 -3.063609696 -3.5223062543 0 1 -2.8614199359 -2.8082158999 0
52 13056 7104 2816 4864 7168 0.051 0.060 0.044 0.047 0.030 0.2450980392 0.7837837838 0 0.1447368421 1 0 0 1 0 0 0.2711009804 0.4146027027 0.4381636364 0.3966815789 0.2069 9.4770030772 8.8684132847 7.9430727173 8.4896164236 8.8773819547 -2.9679955186 -2.8082158999 -3.1150790312 -3.063609696 -3.5223062543 0 0.7837837838 -2.9679955186 -2.8082158999 0
53 8704 6432 3456 36288 7680 0.051 0.060 0.044 0.034 0.030 1 0 0 1 1 0 0 0 1 0 0.2707220588 0.3222940299 0.4151944444 0.1515511464 0.2069 9.0715379691 8.7690408109 8.1478671299 10.4992423871 8.9463748261 -2.9679955186 -2.8082158999 -3.1150790312 -3.3749815395 -3.5223062543 0 0 -2.9679955186 -2.8082158999 0
54 8832 7200 46528 3072 6912 0.051 0.060 0.037 0.047 0.028 1 0 0.3535075653 0 0 0 0 1 0 0 0.2708391304 0.304744 0.2207196699 0.3787 0.1626 9.0861367685 8.881836305 10.7478095609 8.0300840943 8.8410143105 -2.9679955186 -2.8082158999 -3.2875897174 -3.063609696 -3.5766674635 0 0 -2.9679955186 -2.8082158999 0
55 5696 10176 9792 4864 32704 0.057 0.060 0.037 0.047 0.028 0 0 0.6274509804 0 1 0 0 0 0 1 0.3446089888 0.29985 0.2203921569 0.378725 0.1603 8.6475194531 9.2277872856 9.1893210048 8.4896164236 10.3952526736 -2.8614199359 -2.8082158999 -3.2875897174 -3.063609696 -3.5766674635 0 0 -2.8614199359 -2.8082158999 0
56 14208 6144 2240 9152 7232 0.051 0.060 0.044 0.047 0.028 1 0 0 0 1 1 0 0.9587142857 0 0 0.2706054054 0.29018293 0.3317742857 0.3792657343 0.1598 9.5615604652 8.7232312748 7.7142311448 9.1217277136 8.8862709021 -2.9679955186 -2.8175614592 -3.1150790312 -3.063609696 -3.5766674635 0 0 -2.9679955186 -2.8175614592 0
57 7616 8640 1280 42944 1152 0.051 0.060 0.044 0.031 0.039 0.3781512605 0 0 1 0 0 0 0 1 0 0.2706134454 0.2880440303 0.33324 0.063552608 0.4763 8.9380065765 9.0641578618 7.1546153569 10.6676522203 7.0492548413 -2.9679955186 -2.8193220342 -3.1150790312 -3.4707484446 -3.2466003729 0 0 -2.9679955186 -2.8193220342 0
58 5632 18912 2432 4032 65536 0.055 0.048 0.044 0.045 0.019 1 1 0 0 0 0 1 0 0 1 0.2899147727 0.168580203 0.3507394737 0.3500030727 0.0298 8.6362198978 9.8475519202 7.7964692431 8.3020178098 11.090354889 -2.9032670459 -3.0404681674 -3.1150790312 -3.1057614237 -3.9437717037 1 1 -2.9032670459 -3.0404681674 1
59 6592 6720 38208 4032 1024 0.055 0.057 0.031 0.047 0.025 0.3495145631 0 0.4539363484 0 1 0 0 1 0 0 0.3033815534 0.30612 0.1489690117 0.3932746032 0.1956 8.7936120716 8.8128434335 10.5508001968 8.3020178098 6.9314718056 -2.9032670459 -2.8614199359 -3.4707484446 -3.063609696 -3.6951490671 0 0 -2.9032670459 -2.8614199359 0
60 7680 8736 1728 36800 23872 0.055 0.057 0.044 0.031 0.025 0 0 0 1 0 0 0 0 1 1 0.3028525 0.3067307692 0.4134148148 0.1761836522 0.1956 8.9463748261 9.075207698 7.4547199494 10.5132531242 10.080461503 -2.9032670459 -2.8614199359 -3.1150790312 -3.4707484446 -3.6951490671 0 0 -2.9032670459 -2.8614199359 0
61 6720 9216 11904 4224 43328 0.055 0.057 0.031 0.047 0.020 0.6761904762 0 1 0 1 0 0 1 0 0 0.3034580952 0.3066979167 0.1526870968 0.4499348485 0.0085 8.8128434335 9.1286963829 9.3846297571 8.3485378254 10.6765543563 -2.9032670459 -2.8614199359 -3.4707484446 -3.063609696 -3.904240865 0 0 -2.9032670459 -2.8614199359 0
62 10432 12576 4672 25600 59648 0.057 0.057 0.044 0.031 0.015 0 0 0 0.7775 0 0 0 0 1 0 0.2919343558 0.2767923664 0.3903041096 0.16880525 0.2919 9.2526332842 9.4395455147 8.4493425245 10.1503476305 10.996215898 -2.8614199359 -2.8614199359 -3.1150790312 -3.4707484446 -4.1689334192 0 0 -2.8614199359 -2.8614199359 0
63 13824 7008 6208 10176 16320 0.047 0.057 0.036 0.031 0.025 1 0 1 1 0 0 0 0 0 1 0.1973 0.2946383562 0.2438072165 0.168490566 0.3333 9.534161491 8.8548076326 8.7335940619 9.2277872856 9.7001466285 -3.063609696 -2.8614199359 -3.3303312658 -3.4707484446 -3.6951490671 0 0 -3.063609696 -2.8614199359 0
65 7872 9504 2048 29312 6080 0.053 0.053 0.041 0.031 0.034 0 0 0 0.5807860262 0 0 0 0 1 0 0.3197617886 0.3935363636 0.4756927954 0.1401421397 0.516 8.9710674387 9.1594680416 7.6246189862 10.2857522675 8.712759975 -2.938053162 -2.9370703615 -3.1974261478 -3.4707484446 -3.3749815395 0 0 -2.938053162 -2.9370703615 0
66 8064 27936 3392 13440 32832 0.053 0.042 0.038 0.031 0.022 0 0.7697594502 0 1 1 0 0 0 0 1 0.3155666667 0.1542116838 0.4569132075 0.1372738095 0.2381 8.9951649903 10.2376714586 8.1291749969 9.5059906141 10.3991589285 -2.938053162 -3.1805569606 -3.2587217334 -3.4707484446 -3.8295793362 0 0.7697594502 -2.938053162 -3.1805569606 0
67 23872 16032 20032 15296 1856 0.037 0.042 0.031 0.031 0.034 0 0.7425149701 0.4856230032 0.8451882845 0 0 0 1 0 0 0.2519613941 0.1397365269 0.3291811502 0.1458188285 0.5164 10.080461503 9.6823420039 9.9050862739 9.6353466353 7.5261789133 -3.2875897174 -3.1805569606 -3.4707484446 -3.4707484446 -3.3749815395 0 0.7425149701 -3.2875897174 -3.1805569606 0
68 13760 9408 54272 11008 1152 0.037 0.053 0.026 0.031 0.034 1 0 1 1 0 0 0 1 0 0 0.1702567442 0.3491734694 0.0740643868 0.1398918605 0.5164 9.5295211115 9.1493156701 10.9017637192 9.3063775602 7.0492548413 -3.2875897174 -2.9370703615 -3.6341545544 -3.4707484446 -3.3749815395 0 0 -3.2875897174 -2.9370703615 0
71 10368 14784 39488 14976 1792 0.040 0.053 0.026 0.038 0.034 1 0 1 0.1581196581 0 0 0 1 0 0 0.3095111111 0.2642175325 0.11590859 0.3058286325 0.484 9.2464794186 9.6013007939 10.5837521073 9.6142041987 7.4910875935 -3.2072252076 -2.9370703615 -3.6341545544 -3.2587217334 -3.3749815395 0 0 -3.2072252076 -2.9370703615 0
72 4608 18720 4608 37312 6336 0.053 0.037 0.026 0.031 0.034 0 0.7487179487 0.5555555556 1 0 0 1 0 0.994813036 0 0.4728791667 0.1280107692 0.1009666667 0.2465567753 0.5297 8.4355492024 9.83734775 8.4355492024 10.5270702697 8.7540029335 -2.938053162 -3.2861959899 -3.6341545544 -3.4707484446 -3.3749815395 1 0.7487179487 -2.938053162 -3.2861959899 1
73 7104 9888 2688 6528 36288 0.053 0.037 0.038 0.038 0.023 0 0.3980582524 0 0 0 0 0 0 0 1 0.4562540541 0.121323301 0.3776571429 0.3620401961 0.2544 8.8684132847 9.1990771797 7.8965527016 8.7838558966 10.4992423871 -2.938053162 -3.2861959899 -3.2587217334 -3.2587217334 -3.7601069634 0 0.3980582524 -2.938053162 -3.2861959899 0
74 34432 12864 3136 6208 6080 0.039 0.037 0.038 0.038 0.023 0.3680297398 1 0 1 1 0 0 1 0 0 0.3165 0.1142768657 0.378 0.3472824742 0.2497 10.4467416435 9.4621879914 8.0507033815 8.7335940619 8.712759975 -3.2466003729 -3.2861959899 -3.2587217334 -3.2792563359 -3.7601069634 0 1 -3.2466003729 -3.2861959899 0
75 6592 8736 60928 4416 1728 0.053 0.053 0.023 0.038 0.034 0 0 1 1 0 0 0 1 0 0 0.4861592233 0.4181582418 0.0095271008 0.3351101449 0.4863 8.7936120716 9.075207698 11.0174481182 8.392989588 7.4547199494 -2.938053162 -2.9370703615 -3.7601069634 -3.2792563359 -3.3749815395 0 0 -2.938053162 -2.9370703615 0
76 7296 9600 12800 9216 2880 0.053 0.053 0.023 0.038 0.034 0 0 1 0.8819444444 0 0 0 0 0 0 0.4824035088 0.34725 0.00079 0.3076743056 0.4521 8.8950815318 9.1695183775 9.4572004499 9.1286963829 7.9655455731 -2.938053162 -2.9370703615 -3.7601069634 -3.2792563359 -3.3749815395 0 0 -2.938053162 -2.9370703615 0
77 7552 9504 2496 25664 4544 0.053 0.053 0.038 0.031 0.031 0 0 0 1 1 0 0 0 1 1 0.4819898305 0.3287141414 0.4298153846 0.1458870324 0.4251 8.9295677078 9.1594680416 7.8224447295 10.1528445107 8.4215629604 -2.938053162 -2.9370703615 -3.2587217334 -3.4707484446 -3.4707484446 0 0 -2.938053162 -2.9370703615 0
78 6976 10080 2688 5184 67904 0.053 0.053 0.038 0.038 0.017 0.3394495413 0 0 0 0 0 0 0 0 1 0.4670770642 0.3184333333 0.417 0.3307 0.0376 8.8502309656 9.2183085416 7.8965527016 8.553332238 11.125850222 -2.938053162 -2.9370703615 -3.2587217334 -3.2587217334 -4.0727053871 0 0 -2.938053162 -2.9370703615 0
79 6720 10368 39104 15232 2048 0.053 0.049 0.028 0.038 0.034 0 0 0.4451718494 0 0 0 0 1 0 0 0.4330285714 0.2633444444 0.1535600655 0.3594386555 0.4849 8.8128434335 9.2464794186 10.5739800425 9.631153757 7.6246189862 -2.938053162 -3.0062035727 -3.5766674635 -3.2587217334 -3.3749815395 0 0 -2.938053162 -3.0062035727 0
80 5824 9312 1984 86912 2688 0.053 0.049 0.038 0.026 0.034 0.3186813187 0 0 1 0 0 0 0 1 0 0.4517087912 0.2615783505 0.3704870968 0.0888774669 0.5027 8.6697425899 9.13905917 7.5928702878 11.3726513915 7.8965527016 -2.938053162 -3.0062035727 -3.2587217334 -3.6341545544 -3.3749815395 0 0 -2.938053162 -3.0062035727 0
81 96064 7584 1536 19520 8896 0.026 0.049 0.038 0.026 0.025 1 0 0 1 0 1 0 0 0 1 0.042905996 0.3043873418 0.3726 0.1120301639 0.3497 11.472769915 8.9337960439 7.3369369137 9.87919486 9.0933570165 -3.6341545544 -3.0062035727 -3.2587217334 -3.6341545544 -3.6951490671 0 0 -3.6341545544 -3.0062035727 0
82 11072 7584 1984 5440 9408 0.053 0.049 0.037 0.038 0.025 0 0 0 0.8470588235 1 0 0 0 1 0 0.4648572254 0.3148455696 0.3429322581 0.3742517647 0.3516 9.3121746779 8.9337960439 7.5928702878 8.6015343398 9.1493156701 -2.938053162 -3.0062035727 -3.3044677552 -3.2587217334 -3.6951490671 0 0 -2.938053162 -3.0062035727 0
83 55808 6528 832 8320 15680 0.031 0.049 0.037 0.033 0.025 1 0 1 0.1307692308 1 1 0 0 0 0 0.1465107798 0.3177352941 0.3450615385 0.2258069231 0.3516 10.9296725073 8.7838558966 6.7238324408 9.0264175338 9.6601412939 -3.4707484446 -3.0062035727 -3.3044677552 -3.4217190174 -3.6951490671 0 0 -3.4707484446 -3.0062035727 0
84 12928 11232 1472 7424 83008 0.053 0.045 0.037 0.033 0.015 0 0.2393162393 1 1 1 0 0.9772649573 0 0 1 0.4860475248 0.2440094017 0.3489 0.2308465517 0.0303 9.4671507808 9.3265221263 7.2943772993 8.9124732745 11.3266922677 -2.938053162 -3.1080614585 -3.3044677552 -3.4217190174 -4.1689334192 0.9772649573 0.2393162393 -2.938053162 -3.1080614585 0.9772649573
85 68032 6048 1344 6784 1536 0.031 0.049 0.037 0.033 0.034 1 0 1 0.320754717 0 1 0 0 0 1 0.0271874882 0.2965666667 0.3489 0.2480103774 0.5365 11.1277334617 8.7074829179 7.2034055211 8.8223221775 7.3369369137 -3.4707484446 -3.0062035727 -3.3044677552 -3.4217190174 -3.3749815395 0 0 -3.4707484446 -3.0062035727 0
86 26112 6912 2112 21440 1600 0.031 0.049 0.037 0.033 0.034 1 0 1 0.776119403 0 0 0 0 0 0 0.0156612745 0.2968194444 0.3489 0.2372883582 0.5187 10.1701502578 8.8410143105 7.6553906448 9.9730136152 7.3777589082 -3.4707484446 -3.0062035727 -3.3044677552 -3.4217190174 -3.3749815395 0 0 -3.4707484446 -3.0062035727 0
87 11776 10272 1600 132224 1344 0.053 0.049 0.038 0.022 0.034 0 0 0.44 0.5333978703 0 0 0 0 1 0 0.4220168478 0.2964009346 0.378 0.0642841239 0.4849 9.373818841 9.2371770259 7.3777589082 11.792252733 7.2034055211 -2.938053162 -3.0062035727 -3.2587217334 -3.8295793362 -3.3749815395 0 0 -2.938053162 -3.0062035727 0
88 34048 6816 960 5120 3456 0.036 0.049 0.038 0.035 0.034 1 0 0 0 0 1 0 0 0 0 0.1546859023 0.2849309859 0.378 0.42803125 0.4849 10.4355265727 8.8270280685 6.8669332845 8.540909718 8.1478671299 -3.3303312658 -3.0062035727 -3.2587217334 -3.3435182701 -3.3749815395 0 0 -3.3303312658 -3.0062035727 0
89 14848 17184 1408 5696 30976 0.053 0.042 0.037 0.035 0.025 0 1 0 0 1 0 0 0 0 1 0.4240969828 0.2885636872 0.3489 0.4277393258 0.3597 9.605620455 9.7517339973 7.2499255367 8.6475194531 10.3409679901 -2.938053162 -3.1805569606 -3.3044677552 -3.3435182701 -3.6951490671 0 1 -2.938053162 -3.1805569606 0
90 15424 10560 704 5440 4416 0.053 0.042 0.037 0.035 0.025 0.5186721992 0.3454545455 0 0 0 0 0 0 0 0 0.4239647303 0.2453636364 0.3489 0.4215541176 0.3604 9.6436800169 9.2648285573 6.5567783562 8.6015343398 8.392989588 -2.938053162 -3.1805569606 -3.3044677552 -3.3435182701 -3.6951490671 0 0.3454545455 -2.938053162 -3.1805569606 0
91 64000 8064 1664 3520 2688 0.031 0.049 0.037 0.035 0.033 1 0.7857142857 0 0 0 0 0 1 0 0 0.017206 0.3362571429 0.3489 0.4022872727 0.5134 11.0666383623 8.9951649903 7.4169796214 8.1662162686 7.8965527016 -3.4707484446 -3.0062035727 -3.3044677552 -3.3435182701 -3.4217190174 0 0.7857142857 -3.4707484446 -3.0062035727 0
92 14528 8160 14464 19584 13376 0.031 0.049 0.028 0.026 0.022 1 0.7411764706 1 0.9117647059 1 0 0 1 1 1 0.03250837 0.3066035294 0.1307 0.0546745098 0.2787 9.5838331008 9.006999448 9.5794180826 9.8824681853 9.5012173353 -3.4707484446 -3.0062035727 -3.5935692743 -3.6341545544 -3.815293379 0 0.7411764706 -3.4707484446 -3.0062035727 0
93 7040 9408 2368 7040 17856 0.053 0.049 0.037 0.035 0.020 0.4272727273 0 0 0.2272727273 1 0 0 0 0 0 0.3939309091 0.3082204082 0.3489 0.3121427273 0.2116 8.8593634492 9.1493156701 7.769800996 8.8593634492 9.7900948652 -2.938053162 -3.0062035727 -3.3044677552 -3.3435182701 -3.904240865 0 0 -2.938053162 -3.0062035727 0
94 15168 16992 2304 39296 4096 0.053 0.047 0.037 0.026 0.030 0.4978902954 0 0 1 0 0 1 0 1 0 0.3938037975 0.2780011299 0.3489 0.1077245928 0.4423 9.6269432245 9.740497924 7.7424020218 10.5788780115 8.3177661667 -2.938053162 -3.0624954904 -3.3044677552 -3.6341545544 -3.5223062543 1 0 -2.938053162 -3.0624954904 1
95 53056 7968 1024 11264 2496 0.031 0.047 0.037 0.026 0.030 0.4197828709 0.6265060241 1 1 0 0 0 1 0 0 0.1357 0.279660241 0.3489 0.06573125 0.3947 10.8791032385 8.9831887993 6.9314718056 9.3293670784 7.8224447295 -3.4707484446 -3.0624954904 -3.3044677552 -3.6341545544 -3.5223062543 0 0.6265060241 -3.4707484446 -3.0624954904 0
96 9536 11232 2496 4864 14976 0.031 0.049 0.037 0.035 0.025 1 0 0 0.6842105263 0 0 0 0 0 1 0.1179221477 0.314025641 0.3489 0.3041315789 0.3321 9.1628293893 9.3265221263 7.8224447295 8.4896164236 9.6142041987 -3.4707484446 -3.0062035727 -3.3044677552 -3.3435182701 -3.6951490671 0 0 -3.4707484446 -3.0062035727 0
97 11904 21120 11328 5184 6464 0.031 0.039 0.037 0.035 0.025 0.6612903226 0.3409090909 0 0 0 0 1 0 0 0 0.0799887097 0.1044068182 0.3489 0.2708740741 0.3327 9.3846297571 9.9579757378 9.3350328159 8.553332238 8.7740036002 -3.4707484446 -3.2319821715 -3.3044677552 -3.3435182701 -3.6951490671 1 0.3409090909 -3.4707484446 -3.2319821715 1
98 11136 13728 56704 3392 3648 0.053 0.039 0.031 0.035 0.030 0 1 1 0 0 0.5862068966 0 1 0 0 0.3746 0.1304517483 0.2418038375 0.2636886792 0.4386 9.3179383826 9.5271928217 10.945600034 8.1291749969 8.2019343512 -2.938053162 -3.2319821715 -3.4707484446 -3.3435182701 -3.5223062543 0 1 -2.938053162 -3.2319821715 0
99 37568 6912 1472 3712 52928 0.034 0.049 0.037 0.035 0.015 1 0.6666666667 0 0 1 0 0 1 0 1 0.032 0.3087333333 0.4158043478 0.2625551724 0.0273 10.5339079032 8.8410143105 7.2943772993 8.2193260939 10.8766877784 -3.3749815395 -3.0062035727 -3.3044677552 -3.3435182701 -4.1689334192 0 0.6666666667 -3.3749815395 -3.0062035727 0
100 13376 8352 4800 3904 24448 0.034 0.049 0.037 0.035 0.015 1 0.3103448276 0 0 0 0 0 0 0 0 0.032 0.3108482759 0.415716 0.2624114754 0.0253 9.5012173353 9.0302563101 8.4763711969 8.2697569475 10.104303692 -3.3749815395 -3.0062035727 -3.3044677552 -3.3435182701 -4.1689334192 0 0.3103448276 -3.3749815395 -3.0062035727 0
101 6144 12576 5696 8960 20800 0.053 0.049 0.037 0.035 0.023 0 0 0 0 0 0 0 0 0 1 0.3746 0.3109435115 0.4162404494 0.26154 0.2933 8.7232312748 9.4395455147 8.6475194531 9.100525506 9.9427082657 -2.938053162 -3.0062035727 -3.3044677552 -3.3435182701 -3.7601069634 0 0 -2.938053162 -3.0062035727 0
102 7104 13248 2496 83072 5312 0.053 0.049 0.037 0.020 0.023 0.4324324324 0 0 0.7118644068 1 0 0 0 1 0 0.3746 0.277726087 0.4166 0.1516619414 0.2913 8.8684132847 9.4916018766 7.8224447295 11.3274629806 8.5777236912 -2.938053162 -3.0062035727 -3.3044677552 -3.904240865 -3.7601069634 0 0 -2.938053162 -3.0062035727 0
103 5696 17856 23872 4160 15104 0.053 0.046 0.031 0.035 0.022 0 0.2849462366 1 0.2615384615 1 0 1 1 0 0 0.3620573034 0.2614827957 0.3345359249 0.3863723077 0.2403 8.6475194531 9.7900948652 10.080461503 8.3332703533 9.6227148884 -2.938053162 -3.0850189637 -3.4707484446 -3.3435182701 -3.8295793362 1 0.2849462366 -2.938053162 -3.0850189637 1
104 63168 10080 2880 4928 13120 0.031 0.046 0.037 0.035 0.022 0.600810537 0 0.4444444444 0.5194805195 1 1 0 0.4444444444 0 0 0.0596734549 0.2642 0.4255222222 0.388938961 0.2403 11.0535531228 9.2183085416 7.9655455731 8.5026885052 9.4818930625 -3.4707484446 -3.0850189637 -3.3044677552 -3.3435182701 -3.8295793362 0 0 -3.4707484446 -3.0850189637 0
105 7680 11136 6208 34304 12288 0.053 0.049 0.031 0.031 0.022 0 0 1 1 1 0 0 0 1 0 0.3757625 0.32 0.3213319588 0.3109358209 0.2403 8.9463748261 9.3179383826 8.7335940619 10.4430172444 9.4163784554 -2.938053162 -3.0062035727 -3.4707484446 -3.4707484446 -3.8295793362 0 0 -2.938053162 -3.0062035727 0
106 11136 10560 48256 6720 8064 0.043 0.049 0.026 0.030 0.025 0 0 0 0 0 1 0 1 0 1 0.2247940483 0.30131 0.2132233422 0.2931676296 0.3006 9.3179383826 9.2648285573 10.7842754514 8.8128434335 8.9951649903 -3.1539147157 -3.0062035727 -3.6341545544 -3.4901142908 -3.6951490671 0 0 -3.1539147157 -3.0062035727 0
107 8384 11520 12160 12608 9728 0.050 0.049 0.027 0.029 0.025 0 0 1 1 1 0 0 0 0 0 0.3392601828 0.2922383333 0.2467276636 0.2529971312 0.2993 9.0340804066 9.3518399342 9.4059071555 9.4420868121 9.1827636042 -2.9937697106 -3.0062035727 -3.597784966 -3.5432376737 -3.6951490671 0 0 -2.9937697106 -3.0062035727 0
108 7616 12192 2560 11456 9984 0.046 0.049 0.036 0.028 0.024 0 0 0 1 1 0 0 0 0 0 0.2861 0.2689480315 0.420275801 0.2144988827 0.2766 8.9380065765 9.4085352779 7.8477625375 9.3462688892 9.2087390906 -3.0703211305 -3.0062035727 -3.3318652417 -3.5766674635 -3.7271006669 0 0 -3.0703211305 -3.0062035727 0
109 9216 11136 3584 9408 28608 0.046 0.049 0.035 0.030 0.023 1 0 1 1 1 0 0 0 0 1 0.2789 0.3009310345 0.412125 0.2318063501 0.2516 9.1286963829 9.3179383826 8.1842347741 9.1493156701 10.261441678 -3.080473502 -3.0062035727 -3.3479528671 -3.5171417391 -3.7601069634 0 0 -3.080473502 -3.0062035727 0
110 8512 10176 2560 192128 5760 0.046 0.049 0.035 0.026 0.024 1 0 1 1 1 0 0 0 1 0 0.2832954887 0.3061851965 0.365985 0.1146248168 0.2664 9.0492322116 9.2277872856 7.8477625375 12.1659170956 8.6586927537 -3.080473502 -3.0220747438 -3.3479528671 -3.6341545544 -3.7401727485 0 0 -3.080473502 -3.0220747438 0
111 8128 14976 2816 9216 3840 0.046 0.048 0.035 0.035 0.030 1 1 1 0 0 0 1 0 0 0 0.281992126 0.296 0.3252681818 0.3197270833 0.41 9.0030701698 9.6142041987 7.9430727173 9.1286963829 8.2532276456 -3.080473502 -3.0404681674 -3.3479528671 -3.3435182701 -3.5223062543 1 1 -3.080473502 -3.0404681674 1
112 8960 10464 2496 48704 2048 0.046 0.048 0.035 0.023 0.028 1 1 1 1 0 0 0 0 0 0 0.2801714286 0.3054308288 0.3553358974 0.0057743758 0.4071 9.100525506 9.2556960737 7.8224447295 10.7935164412 7.6246189862 -3.080473502 -3.0270067783 -3.3479528671 -3.7601069634 -3.5600465823 0 1 -3.080473502 -3.0270067783 0
113 13248 9408 71232 4544 3392 0.046 0.049 0.031 0.035 0.026 1 0 1 0 1 0 0 0 0 1 0.2898811594 0.3197 0.2668530099 0.3310629705 0.3721 9.4916018766 9.1493156701 11.1736974346 8.4215629604 8.1291749969 -3.073693815 -3.0062035727 -3.4808495406 -3.3594847701 -3.6341545544 0 0 -3.073693815 -3.0062035727 0
114 10624 12384 49536 8000 3968 0.047 0.049 0.031 0.032 0.027 0 0 1 0 1 0 0 1 0 0 0.364753012 0.3072348837 0.2706874677 0.2837050214 0.382 9.2708708717 9.4241605958 10.8104549569 8.9871968207 8.2860174684 -3.063609696 -3.0062035727 -3.4808495406 -3.4292045751 -3.6107616748 0 0 -3.063609696 -3.0062035727 0
115 57408 11040 4160 11136 4352 0.031 0.049 0.033 0.031 0.030 1 0 0 1 0 1 0 0.7293777135 0 0 0.131861204 0.3130669565 0.3259008683 0.2455293103 0.4253 10.9579389454 9.3092803198 8.3332703533 9.3179383826 8.3783907885 -3.4707484446 -3.0062035727 -3.4041412917 -3.4707484446 -3.5223062543 0 0 -3.4707484446 -3.0062035727 0
116 11200 10176 4864 10496 19264 0.043 0.049 0.031 0.031 0.023 1 0 1 1 0 0 0 0 0 1 0.3852461868 0.3231915094 0.2776276316 0.2144658537 0.2586 9.3236690573 9.2277872856 8.4896164236 9.2587495112 9.8659933481 -3.1564428274 -3.0062035727 -3.4707484446 -3.4707484446 -3.7534179753 0 0 -3.1564428274 -3.0062035727 0
117 8576 19776 6336 7360 30784 0.047 0.042 0.031 0.032 0.025 0 1 1 1 1 0 0 0 0 0 0.4398402985 0.2621 0.2795515152 0.2168147786 0.271 9.0567228833 9.8922243603 8.7540029335 8.9038152117 10.3347503535 -3.063609696 -3.1805569606 -3.4707484446 -3.4267072112 -3.6951490671 0 1 -3.063609696 -3.1805569606 0
118 8704 14976 5696 4608 31040 0.047 0.044 0.029 0.035 0.025 0 1 1 0 1 0 0 0 0 1 0.4397823529 0.317458099 0.2575246595 0.2694 0.2377 9.0715379691 9.6142041987 8.6475194531 8.4355492024 10.3430319743 -3.063609696 -3.1285726136 -3.540044897 -3.3435182701 -3.6951490671 0 1 -3.063609696 -3.1285726136 0
119 12032 12960 41280 6272 9856 0.043 0.049 0.026 0.033 0.025 0 0 1 0 1 0 0 1 0 0 0.3897423189 0.38832 0.2188172093 0.2842301624 0.2856 9.3953250462 9.4696229699 10.6281334002 8.743850562 9.1958356858 -3.1493313016 -3.0062035727 -3.6341545544 -3.4056257715 -3.6888794541 0 0 -3.1493313016 -3.0062035727 0
120 19008 12288 22912 17216 20672 0.037 0.049 0.027 0.031 0.026 1 0 1 1 1 0 0 0 1 1 0.2985612795 0.3434265625 0.2379573593 0.2294550186 0.2597 9.8526152222 9.4163784554 10.0394160698 9.753594463 9.9365354066 -3.2875897174 -3.0062035727 -3.6108217923 -3.4707484446 -3.6341545544 0 0 -3.2875897174 -3.0062035727 0
121 18432 12192 22272 18944 11392 0.037 0.049 0.024 0.031 0.026 1 0 0 1 1 0 0 0 0 0 0.2819273809 0.3307685039 0.1767354727 0.2290476351 0.2408 9.8218435635 9.4085352779 10.0110855631 9.8492425377 9.3406666337 -3.3059304182 -3.0062035727 -3.7235044398 -3.4707484446 -3.6341545544 0 0 -3.3059304182 -3.0062035727 0
122 16576 10944 63360 7424 2304 0.036 0.049 0.023 0.034 0.027 1 0 1 1 1 0 0 1 0 0 0.238469112 0.3272192982 0.1656914141 0.2128843504 0.2497 9.7157111451 9.3005466399 11.0565880265 8.9124732745 7.7424020218 -3.3303312658 -3.0062035727 -3.7601069634 -3.3946533059 -3.6223897128 0 0 -3.3303312658 -3.0062035727 0
123 10304 11520 32448 14208 4928 0.039 0.049 0.024 0.039 0.028 1 0 1 0 0 0 0 0 0.04 0 0.2777956522 0.321 0.1381212992 0.3148725225 0.2824 9.2402874483 9.3518399342 10.387394087 9.5615604652 8.5026885052 -3.2466003729 -3.0062035727 -3.7211850951 -3.2466003729 -3.5879035368 0 0 -3.2466003729 -3.0062035727 0
124 6272 18240 8448 11392 15424 0.047 0.042 0.037 0.039 0.023 0 1 0 0 1 0 1 0 1 0 0.3980469388 0.1909 0.4196386364 0.292191573 0.136 8.743850562 9.8113722636 9.0416850059 9.3406666337 9.6436800169 -3.063609696 -3.1805569606 -3.3044677552 -3.2466003729 -3.7736206826 1 1 -3.063609696 -3.1805569606 1
125 9536 16704 12736 4160 65280 0.047 0.042 0.025 0.040 0.020 0 1 0 0 1 0 1 0 0 0 0.3980557047 0.1907 0.2276152967 0.3137636342 0.0077 9.1628293893 9.7234034907 9.4521879081 8.3332703533 11.0864409896 -3.063609696 -3.1805569606 -3.6715393111 -3.2220211688 -3.904240865 1 1 -3.063609696 -3.1805569606 1
126 8448 9984 81792 5184 10880 0.047 0.044 0.023 0.037 0.021 0 1 1 0 1 0 0 1 0 0 0.4232712121 0.2362026564 0.1705380282 0.2600569235 0.0106 9.0416850059 9.2087390906 11.3119347183 8.553332238 9.2946815204 -3.063609696 -3.1226681933 -3.7601069634 -3.2978642447 -3.8812513468 0 1 -3.063609696 -3.1226681933 0
127 34240 12480 4800 6400 17536 0.031 0.049 0.027 0.031 0.017 1 0 1 1 0 1 0 0 0 0 0.1971308411 0.3202 0.2249414365 0.13208 0.2887 10.4411498303 9.4318826419 8.4763711969 8.7640532693 9.7720111897 -3.4707484446 -3.0062035727 -3.6029045271 -3.4707484446 -4.0912244349 0 0 -3.4707484446 -3.0062035727 0
128 7040 29952 1664 123968 7296 0.047 0.049 0.037 0.031 0.015 0 0 0 1 1 0 1 0 1 0 0.4656254545 0.3171487179 0.2446461538 0.13289143 0.4 8.8593634492 10.3073513793 7.4169796214 11.7277787468 8.8950815318 -3.063609696 -3.0062035727 -3.3044677552 -3.4707484446 -4.1689334192 1 0 -3.063609696 -3.0062035727 1
129 74752 8352 1216 14400 4480 0.031 0.049 0.037 0.033 0.019 1 0 0 1 1 1 0 0 0 0 0.1970994863 0.2832781609 0.3059210526 0.1689427815 0.1024 11.2219312467 9.0302563101 7.1033220625 9.5749834856 8.4073783254 -3.4707484446 -3.0062035727 -3.3044677552 -3.4257802997 -3.951868914 0 0 -3.4707484446 -3.0062035727 0
130 41792 11040 2304 7744 58496 0.028 0.049 0.037 0.036 0.022 1 0 0 0 1 0 0 0 0 1 0.1074646248 0.3538173913 0.2398472222 0.2276473862 0.0604 10.6404602126 9.3092803198 7.7424020218 8.954673629 10.9767136548 -3.5766674635 -3.0062035727 -3.3044677552 -3.3242515073 -3.8295793362 0 0 -3.5766674635 -3.0062035727 0
131 83072 9408 896 18560 8384 0.028 0.049 0.040 0.031 0.023 1 0 0 1 1 0 0 0 1 0 0.0454966102 0.3661714286 0.2694394678 0.0392003448 0.0652 11.3274629806 9.1493156701 6.797940413 9.8287640063 9.0340804066 -3.5766674635 -3.0062035727 -3.2120911057 -3.4707484446 -3.7942399698 0 0 -3.5766674635 -3.0062035727 0
132 35712 10464 4096 7040 34432 0.030 0.049 0.035 0.034 0.025 1 0 0 1 1 0 0 0 0 0 0.1077059352 0.2913862385 0.1154129392 0.1018223139 0.1509 10.4832420457 9.2556960737 8.3177661667 8.8593634492 10.4467416435 -3.5094867623 -3.0062035727 -3.3517326993 -3.3932401496 -3.6951490671 0 0 -3.5094867623 -3.0062035727 0
133 21760 8736 171264 5312 5696 0.043 0.049 0.031 0.037 0.026 0 0 1 0 1 0 0 1 0 0 0.2276480778 0.2712637363 0.0017113229 0.1807493689 0.197 9.987828701 9.075207698 12.0509615046 8.5777236912 8.6475194531 -3.1489643887 -3.0062035727 -3.4707484446 -3.3081328396 -3.6460594569 0 0 -3.1489643887 -3.0062035727 0
134 39296 10656 5184 22976 3648 0.039 0.048 0.032 0.031 0.030 1 0 1 1 0 1 0 0 1 0 0.152 0.2550700053 0.0252405157 0.1793348189 0.2925 10.5788780115 9.2738783928 8.553332238 10.0422054718 8.2019343512 -3.2425923515 -3.0457750533 -3.4459107283 -3.4707484446 -3.5223062543 0 0 -3.2425923515 -3.0457750533 0
135 11904 17952 1472 7360 7680 0.041 0.046 0.045 0.035 0.023 1 1 0 1 0 0 1 0 0 0 0.1956447069 0.2401550802 0.3115695652 0.2799326859 0.1358 9.3846297571 9.7954568083 7.2943772993 8.9038152117 8.9463748261 -3.1896903884 -3.0850189637 -3.0976265812 -3.3621190479 -3.7873195269 1 1 -3.1896903884 -3.0850189637 1
136 11392 15744 2112 5952 70016 0.047 0.047 0.045 0.042 0.020 0 1 0 0 1 0 0 0 0 1 0.2909 0.2562 0.3115 0.3190645161 0.0138158135 9.3406666337 9.6642146193 7.6553906448 8.6914825765 11.1564790663 -3.063609696 -3.0647251443 -3.0976265812 -3.1805569606 -3.904240865 0 1 -3.063609696 -3.0647251443 0
137 12800 12384 1344 26880 9536 0.041 0.049 0.039 0.031 0.022 0 0 0 1 1 0 0 0 1 0 0.1861324856 0.2996581395 0.2054792926 0.0090797619 0.0741 9.4572004499 9.4241605958 7.2034055211 10.1991377946 9.1628293893 -3.2013951486 -3.0062035727 -3.24033819 -3.4707484446 -3.8295793362 0 0 -3.2013951486 -3.0062035727 0
138 50624 22656 10176 4928 6784 0.035 0.037 0.034 0.042 0.030 1 1 1 0 0 1 1 1 0 0 0.0535 0.0480262712 0.0788383648 0.2372818182 0.3132 10.8321810511 10.0281799965 9.2277872856 8.5026885052 8.8223221775 -3.3524072175 -3.2945876475 -3.3887748617 -3.1805569606 -3.5223062543 1 1 -3.3524072175 -3.2945876475 1
139 11840 14400 1408 6976 9216 0.047 0.049 0.045 0.042 0.030 0 0 0 0 0 0 0 0 0 0 0.2909 0.2810253333 0.3115909091 0.2353440367 0.3046 9.3792389084 9.5749834856 7.2499255367 8.8502309656 9.1286963829 -3.063609696 -3.0062035727 -3.0976265812 -3.1805569606 -3.5013630804 0 0 -3.063609696 -3.0062035727 0
140 12096 15264 1856 6528 5696 0.047 0.049 0.045 0.042 0.031 0 0 0 0 0 0 0 0 0 0 0.2909 0.2896949686 0.3115137931 0.275377451 0.3371 9.4006300984 9.6332523937 7.5261789133 8.7838558966 8.6475194531 -3.063609696 -3.0062035727 -3.0976265812 -3.1805569606 -3.4707484446 0 0 -3.063609696 -3.0062035727 0
141 10368 14496 704 32192 24384 0.047 0.049 0.045 0.031 0.026 0 0 0 1 1 0 0 0 1 0 0.2909 0.275989404 0.3583181818 0.0611459245 0.2692 9.2464794186 9.5816280283 6.5567783562 10.3794732535 10.1016824585 -3.063609696 -3.0062035727 -3.0976265812 -3.4707484446 -3.6341545544 0 0 -3.063609696 -3.0062035727 0
142 12224 15072 13952 5248 77824 0.047 0.049 0.044 0.042 0.026 0 0 1 0 1 0 0 1 0 0 0.2909 0.2894968153 0.3908431193 0.2789741103 0.2982 9.4111565114 9.6205939968 9.5433781461 8.5656023306 11.2622051459 -3.063609696 -3.0062035727 -3.1328414875 -3.1782639212 -3.6341545544 0 0 -3.063609696 -3.0062035727 0
143 98624 11520 6272 5120 38720 0.039 0.049 0.044 0.042 0.027 1 0 1 0 1 1 0 1 0 0 0.1485 0.30042 0.3986893993 0.25718125 0.3197 11.4990699187 9.3518399342 8.743850562 8.540909718 10.5641115414 -3.2466003729 -3.0062035727 -3.1185938861 -3.1693418897 -3.6165587925 0 0 -3.2466003729 -3.0062035727 0
144 27840 11712 1024 6400 13888 0.035 0.049 0.045 0.042 0.031 1 0 0 0 0 0 0 0 0 0 0.042582938 0.301 0.40905 0.247401 0.4116 10.2342291144 9.3683692362 6.9314718056 8.7640532693 9.5387804369 -3.3639111143 -3.0062035727 -3.0976265812 -3.1693418897 -3.4757862387 0 0 -3.3639111143 -3.0062035727 0
145 81088 10464 1792 4864 3520 0.031 0.049 0.045 0.042 0.031 1 0 0 0 0 1 0 1 0 0 0.0653 0.2868825688 0.4061 0.2434210526 0.414 11.3032902637 9.2556960737 7.4910875935 8.4896164236 8.1662162686 -3.4707484446 -3.0062035727 -3.0976265812 -3.1693418897 -3.4707484446 0 0 -3.4707484446 -3.0062035727 0
146 10688 16704 49344 6656 3008 0.047 0.047 0.031 0.038 0.031 0 1 1 0 0 1 0 1 0 0 0.2909 0.228554023 0.1322342412 0.154032572 0.412 9.2768768958 9.7234034907 10.8065714569 8.8032739825 8.0090306851 -3.063609696 -3.0624954904 -3.4707484446 -3.274601097 -3.4707484446 0 1 -3.063609696 -3.0624954904 0
147 24512 14592 7488 18368 29184 0.042 0.048 0.032 0.031 0.022 1 1 1 1 0 0 0 1 1 0 0.2118 0.2859611842 0.1583019516 0.0184616962 0.168 10.1069180725 9.5882287123 8.9210570182 9.8183652991 10.2813758929 -3.1693418897 -3.0470255679 -3.4388659566 -3.4627130937 -3.815293379 0 1 -3.1693418897 -3.0470255679 0
148 8640 11616 40064 6976 15744 0.047 0.049 0.030 0.037 0.022 0 0 1 0 1 0 0 1 0.408118486 0 0.2909 0.3127619835 0.0848627796 0.1546457566 0.1517 9.0641578618 9.3601387371 10.5982334545 8.8502309656 9.6642146193 -3.063609696 -3.0062035727 -3.5223062543 -3.3078082896 -3.8295793362 0 0 -3.063609696 -3.0062035727 0
149 11456 11712 2368 8128 6656 0.047 0.049 0.045 0.042 0.030 0 0 0 0 0 0 0 0 0 0 0.2909 0.3046 0.3958 0.2798732283 0.373 9.3462688892 9.3683692362 7.769800996 9.0030701698 8.8032739825 -3.063609696 -3.0062035727 -3.0976265812 -3.1693418897 -3.5223062543 0 0 -3.063609696 -3.0062035727 0
150 6080 26880 1152 41152 9024 0.047 0.042 0.045 0.036 0.026 0 1 0 1 1 0 1 0 1 0 0.2909 0.1689464286 0.3358333333 0.1771734059 0.3485 8.712759975 10.1991377946 7.0492548413 10.6250278076 9.1076429737 -3.063609696 -3.1805569606 -3.0976265812 -3.3303312658 -3.6341545544 1 1 -3.063609696 -3.1805569606 1
151 9664 11616 2112 6400 48384 0.046 0.049 0.045 0.042 0.023 0 0 0 0 1 0 0 0 0 1 0.2827324185 0.2848408689 0.3608393939 0.3676041197 0.2583 9.1761629202 9.3601387371 7.6553906448 8.7640532693 10.7869244595 -3.0750567672 -3.0158855862 -3.0976265812 -3.1731367462 -3.7601069634 0 0 -3.0750567672 -3.0158855862 0
152 13120 11616 90240 4992 4992 0.045 0.047 0.040 0.041 0.030 1 1 1 1 1 0.5609756098 1 1 1 1 0.2664 0.2434760331 0.368130922 0.4448038462 0.4121 9.4818930625 9.3601387371 11.4102280667 8.51559191 8.51559191 -3.0976265812 -3.0492209592 -3.2072252076 -3.1843234434 -3.5223062543 1 1 -3.0976265812 -3.0492209592 1
153 8448 12576 3904 76416 4928 0.047 0.049 0.037 0.031 0.030 0 0 0 1 0 0 0 0 1 0 0.3260833333 0.2842992366 0.3068221378 0.2985625628 0.4084 9.0416850059 9.4395455147 8.2697569475 11.2439473773 8.5026885052 -3.063609696 -3.0062035727 -3.2934588071 -3.4707484446 -3.5223062543 0 0 -3.063609696 -3.0062035727 0
154 27072 10080 6720 29632 17728 0.044 0.051 0.031 0.031 0.021 1 0 1 1 1 1 0 0 1 0 0.3546758865 0.2885851387 0.1943333333 0.2879460858 0.1794 10.2062552624 9.2183085416 8.8128434335 10.2966101374 9.7829005895 -3.1328414875 -2.9813554708 -3.4707484446 -3.4867948492 -3.8513983836 0 0 -3.1328414875 -2.9813554708 0
155 14784 10752 50816 13504 11712 0.043 0.053 0.031 0.026 0.017 0 0 1 1 1 0 0 1 0 0 0.2856962235 0.311225 0.2349173804 0.1746075829 0.0424 9.6013007939 9.2828470628 10.8359665446 9.5107412168 9.3683692362 -3.1480803351 -2.9390369292 -3.4707484446 -3.6341545544 -4.1006141752 0 0 -3.1480803351 -2.9390369292 0
156 45632 8928 9472 10240 2944 0.039 0.053 0.032 0.028 0.032 1 0 1 1 0 1 0 0.9653814714 0 0 0.2296674614 0.3103806452 0.2635811989 0.2267864678 0.4669 10.7283645038 9.0969476846 9.1560953571 9.2340568986 7.9875244798 -3.2466003729 -2.9390369292 -3.4491306672 -3.5688612543 -3.4508472903 0 0 -3.2466003729 -2.9390369292 0
157 16000 12096 2304 42496 1536 0.041 0.053 0.044 0.031 0.033 1 0 0 1 0 0 0 0.4473017402 1 0 0.3622283371 0.3297190476 0.4796515878 0.3011528614 0.4976 9.6803440012 9.4006300984 7.7424020218 10.6571652328 7.3369369137 -3.1902895439 -2.9390369292 -3.1202732731 -3.4707484446 -3.4027611036 0 0 -3.1902895439 -2.9390369292 0
158 10048 13056 6464 6336 4416 0.048 0.053 0.032 0.040 0.028 0 0 0 0 0 0 0 0 0 0 0.4151127389 0.3446367647 0.2144031702 0.4446346673 0.4118 9.2151288887 9.4770030772 8.7740036002 8.7540029335 8.392989588 -3.0437414927 -2.9390369292 -3.4475893535 -3.2119645131 -3.5879035368 0 0 -3.0437414927 -2.9390369292 0
159 22976 9504 1920 5632 46080 0.044 0.053 0.043 0.040 0.023 1 0 0 0 1 1 0 0 0 0 0.3354779944 0.3459212121 0.434398779 0.4415478327 0.3013 10.0422054718 9.1594680416 7.560080465 8.6362198978 10.7381342954 -3.1328414875 -2.9390369292 -3.1535086433 -3.229654067 -3.7601069634 0 0 -3.1328414875 -2.9390369292 0
160 10496 19008 960 65024 5056 0.047 0.042 0.043 0.034 0.027 1 1 1 1 1 0 0 0 0.2254724409 0 0.3957984088 0.1677151515 0.4913733333 0.3643989173 0.3947 9.2587495112 9.8526152222 6.8669332845 11.0825117115 8.5283309358 -3.0639249531 -3.1805569606 -3.1400357632 -3.3749815395 -3.6165587925 0 1 -3.0639249531 -3.1805569606 0

Q1

Question 1: Multiple Choice
Select the BEST answer by typing the corresponding letter in the Answer Column.
Question Number Points Question A B C D E Answer:
1A 2 Consider 2 events, A and B. A = getting an A in this class. B = getting a B in this class. Which of the following is true? A and B are independent events. A and B are mutually exclusive events. A and B are not independent events. B and C None of the above D The events are both mutually exclusive and not independent. You cannot get both an A and a B, so they rae mutually exclusive. If you get an A, then you know you did NOT get a B, so they are not independent.
1B 2 In a symmetric distribution, which of the following is true? median = mean median > mean median < mean Not enough information given. None of the above A In a symetric distribution, the mean = the median.
1C 2 The null hypothesis, H not, should be What you want to show. The opposite of what you want to show. Unrelated to what you want to show. Not enough information given. None of the above B The null hypothesis should be the opposite of what you want to show.
1D 2 The Central Limit Theorem states the following about sample statistic distributions: for small sample sizes, the sample statistic distribution will be normally distributed. For large sample sizes, the sample statistic distribution will be normally distributed. The sample statistic distribution will look similar to the population distribution. The sample statistic standard deviation increases as the sample size increases. None of the above B Answer B is basically a restatement of the Central Limit Theorem.
1E 2 Which of the following best measures accuracy of a regression model? p-values residual plots R^2 or adjusted R^2 t-statistic None of the above C R^2 (simple regression) or Adjusted R^2 (multiple regression) tells us about accuracy of the model.
1F 2 If your data is growing exponentially over time, the best way to display it is A line graph using a linear scale A line graph using a log scale A scatterplot using a linear scale A scatterplot using a log scale. None of the above B A line graph is required because the data is linked over time. A log scale is better for exponential growth because it makes exponential growth appear linear.
1G 2 If you run a regression and your R^2 = 0.9 and your p-values are very low, then Your model is ready to use. Your model is not good based on the low p-values. Your model is not good based on the high R^2. You must look at the residual plots to draw any conclusions. None of the above D You must look at residual plots. All 3 diagnostic tools must be good before you should use the model (R^2, p-values, and residual plots).
1H 2 If you try 100 models, how many do you expect to be significant at the 5% level simply due to random chance? 0 100 5 Not enough information given. None of the above C You expect 5% of the models to appear good at the 5% level due to random chance. This means 5% of 100 = 5.
1I 8 Upload your file to canvas with the correct name. This is worth 6% of the final exam grade. Nothing to submit.
Total 24

Work_Q1

Work can be done on this page for Q1

Q2

Question 2: Shorter Problems
Question Points Instructions
Use the data from the Data tab to answer the following questions. The data are weekly adjusted closing prices and volumes for the stock of Zoom (the conferencing software).
Questions Notes: Answer Guide:
2A 2 Average Weekly Adjusted Close of ZM. 88.7051701852 Use formula in Excel
2B 2 Weekly standard deviation of ZM Adjusted Closes 22.5853729642
2C 2 Average Weekly Volume of ZM 22271203.5555556
2D 2 Weekly standard deviation of ZM 23491893.0332999
2E 2 Median Weekly Adjusted Close of ZM 84.0200005
2F 2 Median Weekly Volume of ZM 13028700
2G 4 What percentage return has ZM generated over this time period? 1.4572451129 Use (final - initial)/initial to compute percentage return. 146% increase!
2H 4 Did ZM generate more of these returns in the FIRST or SECOND half of the data set? SECOND Answer FIRST or SECOND Easiest way to answer is to compute for first and second half returns. Then compare. Second half return is almost 100%, first half return is much lower. Work done on data tab.
Total 20

Work_Q2

Work can be done on this page for Q2

Q3

Question 3: Longer Problems
Assumptions and Information: Questions
Question Number Points Human Resources and Sick Days Answers: Notes: Answer Notes:
You work as an analyst at a large firm and are planning schedules for the upcoming month (pretend there is no Covid-19). From examining the entire population of workers, you know that an average employee misses 2.5 days of work each month with a standard deviation of 0.5. You may assume all months are identical for your business (stupid assumption in the real world, but it makes this problem much easier). You study a sample of 200 employees and see that on average they missed 2.7 days each last month. 3A 2 What is your best guess for the standard deviation of sample statistic for this 200 person sample? 0.0353553391 Use Central Limit Theorem Formula, sigma/square root n
Your boss is concerned that people are missing more days of work than they did in the past. Your sample of 200 will be used to test this. 3B 2 What is the z-score associated with your measured mean from the sample? 5.6568542495 Use z-score formula: (X-mu)/sigma
Null is that people are missing the same or fewer days. 3C 4 What is the p-value associated with the observed mean from your sample? 0.0000000077 Draw picture. You want area to right of 5.6 z-score. You could also calculate this area on the raw variables by feeding the norm.dist command that info.
3D 4 Based on your sample of 200, would you accept or reject the null hypothesis at the 1% level? REJECT Answer Accept or Reject reject, as p-value is less than alpha
Suppose now that your boss is concerned that people are missing more or fewer days than in the past. 3E 4 What is the p-value associated with your sample under the new null? 0.0000000154 Double the old p-value, as now we are considering extreme values on both sides (2-Tailed)
Assume that people are missing days at the same rate as the entire population of workers from the beginning of the question. 3F 2 What is the upper bound of the 90% confidence level for a sample of 1000 workers on average days missed? 2.5260074194 0.0260074194 CONFIDENCE.NORM gives us the 'garbage' term, or the +- term away from the middle. So add that garbage term to the middle (mean) to get upper bound.
Data Analysis and Probability Answers:
Suppose you work for the Centers for Disease Control in the US. From studying the data, you conclude that the number of deaths each day is drawn from a normal distribution with mean 2000 and standard deviation 500. 3G 2 What is the probability that deaths tomorrow are between 1900 and 2500? 0.4206044555 Draw a picture. You want the area between 1900 and 2500. Formulas at left take area left of 2500 and subtract area left of 1900, leaving you the area in between.
3H 2 What is the probability that deaths tomorrow are below 1750? 0.3085375387 Simpler question, same logic.
Suppose now you are drawing cards from a standard deck. 3I 4 What is the probability of drawing at least 5 red cards if you draw 6 cards from a standard 52 card deck without replacement? 0.0145398585 answer = probability of getting 5 red cards + probability of getting 6 red cards = 26 choose 5/52 choose 6 + 26 choose 6/52 choose 6. Excel uses COMBIN for the choose.
Suppose you draw 4 cards without replacement. 3J 4 What is the probability of drawing exactly 4 face cards (Jack, Queen, King, or 3 for every suit, 12 total) 0.0018284237 12 choose 4/52 choose 4
3K 2 How many combinations of exactly 4 face cards are there in a standard deck? 495 12 choose 4 = all the ways you can combine 12 cards into groups of 4
These questions deal with election polls. Election Polls
Suppose you work for Joe Biden's presidential campaign. You have been tasked with answering the following quantitative questions about his campaign polls. You may assume there are only two outcomes, Y for Joe or N for Joe. 3L 4 How many potential voters do you need to sample to provide a 2% margin of error at the 95% confidence interval? 2400.9117629338 See lecture 9 slies for formula deriviation. Derived from the formula for the error term or 'garbage'. You change error to known and sample size to the unknown and use algebra to solve for sample size. Use 0.5 as p and p' as that is the worst case scenario. N = Zalpha^2 p'(1-p')/e^2 Use NORM.INV of 0.025 to get Zalpha -1.9599639845
A rival analyst challenges your answer to 3L by saying that rather than sampling a certain number, you must sample a certain percentage of the population to have a good guess at who will win the election. 3M 2 Does the rival analyst's claim have any merit? N Answer Y, N It does not matter what portion of the population you sample. It is the sample size that determines the error (statistical sampling error).
Suppose you run a poll of 300 voters in Texas and find that 155 support Biden. 3N 4 Using only your poll, what is the probability that Biden will win Texas? (assume you must win 50% of the vote to win). 0.718256966 Use proportions formulas from class. Technicallly speaking, we are using the normal distribution to approximate the binomial distrbution. For sigma, use CLT version for proportions and plug in 155/300 for p sigma = (155/300*(1-155/300)/300)^0.5
What is the margin of error associated with the poll in 3N? 3O 4 What is the margin of error associated with the poll in 3N at the 90% confidence level? 0.0949128951 Answer in decimal form, giving the width of the confidence interval. e = Zalpha * sigma (where sigma already has square root of n in it). Note I asked for width of the interval, so I made the answer positive. I also doubled the error, as the width of the interval is twice the error. -1.644853627 0.0288514715
Total 46

Work_Q3

Work can be done on this page for Q3

Q4

Question 4: Working with OJ Data
Question Points Instructions
Use the information on the 5 SKU's from the OJ_Data tab to answer these questions. The Data Description is posted on canvas if you need to review it.
Questions Answer Notes: Answer Notes:
Regress sales2 on price2.
4A 2 What is the slope coefficient on price2? -457129.583350942 See regression output
4B 4 Using the model generated by the regression, forecast sales if price = 0.055. 9492.8404176904 Y = mx + b
2 Regress LN(sales2) on LN(price2)
4C 2 What is the slope coefficient on LN(price2)? -1.806835499 See regression output
4D 4 Using the model generated by the regression, forecast sales if price = 0.055. 9242.7902357367 EXP(Y=mx+b)
4E 2 Which model has a better R^2? LOG Answer LOG, LINEAR
4F 2 Which model as better residual plots? SAME Answer LOG, LINEAR, SAME Really both residual plots look fine, so I added an answer 'SAME'
Regress LN(unit sales of SKU2) on LN of all SKU prices and marketing strategies (feat and disp) of SKU2. Do NOT LN feat2 and disp2. (7 Right Hand Side variables).
4G 6 Use this regression to forecast the unit sales of SKU2 when price2 = 0.055, all other prices equal their historical average, and SKU2 is both featured and displayed that month. 12591.2479687789 See work on REG_2_LOG_MULT sheet
4H 2 Based on this model, what % change in sales would you expect if you feature SKU2? 0.3034774094 It is a 30% change.
4I 2 Are the signs of all of the regression coefficients sensible? N Answer Y, N Many are wrong, see highlighting.
4J 2 Do the relative magnitudes of the coefficients on LN(prices) make sense? Y Answer Y, N Yes, as the coefficient on OWN price (price2) is largest
4K 2 Based on this model, what % change to sales would you expect if you raised the price of SKU3 by 10%? 0.0049174795 1/2 of 1% increase in sales of SKU2 when you increase price of SKU3
4L 2 Assuming feature and display cost the same amount to implement, which is more cost-effective based on this model? FEAT Answer FEAT, DISP FEAT has higher coefficient, so it affects sales by more.
4M 2 What is the R^2 of your regression? 0.5290309924 You MUST answer adjsuted R^2 here as it is a multiple regression. This is how the language is used in the real world.
Improve your model by removing the variables with bad p-values to create a new model. You need to run a new regression.
4N 2 Are the signs of all of the regression coefficients sensible? Y Answer Y, N Rerun the regression dropping all the variables with high p-values. Those are highlighted in red in the REG_2_LOG_MULT sheet.
4O 2 Use this regression to forecast the unit sales of SKU2 when price2 = 0.055, all other prices equal their historical average, and SKU2 is both featured and displayed that month. 12438.8917062984 See work on REG_2_LOG_MULT_2 sheet. Note that we dropped display from the model, so you do not use it in the prediction.
4P 2 Based on this model, what % change to sales would you expect if you lowered the price of SKU1 by 20%? -0.064644784 6.5% drop. This makes sense, as the calculated elasticity is roughly 1/3 (0.32). 1/3 of 20% is roughly 6.5%.
4Q 2 What is the R^2 of this new model? 0.5394134353
4R 2 Are all of the p-values for this new model good? Y Answer Y, N
Total 46

REG_2_LIN

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5945602386
R Square 0.3535018773
Adjusted R Square 0.3478308412
Standard Error 3566.3962422843
Observations 116
ANOVA
df SS MS F Significance F
Regression 1 792845370.931884 792845370.931884 62.3346187787 0
Residual 114 1449986765.8957 12719182.1569799
Total 115 2242832136.82759
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 34634.9675019922 2915.7601807874 11.8785377927 1.14621392917241E-21 28858.8690411077 40411.0659628767 28858.8690411077 40411.0659628767
price2 -457129.583350942 57899.4814074441 -7.8952275951 0 -571828.010075885 -342431.156625998 -571828.010075885 -342431.156625998
RESIDUAL OUTPUT
Observation Predicted sales2 Residuals
1 7064.3395061386 1383.6604938614
2 7064.3395061386 1287.6604938614
3 7064.3395061386 711.6604938614
4 7064.3395061386 903.6604938614
5 7064.3395061386 231.6604938614
6 7064.3395061386 135.6604938614
7 7064.3395061386 903.6604938614
8 7064.3395061386 135.6604938614
9 7064.3395061386 4647.6604938614
10 7064.3395061386 3303.6604938614
11 7064.3395061386 39.6604938614
12 7064.3395061386 -632.3395061386
13 7064.3395061386 135.6604938614
14 7064.3395061386 3111.6604938614
15 7320.8021848786 -1176.8021848786
16 7368.848515459 1271.151484541
17 12778.4592980253 6133.5407019747
18 8492.8694541103 -1772.8694541103
19 8492.8694541103 243.1305458897
20 8492.8694541103 723.1305458897
21 8492.8694541103 4083.1305458897
22 8492.8694541103 -1484.8694541103
23 10397.5760666435 -893.5760666435
24 15635.5191939687 12300.4808060313
25 15635.5191939687 396.4808060313
26 10397.5760666435 -989.5760666435
27 10397.5760666435 4386.4239333565
28 17540.225806502 1179.774193498
29 17540.225806502 -7652.225806502
30 17540.225806502 -4676.225806502
31 10397.5760666435 -1661.5760666435
32 10397.5760666435 -797.5760666435
33 10397.5760666435 -893.5760666435
34 10397.5760666435 -317.5760666435
35 12016.5766438694 -1648.5766438694
36 12016.5766438694 -2704.5766438694
37 12016.5766438694 -4432.5766438694
38 12016.5766438694 -4432.5766438694
39 12016.5766438694 -5488.5766438694
40 14206.989245997 -2974.989245997
41 12016.5766438694 -5968.5766438694
42 12016.5766438694 -5104.5766438694
43 12016.5766438694 -1744.5766438694
44 12016.5766438694 -5200.5766438694
45 15635.5191939687 1548.4808060313
46 15635.5191939687 -5075.5191939687
47 12016.5766438694 -3952.5766438694
48 12016.5766438694 -3856.5766438694
49 12016.5766438694 -2608.5766438694
50 13254.6359625869 3737.3640374131
51 13254.6359625869 -5286.6359625869
52 12016.5766438694 -784.5766438694
53 16587.8724773789 4532.1275226211
54 16587.8724773789 -2859.8724773789
55 12016.5766438694 -5104.5766438694
56 12016.5766438694 -3664.5766438694
57 12016.5766438694 559.4233561306
58 12016.5766438694 1231.4233561306
59 13730.8125814355 4125.1874185645
60 13730.8125814355 -3650.8125814355
61 12016.5766438694 -880.5766438694
62 12016.5766438694 -1456.5766438694
63 12016.5766438694 -496.5766438694
64 12016.5766438694 175.4233561306
65 12016.5766438694 -880.5766438694
66 12372.7232852029 -2196.7232852029
67 12778.4592980253 2197.5407019747
68 12482.2511219572 -2018.2511219572
69 12016.5766438694 -2608.5766438694
70 12016.5766438694 367.4233561306
71 12016.5766438694 -976.5766438694
72 12016.5766438694 -1840.5766438694
73 15635.5191939687 4140.4808060313
74 14621.7228032697 354.2771967303
75 12016.5766438694 943.4233561306
76 12016.5766438694 271.4233561306
77 12016.5766438694 175.4233561306
78 12016.5766438694 -1072.5766438694
79 12016.5766438694 -496.5766438694
80 15635.5191939687 2604.4808060313
81 15635.5191939687 1068.4808060313
82 14503.2066560828 -4519.2066560828
83 12016.5766438694 463.4233561306
84 12016.5766438694 17935.4233561306
85 12016.5766438694 -3664.5766438694
86 12016.5766438694 -976.5766438694
87 12016.5766438694 -2608.5766438694
88 12016.5766438694 -1552.5766438694
89 12016.5766438694 -3280.5766438694
90 12894.1420646287 -2238.1420646287
91 13730.8125814355 4221.1874185645
92 13302.253597044 2441.746402956
93 12016.5766438694 367.4233561306
94 17683.0788012991 4972.9211987009
95 12016.5766438694 2383.4233561306
96 12016.5766438694 3247.4233561306
97 12016.5766438694 2479.4233561306
98 12016.5766438694 3055.4233561306
99 12016.5766438694 -496.5766438694
100 12016.5766438694 -304.5766438694
101 12016.5766438694 -1552.5766438694
102 13254.6359625869 3449.3640374131
103 12921.3122928225 1670.6877071775
104 12016.5766438694 -400.5766438694
105 12016.5766438694 -304.5766438694
106 15635.5191939687 11244.4808060313
107 12234.5114813526 -618.5114813526
108 12968.9299729925 -1352.9299729925
109 12016.5766438694 559.4233561306
110 11447.5117508135 -1367.5117508135
111 10445.1937011006 306.8062988994
112 10445.1937011006 -1517.1937011006
113 10445.1937011006 1650.8062988994
114 10445.1937011006 2610.8062988994
115 10445.1937011006 -941.1937011006
116 15635.5191939687 3372.4808060313

price2 Residual Plot

6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 5.9751471600000002E-2 5.9646367200000001E-2 4.7812500000000001E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.30208333E-2 4.1562500000000002E-2 4.1562500000000002E-2 5.30208333E-2 5.30208333E-2 3.73958333E-2 3.73958333E-2 3.73958333E-2 5.30208333E-2 5.30208333E-2 5.30208333E-2 5.30208333E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.4687499999999998E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.1562500000000002E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.6770833300000002E-2 4.6770833300000002E-2 4.94791667E-2 3.9479166699999999E-2 3.9479166699999999E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.5729166699999997E-2 4.5729166699999997E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.8700073300000001E-2 4.7812500000000001E-2 4.8460474199999999E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.3780244000000003E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.1562500000000002E-2 4.4039505600000001E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.7559436599999998E-2 4.5729166699999997E-2 4.6666666699999998E-2 4.94791667E-2 3.70833333E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.6770833300000002E-2 4.7500000000000001E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.9002420400000003E-2 4.7395833300000002E-2 4.94791667E-2 5.0724032299999999E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 4.1562500000000002E-2 1383.6604938614328 1287.6604938614328 711.66049386143277 903.66049386143277 231.66049386143277 135.66049386143277 903.66049386143277 135.66049386143277 4647.6604938614328 3303.6604938614328 39.660493861432769 -632.33950613856723 135.66049386143277 3111.6604938614328 -1176.802184878612 1271.1514845410384 6133.5407019746635 -1772.8694541102595 243.13054588974046 723.13054588974046 4083.1305458897405 -1484.8694541102595 -893.57606664350169 12300.480806031279 396.48080603127892 -989.57606664350169 4386.4239333564983 1179.7741934980331 -7652.2258065019669 -4676.2258065019669 -1661.5760666435017 -797.57606664350169 -893.57606664350169 -317.57606664350169 -1648.5766438694482 -2704.5766438694482 -4432.5766438694482 -4432.5766438694482 -5488.5766438694482 -2974.9892459970324 -5968.5766438694482 -5104.5766438694482 -1744.5766438694482 -5200.5766438694482 1548.4808060312789 -5075.5191939687211 -3952.5766438694482 -3856.5766438694482 -2608.5766438694482 3737.3640374131137 -5286.6359625868863 -784.57664386944816 4532.1275226211328 -2859.8724773788672 -5104.5766438694482 -3664.5766438694482 559.42335613055184 1231.4233561305518 4125.1874185645174 -3650.8125814354826 -880.57664386944816 -1456.5766438694482 -496.57664386944816 175.42335613055184 -880.57664386944816 -2196.7232852029192 2197.5407019746635 -2018.2511219571788 -2608.5766438694482 367.42335613055184 -976.57664386944816 -1840.5766438694482 4140.4808060312789 354.27719673032698 943.42335613055184 271.42335613055184 175.42335613055184 -1072.5766438694482 -496.57664386944816 2604.4808060312789 1068.4808060312789 -4519.2066560827734 463.42335613055184 17935.423356130552 -3664.5766438694482 -976.57664386944816 -2608.5766438694482 -1552.5766438694482 -3280.5766438694482 -2238.1420646287115 4221.1874185645174 2441.7464029560251 367.42335613055184 4972.9211987008639 2383.4233561305518 3247.4233561305518 2479.4233561305518 3055.4233561305518 -496.57664386944816 -304.57664386944816 -1552.5766438694482 3449.3640374131137 1670.6877071774943 -400.57664386944816 -304.57664386944816 11244.480806031279 -618.51148135255062 -1352.9299729925478 559.42335613055184 -1367.5117508135299 306.80629889940974 -1517.1937011005903 1650.8062988994097 2610.8062988994097 -941.19370110059026 3372.4808060312789

price2

Residuals

REG_2_LOG

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6325873221
R Square 0.40016672
Adjusted R Square 0.3949050246
Standard Error 0.2580246141
Observations 116
ANOVA
df SS MS F Significance F
Regression 1 5.0633451855 5.0633451855 76.0528093511 0
Residual 114 7.5897439696 0.0665767015
Total 115 12.6530891551
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.8910134915 0.622369558 6.2519341468 0.0000000073 2.6581041906 5.1239227924 2.6581041906 5.1239227924
LOGprice2 -1.806835499 0.2071862758 -8.7208261851 0 -2.2172699156 -1.3964010824 -2.2172699156 -1.3964010824
RESIDUAL OUTPUT
Observation Predicted LOGsales2 Residuals
1 8.9649976683 0.0766873376
2 8.9649976683 0.0652586418
3 8.9649976683 -0.0062003222
4 8.9649976683 0.018191131
5 8.9649976683 -0.0699161366
6 8.9649976683 -0.0831613633
7 8.9649976683 0.018191131
8 8.9649976683 -0.0831613633
9 8.9649976683 0.4033715679
10 8.9649976683 0.2814817503
11 8.9649976683 -0.0965843836
12 8.9649976683 -0.1959568574
13 8.9649976683 -0.0831613633
14 8.9649976683 0.2627896173
15 8.9818835566 -0.2586522818
16 8.9850646261 0.0790932357
17 9.38463931 0.4629126102
18 9.0611286094 -0.2482851758
19 9.0611286094 0.0140790886
20 9.0611286094 0.0675677736
21 9.0611286094 0.3784169053
22 9.0611286094 -0.2063209767
23 9.1978164838 -0.0383484422
24 9.6377567145 0.5999147441
25 9.6377567145 0.0445852894
26 9.1978164838 -0.0485008137
27 9.1978164838 0.4034843101
28 9.8286290627 0.0087186873
29 9.8286290627 -0.629551883
30 9.8286290627 -0.3664410713
31 9.1978164838 -0.1226087858
32 9.1978164838 -0.0282981064
33 9.1978164838 -0.0383484422
34 9.1978164838 0.0204920578
35 9.322728824 -0.0762494054
36 9.322728824 -0.183669654
37 9.322728824 -0.3889327801
38 9.322728824 -0.3889327801
39 9.322728824 -0.5388729274
40 9.5067692679 -0.1802471416
41 9.322728824 -0.6152459061
42 9.322728824 -0.4817145135
43 9.322728824 -0.0855517981
44 9.322728824 -0.4957007555
45 9.6377567145 0.1139772828
46 9.6377567145 -0.3729281572
47 9.322728824 -0.3275638337
48 9.322728824 -0.315729376
49 9.322728824 -0.1734131539
50 9.4244390592 0.3160588649
51 9.4244390592 -0.4412502599
52 9.322728824 0.0037933023
53 9.7306736112 0.2273021266
54 9.7306736112 -0.2034807895
55 9.322728824 -0.4817145135
56 9.322728824 -0.2924725139
57 9.322728824 0.1168166907
58 9.322728824 0.1688730526
59 9.4651352702 0.324959595
60 9.4651352702 -0.2468267285
61 9.322728824 -0.0047904414
62 9.322728824 -0.0579002667
63 9.322728824 0.0291111102
64 9.322728824 0.0858064539
65 9.322728824 -0.0047904414
66 9.3514054192 -0.1236181336
67 9.38463931 0.2295648887
68 9.3603167942 -0.1046207205
69 9.322728824 -0.1734131539
70 9.322728824 0.1014317718
71 9.322728824 -0.0134485042
72 9.322728824 -0.0949415384
73 9.6377567145 0.2544676458
74 9.543829551 0.0703746478
75 9.322728824 0.1468941459
76 9.322728824 0.0936496314
77 9.322728824 0.0858064539
78 9.322728824 -0.0221821841
79 9.322728824 0.0291111102
80 9.6377567145 0.1736155491
81 9.6377567145 0.0856467762
82 9.5331612349 -0.3244221443
83 9.322728824 0.1091538179
84 9.322728824 0.9846225553
85 9.322728824 -0.2924725139
86 9.322728824 -0.0134485042
87 9.322728824 -0.1734131539
88 9.322728824 -0.0670327503
89 9.322728824 -0.247521126
90 9.3942279799 -0.1203495871
91 9.4651352702 0.3303215382
92 9.428467677 0.2357469423
93 9.322728824 0.1014317718
94 9.8437914076 0.1843885889
95 9.322728824 0.2522546616
96 9.322728824 0.3105235697
97 9.322728824 0.2588992043
98 9.322728824 0.2978651728
99 9.322728824 0.0291111102
100 9.322728824 0.0456404122
101 9.322728824 -0.0670327503
102 9.4244390592 0.2989644315
103 9.3964874541 0.1917412582
104 9.322728824 0.0374099131
105 9.322728824 0.0456404122
106 9.6377567145 0.5613810801
107 9.3402226296 0.0199161075
108 9.4004541649 -0.0403154279
109 9.322728824 0.1168166907
110 9.2778323914 -0.0595238498
111 9.2013697482 0.0814773146
112 9.2013697482 -0.1044220636
113 9.2013697482 0.1992603502
114 9.2013697482 0.275633329
115 9.2013697482 -0.0419017066
116 9.6377567145 0.2148585077

LOGprice2 Residual Plot

-2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8082158998829323 -2.8175614591851099 -2.8193220342207224 -3.040468 1673953826 -2.861419935946397 -2.861419935946397 -2.861419935946397 -2.861419935946397 -2.861419935946397 -2.9370703615340887 -3.1805569605660642 -3.1805569605660642 -2.9370703615340887 -2.9370703615340887 -3.2861959898590136 -3.2861959898590136 -3.2861959898590136 -2.9370703615340887 -2.9370703615340887 -2.9370703615340887 -2.9370703615340887 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.1080614585279105 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.1805569605660642 -3.1805569605660642 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.0624954904263681 -3.0624954904263681 -3.006203572747602 -3.2319821715291743 -3.2319821715291743 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.085018963651827 -3.085018963651827 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.0220747437612556 -3.0404681673953826 -3.0270067782516215 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.1805569605660642 -3.1285726135817997 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.1805569605660642 -3.1805569605660642 -3.1226681933487099 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.0457750533188719 -3.085018963651827 -3.064725144326657 -3.006203572747602 -3.2945876475027736 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.006203572747602 -3.0624954904263681 -3.0470255679415414 -3.006203572747602 -3.006203572747602 -3.1805569605660642 -3.0158855861730811 -3.0492209592082737 -3.006203572747602 -2.9813554708302528 -2.9390369292475245 -2.9390369292475245 -2.9390369292475245 -2.9390369292475245 -2.9390369292475245 -3.1805569605660642 7.6687337638087882E-2 6.5258641814464724E-2 -6.2003221676807385E-3 1.8191130956479284E-2 -6.9916136553787922E-2 -8.3161363303808855E-2 1.8191130956479284E-2 -8.3161363303808855E-2 0.40337156789313688 0.28148175028410094 -9.6584383635949678E-2 -0.19595685744915237 -8.3161363303808855E-2 0.26278961727194883 -0.25865228178577482 7.9093235711795273E-2 0.46291261022955332 -0.2482851758411222 1.4079088626369085E-2 6.7567773577355439E-2 0.37841690531067051 -0.20632097674208971 -3.8348442223355761E-2 0.5999147441480428 4.4585289393305416E-2 -4.8500813687374134E-2 0.40348431005568308 8.7186873322657732E-3 -0.62955188300184517 -0.36644107128056902 -0.12260878584109669 -2.8298106369854636E-2 -3.8348442223355761E-2 2.0492057799577523E-2 -7.6249405415232729E-2 -0.18366965403606983 -0.38893278007243026 -0.38893278007243026 -0.53887292736334658 -0.18024714162421418 -0.61524590614791919 -0.48171451352339645 -8.5551798077545982E-2 -0.4957007554981363 0.11397728281730579 -0.37292815723103345 -0.32756383369613928 -0.31572937604913598 -0.17341315386888034 0.31605886486472912 -0.44125025991250233 3.7933022583036546E-3 0.22730212661640614 -0.20348078947604797 -0.481714513 52339645 -0.29247251388486895 0.11681669066169853 0.16887305261775154 0.32495959501921945 -0.24682672853645826 -4.7904414330872669E-3 -5.7900266747036966E-2 2.9111110242594052E-2 8.5806453919138548E-2 -4.7904414330872669E-3 -0.1236181336257971 0.22956488874110192 -0.10462072050828475 -0.17341315386888034 0.10143177182222018 -1.3448504176201581E-2 -9.4941538427384842E-2 0.25446764576613212 7.0374647750391262E-2 0.14689414589897609 9.3649631380165133E-2 8.5806453919138548E-2 -2.2182184144957873E-2 2.9111110242594052E-2 0.17361554913703792 8.5646776191079965E-2 -0.32442214427952187 0.10915381791613044 0.98462255527003073 -0.29247251388486895 -1.3448504176201581E-2 -0.17341315386888034 -6.7032750310309197E-2 -0.2475211260226029 -0.12034958707188714 0.33032153816060372 0.23574694229753668 0.10143177182222018 0.18438858886780096 0.25225466155680287 0.31052356968077888 0.25889920427547253 0.29786517280885505 2.9111110242594052E-2 4.5640412193803215E-2 -6.7032750310309197E-2 0.29896443150542851 0.1917412582497775 3.7409913057288691E-2 4.5640412193803215E-2 0.56138108014580013 1.9916107498190172E-2 -4.0315427874716647E-2 0.11681669066169853 -5.9523849754508973E-2 8.1477314581293925E-2 -0.10442206356054484 0.19926035023767774 0.27563332902225035 -4.190170657921044E-2 0.21485850767108694

LOGprice2

Residuals

REG_2_LOG_MULT

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.7467922543
R Square 0.5576986711
Adjusted R Square 0.5290309924
Standard Error 0.2276384021
Observations 116
ANOVA
df SS MS F Significance F
Regression 7 7.0566110074 1.0080872868 19.4539179992 1.24293168712197E-16
Residual 108 5.5964781478 0.0518192421
Total 115 12.6530891551
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 5.2359709089 0.7384312538 7.0906680643 0.0000000001 3.7722720584 6.6996697594 3.7722720584 6.6996697594
LOGprice1 0.3290718628 0.1127910702 2.9175347148 0.0042928077 0.1055003943 0.5526433313 0.1055003943 0.5526433313
LOGprice2 -1.5685837587 0.2535926771 -6.1854457982 0.0000000113 -2.0712484387 -1.0659190787 -2.0712484387 -1.0659190787
LOGprice3 0.0491747952 0.1194838709 0.4115601108 0.6814774825 -0.1876629655 0.2860125558 -0.1876629655 0.2860125558
LOGprice4 -0.031182829 0.1347088638 -0.2314831265 0.8173774992 -0.2981991667 0.2358335088 -0.2981991667 0.2358335088 Bad p-values highlighted in red for 2nd part
LOGprice5 -0.1125583205 0.1009833933 -1.1146220859 0.2674866635 -0.312724925 0.087608284 -0.312724925 0.087608284 Bad coefficient signs in red, good in green
feat2 0.3034774094 0.072793191 4.1690356645 0.0000619279 0.1591886765 0.4477661424 0.1591886765 0.4477661424
disp2 0.0217874131 0.0679274063 0.3207455475 0.7490230314 -0.1128564904 0.1564313167 -0.1128564904 0.1564313167
Prediction Model:
X's LN if required:
RESIDUAL OUTPUT Intercept 1 1
LOGprice1 0.0453214084 -3.0939757655
Observation Predicted LOGsales2 Residuals LOGprice2 0.055 -2.9004220937
1 9.0375230733 0.0041619327 LOGprice3 0.035965532 -3.3251942433
2 9.0070605009 0.0231958093 LOGprice4 0.0354404248 -3.3399021657
3 9.0115388388 -0.0527414927 LOGprice5 0.0267793642 -3.6201236797
4 8.9985773232 -0.0153885239 feat2 1 1
5 9.0070605009 -0.1119789691 disp2 1 1
6 9.0337558921 -0.1519195871 Predicted LOG Sales2 9.4407572459
7 9.0182883837 -0.0350995844 Predicted Sales2 12591.2479687789
8 9.0309841217 -0.1491478167
9 9.3345464134 0.0338228228
10 9.0598809049 0.1865985136
11 9.0200990874 -0.1516858027
12 9.0127319212 -0.2436911104
13 9.0006580951 -0.1188217901
14 9.0357291206 0.192058165
15 9.0238005653 -0.3005692904
16 9.0021061153 0.0620517465
17 9.762648596 0.0849033242
18 9.1097426984 -0.2968992649
19 9.1399284069 -0.064720709
20 9.13327772 -0.0045813371
21 9.2070274843 0.2325180304
22 9.0765791679 -0.2217715352
23 9.2070583821 -0.0475903405
24 9.6539131405 0.5837583181
25 9.4767017738 0.2056402301
26 9.0705597067 0.0787559634
27 9.090393813 0.5109069809
28 10.0530051792 -0.2156574292
29 9.7970871124 -0.5980099327
30 9.7093079874 -0.247119996
31 9.1734174026 -0.0982097046
32 9.1734174026 -0.0038990251
33 9.2148235462 -0.0553555046
34 9.275967216 -0.0576586744
35 9.2902389077 -0.0437594891
36 9.3175808813 -0.1785217113
37 9.1245510185 -0.1907549746
38 9.3396617918 -0.4058657478
39 9.1694494792 -0.3855935826
40 9.8596374238 -0.5331152976
41 9.13341196 -0.4259290422
42 9.13341196 -0.2923976495
43 9.3236747788 -0.0864977529
44 9.1794303334 -0.3524022648
45 9.6375812932 0.1141527041
46 9.623320441 -0.3584918837
47 9.1533528132 -0.1581878229
48 9.1915288722 -0.1845294242
49 9.3658410093 -0.2165253391
50 9.7236899497 0.0168079743
51 9.2585674569 -0.2753786576
52 9.1670109587 0.1595111676
53 9.8320685384 0.1259071994
54 9.6906143283 -0.1634215066
55 9.2663784656 -0.4253641551
56 9.2586151344 -0.2283588243
57 9.3496175394 0.0899279753
58 9.3671024561 0.1244994205
59 9.7825746071 0.0075202581
60 9.3057707461 -0.0874622045
61 9.3532278109 -0.0352894283
62 9.2596310219 0.0051975354
63 9.3157752414 0.0360646928
64 9.3082997249 0.100235553
65 9.3060267122 0.0119116703
66 9.3323270022 -0.1045397166
67 9.6528579021 -0.0386537034
68 9.3455035782 -0.0898075045
69 9.2826293678 -0.1333136977
70 9.2854887652 0.1386718306
71 9.146622033 0.1626582868
72 9.272789322 -0.0450020364
73 9.5906814183 0.3015429419
74 9.5031379106 0.1110662881
75 9.2577990064 0.2118239636
76 9.2093204021 0.2070580533
77 9.1977438474 0.2107914306
78 9.1842172009 0.1163294389
79 9.2051862414 0.1466536929
80 9.9055520391 -0.0941797755
81 9.9014373098 -0.1780338191
82 9.5025785642 -0.2938394736
83 9.2008843665 0.2309982754
84 9.6617620423 0.645589337
85 9.1984720763 -0.1682157661
86 9.146686443 0.1625938769
87 9.151819495 -0.0025038248
88 9.1534894932 0.1022065805
89 9.2580953566 -0.1828876586
90 9.2817189618 -0.007840569
91 9.7295185709 0.0659382375
92 9.4431970643 0.221017555
93 9.2778997499 0.146260846
94 9.9548908492 0.0732891473
95 9.2842664177 0.2907170679
96 9.2808204857 0.352431908
97 9.3082621943 0.2733658339
98 9.2974100137 0.3231839831
99 9.23563479 0.1162051442
100 9.1822170665 0.1861521697
101 9.1464928616 0.1092032121
102 9.3754908576 0.3479126331
103 9.3626465767 0.2255821356
104 9.3042944087 0.0558443284
105 9.2862740285 0.0820952077
106 9.9026363045 0.2965014902
107 9.3245789512 0.0355597858
108 9.6628989022 -0.3027601652
109 9.2860427279 0.1535027867
110 9.2531084123 -0.0347998706
111 9.2143599627 0.0684871
112 9.107830141 -0.0108824564
113 9.13406001 0.2665700884
114 9.1789588553 0.2980442219
115 9.1840344515 -0.0245664099
116 9.5963814624 0.2562337598

LOGprice1 Residual Plot

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LOGprice1

Residuals

LOGprice2 Residual Plot

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LOGprice2

Residuals

LOGprice3 Residual Plot

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LOGprice3

Residuals

LOGprice4 Residual Plot

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LOGprice4

Residuals

LOGprice5 Residual Plot

-3.6951490671275318 -3.2466003728830555 -3.2466003728830555 -3.2466003728830555 -3.2466003728830555 -3.4217190173829524 -3.4217190173829524 -3.4217190173829524 -3.4217190173829524 -3.5223062542881207 -3.5223062542881207 -3.5223062542881207 -3.5766674635070084 -3.5766674635070084 -3.5766674635070084 -3.2466003728830555 -3.943771 7037427262 -3.6951490671275318 -3.6951490671275318 -3.9042408649860909 -4.1689334192131735 -3.6951490671275318 -3.3749815395312623 -3.8295793362170714 -3.3749815395312623 -3.3749815395312623 -3.3749815395312623 -3.3749815395312623 -3.7601069634023041 -3.7601069634023041 -3.3749815395312623 -3.3749815395312623 -3.470748444623271 -4.0727053871186198 -3.3749815395312623 -3.3749815395312623 -3.6951490671275318 -3.6951490671275318 -3.6951490671275318 -4.1689334192131735 -3.3749815395312623 -3.3749815395312623 -3.3749815395312623 -3.3749815395312623 -3.6951490671275318 -3.6951490671275318 -3.4217190173829524 -3.8152933789695949 -3.9042408649860909 -3.5223062542881207 -3.5223062542881207 -3.6951490671275318 -3.6951490671275318 -3.5223062542881207 -4.1689334192131735 -4.1689334192131735 -3.7601069634023041 -3.7601069634023041 -3.8295793362170714 -3.8295793362170714 -3.8295793362170714 -3.6951490671275318 -3.6951490671275318 -3.7271006669341342 -3.7601069634023041 -3.7401727485014868 -3.5223062542881207 -3.5600465822709677 -3.6341545544246898 -3.6107616748499844 -3.5223062542881207 -3.7534179752515073 -3.6951490671275318 -3.6951490671275318 -3.6888794541139363 -3.6341545544246898 -3.6341545544246898 -3.6223897128451035 -3.587903536773934 -3.7736206825690268 -3.9042408649860909 -3.8812513467613923 -4.0912244348858566 -4.1689334192131735 -3.9518689139753458 -3.8295793362170714 -3.7942399697717626 -3.6951490671275318 -3.6460594569310083 -3.5223062542881207 -3.7873195269271887 -3.9042408649860909 -3.8295793362170714 -3.5223062542881207 -3.5013630804428777 -3.470748444623271 -3.6341545544246898 -3.6341545544246898 -3.6165587925343101 -3.4757862386532281 -3.470748444623271 -3.470748444623271 -3.8152933789695949 -3.8295793362170714 -3.5223062542881207 -3.6341545544246898 -3.7601069634023041 -3.5223062542881207 -3.5223062542881207 -3.8513983836117109 -4.1006141752356964 -3.4508472903059761 -3.4027611036383383 -3.587903536773934 -3.7601069634023041 -3.6165587925343101 4.1619326844042348E-3 2.3195809263739875E-2 -5.2741492654689637E-2 -1.5388523936998411E-2 -0.11197896910451277 -0.1519195870635528 -3.50995844476234E-2 -0.14914781666833221 3.3822822809407072E-2 0.18659851364921032 -0.15168580273356724 -0.24369111038771507 -0.11882179005493754 0.19205816501026618 -0.30056929042626557 6.2051746545261466E-2 8.4903324245836131E-2 -0.29689926491954566 -6.4720708960823714E-2 -4.581337095343585E-3 0.23251803035969587 -0.22177153523416848 -4.7590340465099246E-2 0.58375831809160239 0.20564023009232102 7.8755963432517007E-2 0.51090698090992248 -0.21565742915167085 -0.59800993265573865 -0.24711999599430712 -9.8209704566624367E-2 -3.8990250953823136E-3 -5.5355504595869576E-2 -5.7658674355479889E-2 -4.375948909108196E-2 -0.178521711300764 -0.19075497458069535 -0.40586574782538953 -0.38559358255629306 -0.53311529755166731 -0.42592904216059857 -0.29239764953607583 -8.6497752886350199E-2 -0.35240226484524229 0.11415270411667677 -0.35849188370524487 -0.15818782293305489 -0.18452942419721019 -0.21652533911472283 1.680797429818881E-2 -0.27537865761999392 0.15951116759276296 0.12590719938752137 -0.163421506607 54662 -0.42536415507265168 -0.22835882432011978 8.9927975303165653E-2 0.12449942049117801 7.520258121907375E-3 -8.7462204467250615E-2 -3.5289428326276351E-2 5.1975353746005482E-3 3.6064692811349275E-2 0.10023555303772191 1.1911670346099967E-2 -0.10453971658825978 -3.8653703379468496E-2 -8.980750447451058E-2 -0.13331369770756929 0.13867183061905131 0.1626582868489912 -4.5002036422078007E-2 0.30154294191599718 0.11106628813392838 0.21182396355530209 0.20705805331344607 0.21079143055112581 0.11632943894721137 0.14665369287286367 -9.4179775507331698E-2 -0.17803381911985916 -0.29383947362691742 0.23099827542137774 0.64558933698992327 -0.16821576614753475 0.16259387686853444 -2.5038248442665889E-3 0.10220658045315467 -0.18288765864943457 -7.8405690316003529E-3 6.5938237470939853E-2 0.2210175550128799 0.14626084595022704 7.3289147308987168E-2 0.29071706791229701 0.35243190802640534 0.27336583393334735 0.32318398313523744 0.11620514421322881 0.18615216968668058 0.10920321212555706 0.34791263305617015 0.22558213564022722 5.584432840769793E-2 8.2095207710736418E-2 0.29650149017881589 3.5559785833669366E-2 -0.30276016515534643 0.15350278672502249 -3.4799870642736153E-2 6.848710003845504E-2 -1.0882456426855924E-2 0.26657008836983032 0.2980442219098336 -2.4566409930200095E-2 0.25623375980823759

LOGprice5

Residuals

feat2 Residual Plot

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0.97726495729999996 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 4.1619326844042348E-3 2.3195809263739875E-2 -5.2741492654689637E-2 -1.5388523936998411E-2 -0.11197896910451277 -0.1519195870635528 -3.50995844476234E-2 -0.14914781666833221 3.3822822809407072E-2 0.18659851364921032 -0.15168580273356724 -0.24369111038771507 -0.11882179005493754 0.19205816501026618 -0.30056929042626557 6.205174654 5261466E-2 8.4903324245836131E-2 -0.29689926491954566 -6.4720708960823714E-2 -4.581337095343585E-3 0.23251803035969587 -0.22177153523416848 -4.7590340465099246E-2 0.58375831809160239 0.20564023009232102 7.8755963432517007E-2 0.51090698090992248 -0.21565742915167085 -0.59800993265573865 -0.24711999599430712 -9.8209704566624367E-2 -3.8990250953823136E-3 -5.5355504595869576E-2 -5.7658674355479889E-2 -4.375948909108196E-2 -0.178521711300764 -0.19075497458069535 -0.40586574782538953 -0.38559358255629306 -0.53311529755166731 -0.42592904216059857 -0.29239764953607583 -8.6497752886350199E-2 -0.35240226484524229 0.11415270411667677 -0.35849188370524487 -0.15818782293305489 -0.18452942419721019 -0.21652533911472283 1.680797429818881E-2 -0.27537865761999392 0.15951116759276296 0.12590719938752137 -0.16342150660754662 -0.42536415507265168 -0.22835882432011978 8.9927975303165653E-2 0.12449942049117801 7.520258121907375E-3 -8.7462204467250615E-2 -3.5289428326276351E-2 5.1975353746005482E-3 3.6064692811349275E-2 0.10023555303772191 1.1911670346099967E-2 -0.10453971658825978 -3.8653703379468496E-2 -8.980750447451058E-2 -0.13331369770756929 0.13867183061905131 0.1626582868489912 -4.5002036422078007E-2 0.30154294191599718 0.11106628813392838 0.21182396355530209 0.20705805331344607 0.21079143055112581 0.11632943894721137 0.14665369287286367 -9.4179775507331698E-2 -0.17803381911985916 -0.29383947362691742 0.23099827542137774 0.64558933698992327 -0.16821576614753475 0.16259387686853444 -2.5038248442665889E-3 0.10220658045315467 -0.18288765864943457 -7.8405690316003529E-3 6.5938237470939853E-2 0.2210175550128799 0.14626084595 022704 7.3289147308987168E-2 0.29071706791229701 0.35243190802640534 0.27336583393334735 0.32318398313523744 0.11620514421322881 0.18615216968668058 0.10920321212555706 0.34791263305617015 0.22558213564022722 5.584432840769793E-2 8.2095207710736418E-2 0.29650149017881589 3.5559785833669366E-2 -0.30276016515534643 0.15350278672502249 -3.4799870642736153E-2 6.848710003845504E-2 -1.0882456426855924E-2 0.26657008836983032 0.2980442219098336 -2.4566409930200095E-2 0.25623375980823759

feat2

Residuals

disp2 Residual Plot

0 0 0 0 0 0 0 0 1 1 0.78378378380000002 0 0 0 0 0 1 0 0 0 0 0 0 0.76975945020000003 0.7425149701 0 0 0.74871794869999997 0.39805825239999998 1 0 0 0 0 0 0 0 0 0 0.23931623930000001 0 0 0 0 1 0.34545454549999999 0.78571428570000001 0.74117647060000003 0 0 0.6265060241 0 0.34090909089999999 1 0.66666666669999997 0.31034482759999998 0 0 0.28494623660000001 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 1 4.1619326844042348E-3 2.3195809263739875E-2 -5.2741492654689637E-2 -1.5388523936998411E-2 -0.11197896910451277 -0.1519195870635528 -3.50995844476234E-2 -0.14914781666833221 3.3822822809407072E-2 0.18659851364921032 -0.15168580273356724 -0.243 69111038771507 -0.11882179005493754 0.19205816501026618 -0.30056929042626557 6.2051746545261466E-2 8.4903324245836131E-2 -0.29689926491954566 -6.4720708960823714E-2 -4.581337095343585E-3 0.23251803035969587 -0.22177153523416848 -4.7590340465099246E-2 0.58375831809160239 0.20564023009232102 7.8755963432517007E-2 0.51090698090992248 -0.21565742915167085 -0.59800993265573865 -0.24711999599430712 -9.8209704566624367E-2 -3.8990250953823136E-3 -5.5355504595869576E-2 -5.7658674355479889E-2 -4.375948909108196E-2 -0.178521711300764 -0.19075497458069535 -0.40586574782538953 -0.38559358255629306 -0.53311529755166731 -0.42592904216059857 -0.29239764953607583 -8.6497752886350199E-2 -0.35240226484524229 0.11415270411667677 -0.35849188370524487 -0.15818782293305489 -0.18452942419721019 -0.21652533911472283 1.680797429818881E-2 -0.27537865761999392 0.15951116759276296 0.12590719938752137 -0.16342150660754662 -0.42536415507265168 -0.22835882432011978 8.9927975303165653E-2 0.12449942049117801 7.520258121907375E-3 -8.7462204467250615E-2 -3.5289428326276351E-2 5.1975353746005482E-3 3.6064692811349275E-2 0.10023555303772191 1.1911670346099967E-2 -0.10453971658825978 -3.8653703379468496E-2 -8.980750447451058E-2 -0.13331369770756929 0.13867183061905131 0.1626582868489912 -4.5002036422078007E-2 0.30154294191599718 0.11106628813392838 0.21182396355530209 0.20705805331344607 0.21079143055112581 0.11632943894721137 0.14665369287286367 -9.4179775507331698E-2 -0.17803381911985916 -0.29383947362691742 0.23099827542137774 0.64558933698992327 -0.16821576614753475 0.16259387686853444 -2.5038248442665889E-3 0.10220658045315467 -0.1828876 5864943457 -7.8405690316003529E-3 6.5938237470939853E-2 0.2210175550128799 0.14626084595022704 7.3289147308987168E-2 0.29071706791229701 0.35243190802640534 0.27336583393334735 0.32318398313523744 0.11620514421322881 0.18615216968668058 0.10920321212555706 0.34791263305617015 0.22558213564022722 5.584432840769793E-2 8.2095207710736418E-2 0.29650149017881589 3.5559785833669366E-2 -0.30276016515534643 0.15350278672502249 -3.4799870642736153E-2 6.848710003845504E-2 -1.0882456426855924E-2 0.26657008836983032 0.2980442219098336 -2.4566409930200095E-2 0.25623375980823759

disp2

Residuals

REG_2_LOG_MULT_2

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.7425824783
R Square 0.551428737
Adjusted R Square 0.5394134353
Standard Error 0.2251152915
Observations 116
ANOVA
df SS MS F Significance F
Regression 3 6.9772769723 2.3257589908 45.8938736128 2.02214697271537E-19
Residual 112 5.6758121828 0.0506768945
Total 115 12.6530891551
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 5.3443979195 0.5924342691 9.0210816591 0 4.1705653565 6.5182304826 4.1705653565 6.5182304826
LOGprice1 0.3232239202 0.1085445528 2.9777995463 0.0035592979 0.1081568025 0.5382910378 0.1081568025 0.5382910378
LOGprice2 -1.6444383322 0.206202555 -7.9748688466 0 -2.0530022628 -1.2358744015 -2.0530022628 -1.2358744015
feat2 0.3146670571 0.0684903168 4.5943291251 0.0000114483 0.1789622738 0.4503718403 0.1789622738 0.4503718403
Prediction Model:
RESIDUAL OUTPUT X LN if needed:
Intercept 1 1
Observation Predicted LOGsales2 Residuals LOGprice1 0.0453214084 -3.0939757655
1 9.0374564213 0.0042285846 LOGprice2 0.055 -2.9004220937
2 9.0374564213 -0.0072001112 feat2 1 1
3 9.0374564213 -0.0786590752 Predicted LOGsales2 9.4285832712
4 9.0374564213 -0.0542676221 Predicted sales 2 12438.8917062984
5 9.0374564213 -0.1423748896
6 9.0374564213 -0.1556201163
7 9.0374564213 -0.0542676221
8 9.0374564213 -0.1556201163
9 9.3521234784 0.0162457578
10 9.0374564213 0.2090229972
11 9.0030086437 -0.1345953591
12 9.0030086437 -0.2339678329
13 9.0030086437 -0.1211723387
14 9.0374564213 0.1903308642
15 9.0183768397 -0.2951455649
16 9.0212719968 0.042885865
17 9.7205220229 0.1270298973
18 9.1114211908 -0.2985777572
19 9.1114211908 -0.0362134928
20 9.1114211908 0.0172751922
21 9.1249471777 0.314598337
22 9.0595946108 -0.2047869782
23 9.2245799456 -0.065111904
24 9.6249786424 0.6126928162
25 9.5120000668 0.1703419371
26 9.1116013699 0.0377143002
27 9.1375771018 0.4637236921
28 10.1133625686 -0.2760148186
29 9.7986955116 -0.5996183319
30 9.6989656725 -0.2367776811
31 9.2245799456 -0.1493722476
32 9.2245799456 -0.0550615682
33 9.2245799456 -0.065111904
34 9.2245799456 -0.006271404
35 9.3382652482 -0.0917858296
36 9.3382652482 -0.1992060782
37 9.1132686273 -0.1794725833
38 9.3382652482 -0.4044692042
39 9.1660853907 -0.382229494
40 9.8132773481 -0.4867552218
41 9.1660853907 -0.4586024728
42 9.1660853907 -0.3250710802
43 9.3382652482 -0.1010882222
44 9.2114715817 -0.3844435132
45 9.6249786424 0.1267553549
46 9.6249786424 -0.3601500852
47 9.1660853907 -0.1709204004
48 9.1660853907 -0.1590859427
49 9.3382652482 -0.188949578
50 9.7455008925 -0.0050029684
51 9.2586539779 -0.2754651786
52 9.1660853907 0.1604367356
53 9.8520314301 0.1059443077
54 9.7095442306 -0.1823514089
55 9.1970395451 -0.3560252347
56 9.1970395451 -0.166783235
57 9.3382652482 0.1012802665
58 9.3382652482 0.1533366285
59 9.7825393552 0.00755551
60 9.2956924406 -0.077383899
61 9.3382652482 -0.0203268656
62 9.2684936306 -0.0036650733
63 9.3202563269 0.0315836074
64 9.2955130769 0.1130222011
65 9.2922315876 0.025706795
66 9.3183307495 -0.090543464
67 9.6632446575 -0.0490404588
68 9.3264411761 -0.0707451024
69 9.2944229446 -0.1451072744
70 9.2976823731 0.1264782228
71 9.1660853907 0.1431949292
72 9.2676764844 -0.0398891988
73 9.5843957673 0.3078285929
74 9.4989107145 0.1152934842
75 9.2699750996 0.1996478703
76 9.2252866725 0.1910917829
77 9.2193585193 0.1891767587
78 9.2114715817 0.0890750582
79 9.2385354091 0.1133045251
80 9.8990628244 -0.0876905608
81 9.8990628244 -0.1756593337
82 9.4892012595 -0.2804621689
83 9.1660853907 0.2657972513
84 9.6123494301 0.6950019492
85 9.1660853907 -0.1358290805
86 9.1318498302 0.1774304897
87 9.1318498302 0.01746584
88 9.1535642398 0.1021318339
89 9.2700936947 -0.1948859967
90 9.304903757 -0.0310253642
91 9.7012041844 0.0942526239
92 9.3939174886 0.2702971307
93 9.2531468189 0.1710137769
94 9.99323299 0.0349470065
95 9.2976823731 0.2773011125
96 9.2976823731 0.3355700206
97 9.2976823731 0.2839456552
98 9.2976823731 0.3229116238
99 9.2385354091 0.1133045251
100 9.2006177714 0.1677514648
101 9.1660853907 0.089610683
102 9.3902509603 0.3331525304
103 9.3306364526 0.2575922597
104 9.2976823731 0.062456364
105 9.2976823731 0.0706868631
106 9.8990628244 0.3000749703
107 9.3099038798 0.0502348572
108 9.6720937983 -0.3119550613
109 9.2976823731 0.1418631416
110 9.2344438307 -0.0161352891
111 9.1599280387 0.122919024
112 9.1280840059 -0.0311363213
113 9.1462850128 0.2543450856
114 9.1936528484 0.2833502288
115 9.1648535988 -0.0053855572
116 9.5842938687 0.2683213535

LOGprice1 Residual Plot

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LOGprice1

Residuals

LOGprice2 Residual Plot

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LOGprice2

Residuals

feat2 Residual Plot

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feat2

Residuals

Work_Q4