Chat with us, powered by LiveChat At the beginning of this class, you collected some international 'flight data' for two times of the year. You were asked if | Wridemy

At the beginning of this class, you collected some international ‘flight data’ for two times of the year. You were asked if

At the beginning of this class, you collected some international "flight data" for two times of the year. You were asked if there appeared to be a difference. You now have the tools to answer that question statistically. Go back. Use that data. What do you conclude?

Use the data set "Birthweight_smoker" Is there a difference in birth weight for babies whose mother was a non-smoker vs a smoker? Be sure to include summary statistics and any figures that benefit the analysis. Your response can simply be a paragraph stating yes or no and what parameters indicate there is a statistically significant difference or why the parameters indicate there is not a statistically significant difference.

Use the data set "stkmrkt_season"and perform an ANOVA analysis to determine if there are differences in the months of the year. What is the conclusion? Your response can simply be a paragraph stating yes or no and what parameters indicate there is a statistically significant difference or why the parameters indicate there is not a statistically significant difference. Note: The data source for this is:

  • C-C. Chien, C-F. Lee, A.M.L. Wang (2002). "A Note on Stock Market Seasonality: The Impact of Stock Price Volatility on the Application of Dummy Variable Regression Model," The Quarterly Review of Economics and Finance, Vol. 42, pp. 155-162. 

There are three different data sets and each will need to be analyzed and results presented. Think about what you have studied to date (format data, plot data, which analysis to perform, user-friendly, present results and provide the correct evidence supporting your conclusion). Your submission will be on workbook but each data set must be presented and analyzed on a separate tab. Your conclusions/write-up should be a separate tab and include each of the write-ups. The write-ups can (should) be short; three sentences at the most. Include all write-ups on one tab.

month vwmr
1 0.086582095
1 0.032717217
1 0.014840943
1 -0.010237348
1 -0.00541302
1 0.04962986
1 0.12675767
1 0.006146815
1 -0.060963936
1 -0.024779683
1 0.028475084
1 -0.00273007
1 0.004823829
1 0.005246674
1 -0.059965979
1 0.005040938
1 0.060583307
1 0.052305072
1 0.044298696
1 -0.007376929
1 0.004075116
1 -0.050323594
1 0.011583767
1 0.028308799
1 0.106997825
1 0.068788722
1 -0.012413152
1 0.033751972
1 -0.047073569
1 0.062100462
1 0.06723766
1 0.129515548
1 0.036950655
1 -0.011281445
1 0.1414
1 -0.0732
2 0.029098351
2 0.038238388
2 0.074419805
2 -0.01434395
2 0.033636267
2 0.039485709
2 0.006854789
2 0.017544606
2 -0.026200134
2 0.022511637
2 -0.015982699
2 0.033802703
2 -0.019613443
2 0.01755595
2 -0.020766884
2 -0.001020415
2 -0.043983578
2 0.011279138
2 0.039448329
2 0.013080813
2 -0.05099688
2 -0.013338813
2 0.061490387
2 0.066544429
2 -0.031564872
2 -0.004995616
2 0.018680995
2 0.01754802
2 -0.01200509
2 -0.024275867
2 0.001159948
2 0.040249181
2 0.007834205
2 -0.014275405
2 0.0757
2 -0.0524
3 -0.003231685
3 0.056769574
3 -0.001605768
3 0.032664051
3 0.052729228
3 0.023615805
3 -0.000891056
3 0.009918868
3 -0.002889188
3 0.045819355
3 0.00681647
3 0.054945603
3 -0.004852615
3 -0.00727402
3 0.015105028
3 0.01173667
3 -0.003233874
3 -0.033731854
3 0.011903123
3 -0.00924104
3 0.041710098
3 -0.015927397
3 0.042952501
3 -0.007328556
3 0.019334124
3 0.036502007
3 -0.003638576
3 0.0267528
3 0.008560349
3 -0.021096387
3 0.003403297
3 0.056922451
3 0.004987699
3 -0.012107083
3 0.0656
3 -0.1201
4 0.009133414
4 0.020237393
4 0.038307824
4 0.04376018
4 0.013600427
4 0.044356051
4 -0.011395438
4 -0.057071442
4 -0.00293874
4 -0.004215499
4 -0.056248329
4 0.026952716
4 -0.01195951
4 0.004160989
4 -0.03517908
4 0.026520668
4 0.065359359
4 -0.014685235
4 0.007441476
4 0.067020949
4 0.037686144
4 0.065990408
4 0.010543618
4 0.006293946
4 -0.004035604
4 0.052160148
4 -0.026471264
4 0.019577584
4 0.009356103
4 0.07578265
4 -0.00687984
4 -0.032929903
4 0.017418946
4 0.00954889
4 0.0941
4 -0.1053
5 -0.006349846
5 0.009157479
5 -0.037515773
5 0.001812185
5 -0.013016646
5 -0.063423666
5 0.001842605
5 -0.006920079
5 0.051457506
5 -0.009371212
5 0.031304801
5 0.016776529
5 -0.031530182
5 0.024883667
5 0.008067058
5 0.017597091
5 0.034824906
5 0.015772695
5 0.067290775
5 0.015828392
5 -0.032130346
5 0.037760447
5 -0.031041382
5 -0.05541352
5 -0.016580242
5 -0.014295008
5 -0.009491569
5 0.068047174
5 0.069057084
5 0.037208087
5 -0.032824955
5 0.012592504
5 0.028839884
5 0.006983558
5 0.0889
5 -0.0845
6 0.033996787
6 -0.022317837
6 0.004835248
6 0.046236476
6 0.038473628
6 -0.05121938
6 0.031061859
6 -0.010772396
6 -0.020031873
6 -0.035499829
6 -0.009838849
6 0.022682506
6 0.04695248
6 0.004811987
6 -0.002078108
6 -0.006280685
6 0.031301415
6 -0.03484941
6 0.008423855
6 0.033506102
6 0.03481383
6 0.013854142
6 -0.037488064
6 -0.026394144
6 -0.023124088
6 -0.046619375
6 0.001597509
6 0.040050497
6 -0.079237274
6 0.024949241
6 -0.006291273
6 -0.024705394
6 0.025326998
6 0.021373419
6 0.0516
6 -0.0827
7 -0.006009166
7 0.066040667
7 0.005199458
7 -0.027765819
7 0.069638398
7 0.002310955
7 0.032104551
7 0.053785671
7 0.02668766
7 0.060575595
7 -0.018984957
7 0.071926837
7 -0.05824734
7 -0.02600218
7 -0.011206106
7 0.019975031
7 -0.006906482
7 0.033291087
7 0.027086533
7 0.036933962
7 0.067507506
7 -0.043119744
7 0.048009606
7 0.001006375
7 -0.019448517
7 -0.056502328
7 -0.021196008
7 -0.000287653
7 -0.062711784
7 -0.002591495
7 0.030513567
7 -0.038276006
7 0.010921788
7 -0.040059659
7 0.0771
7 -0.0709
8 0.000137618
8 0.032904008
8 -0.073534718
8 0.081768543
8 -0.038497508
8 0.034481859
8 0.073899532
8 -0.030745798
8 -0.021455574
8 0.037293492
8 -0.026372928
8 -0.031206093
8 0.05666076
8 0.063076148
8 -0.005932548
8 0.080892511
8 0.067829248
8 -0.000448707
8 -0.026246773
8 -0.021379457
8 0.050461201
8 0.020620664
8 0.024160538
8 0.001486458
8 0.008257566
8 -0.014984243
8 0.041225539
8 -0.009360204
8 0.070879556
8 0.022239237
8 0.036140146
8 0.053008803
8 0.000301385
8 -0.05926026
8 0.119
8 -0.0917
9 -0.007104741
9 -0.043881161
9 0.021271336
9 0.0152748
9 -0.033676456
9 -0.00555404
9 0.042847883
9 0.042414666
9 -0.039490278
9 0.026919274
9 0.004756603
9 0.013346199
9 -0.028035744
9 -0.006204008
9 -0.066972458
9 -0.023667722
9 -0.009471178
9 0.043377233
9 -0.034156282
9 0.010497226
9 0.003546781
9 -0.044158906
9 0.009867121
9 0.052478811
9 0.031449628
9 0.037731868
9 -0.006420941
9 0.032033856
9 -0.077226665
9 -0.040543847
9 -0.005145784
9 0.004155291
9 0.017561022
9 0.024280611
9 0.054
9 -0.1097
10 -0.008442866
10 -0.009355277
10 -0.003782697
10 -0.01273052
10 0.005673888
10 0.047878098
10 0.030436217
10 -0.007576321
10 -0.018232149
10 0.006526596
10 0.002166304
10 0.062269191
10 -0.084681324
10 -0.030066339
10 0.037082883
10 0.038825002
10 -0.016487687
10 0.002135263
10 0.086591631
10 -0.035343712
10 0.085784415
10 0.024271317
10 0.055121904
10 -0.011236265
10 -0.036246287
10 0.027695435
10 0.02866594
10 -0.006977877
10 -0.02600441
10 0.06566691
10 -0.045366066
10 0.071109355
10 -0.014117057
10 -0.068353494
10 0.1656
10 -0.2249
11 0.027761704
11 0.028627216
11 -0.068254517
11 0.03832102
11 0.025458624
11 -0.027961387
11 0.005440516
11 0.094143885
11 0.044463424
11 -0.036563243
11 0.017224017
11 0.082307143
11 0.014917744
11 -0.006461295
11 0.0594283
11 -0.004647018
11 0.004576502
11 0.047282276
11 0.083576754
11 -0.018800475
11 0.004645295
11 0.010928393
11 0.048788634
11 0.074192429
11 0.080116161
11 0.004710666
11 0.050246172
11 -0.020697477
11 -0.007498581
11 -0.033407393
11 -0.029613054
11 0.036881798
11 0.042482901
11 0.022182864
11 0.1101
11 -0.1209
12 0.056382775
12 0.044657683
12 -0.026295999
12 -0.029770682
12 0.013579323
12 0.01575871
12 0.029554151
12 -0.027838248
12 0.004149611
12 0.020939478
12 -0.033067642
12 0.016524898
12 0.062478179
12 -0.029155553
12 0.047151427
12 0.039681524
12 0.039775825
12 0.049853096
12 0.012071626
12 0.016910616
12 -0.014988895
12 0.025404049
12 0.045644443
12 0.016665422
12 0.042990819
12 0.044791124
12 -0.02306595
12 0.020569862
12 -0.001206353
12 0.007074266
12 0.007062637
12 -0.018560609
12 0.036718113
12 -0.012939723
12 0.1071
12 -0.0342

,

id headcirumference length Birthweight smoker
1313 12 17 5.8 0
431 12 19 4.2 1
808 13 19 6.4 0
300 12 18 4.5 1
516 13 18 5.8 1
321 13 19 6.8 0
1363 12 19 5.2 1
575 12 19 6.1 1
822 13 19 7.5 0
1081 14 21 8 0
1636 14 20 8.6 0
1107 14 20 7.1 0
1023 13 20 6.6 1
1369 13 19 7 1
697 13 19 6.6 0
1600 13 21 6.3 0
57 14 20 7.3 1
272 14 20 8.5 1
569 13 19 5.5 1
619 13 20 7.5 1
1522 13 19 6 1
820 13 20 8.3 0
1016 14 21 9.5 0
1058 13 20 6.9 0
1088 14 20 7.2 0
365 14 20 7.7 1
532 13 21 7.9 1
752 14 19 7.3 1
792 14 21 8 1
1272 12 20 6 1
462 15 22 9 0
755 13 21 7 0
1683 13 21 7.3 0
27 14 20 7.8 1
1262 13 21 7 1
1388 13 20 6.9 1
1764 15 22 10 1
553 14 21 8.6 0
1191 13 21 8 0
1360 13 22 10 0
223 13 19 8.5 1
1187 14 20 8.9 0

,

Sheet1

Year Month Domestic International Total Mean 81,620,168
2016 1 52,474,245 16,492,079 68,966,324 Median 82,229,641
2016 2 51,111,616 14,421,545 65,533,161 Range 55,812,275
2016 3 61,593,610 17,382,692 78,976,302 Standard Deviation 9642546.38104557
2016 4 58,894,278 16,596,730 75,491,008 Variance 91155588931975.3
2016 5 62,752,723 17,868,001 80,620,724 June, July, August 2016 2017
2016 6 64,757,943 19,407,251 84,165,194 Mean 85,296,088 89,121,911
2016 7 66,135,126 21,435,123 87,570,249 Median 84,165,194 88,677,138
2016 8 63,498,615 20,654,207 84,152,822 Rnage 3,417,427 3,842,854
2016 9 58,620,581 17,161,889 75,782,470 Standard Deviation 1969490.62449059 1959655.26562437
2016 10 61,709,421 16,905,446 78,614,867 Variance 2585928879970.89 2560165840059.56
2016 11 59,269,837 15,653,035 74,922,872 January, February, March 2016 2017
2016 12 59,178,833 18,014,473 77,193,306 Mean 71,158,596 73,086,734
2017 1 54,109,329 17,488,782 71,598,111 Median 68,966,324 71,598,111
2017 2 51,078,172 14,709,988 65,788,160 Rnage 13,443,141 16,085,770
2017 3 63,963,808 17,910,122 81,873,930 Standard Deviation 6984558.05916325 8145550.76136723
2017 4 61,099,004 18,686,523 79,785,527 Variance 32522700854548.2 44233331470673.6
2017 5 64,442,977 18,865,393 83,308,370 The number of passengers in June, July and August is definitely higher than in January, February and March. The reason is judged to be an increase in passengers due to vacations and student vacations.
2017 6 66,746,463 20,676,407 87,422,870 The range in June, July, and August of 2016 and 2017 is around 3000~4000, but the number of passengers in March is higher than in January and February. Thus, the range for January, February, and March is more than 10,000 different.
2017 7 68,575,481 22,690,243 91,265,724
2017 8 66,664,019 22,013,119 88,677,138
2017 9 57,144,508 17,438,756 74,583,264
2017 10 64,625,405 17,604,236 82,229,641
2017 11 61,911,765 16,476,642 78,388,407
2017 12 61,374,167 18,469,636 79,843,803
2018 1 55,831,159 17,897,787 73,728,946
2018 2 54,078,219 15,720,901 69,799,120
2018 3 66,644,135 19,716,774 86,360,909
2018 4 64,557,780 19,111,955 83,669,735
2018 5 67,846,119 20,126,585 87,972,704
2018 6 70,279,158 22,046,732 92,325,890
2018 7 72,540,389 23,754,573 96,294,962
2018 8 70,338,563 22,909,627 93,248,190
2018 9 60,468,575 18,051,374 78,519,949
2018 10 67,082,086 18,830,854 85,912,940
2018 11 64,660,022 17,552,376 82,212,398
2018 12 63,646,582 19,520,179 83,166,761
2019 1 58,034,262 18,690,817 76,725,079
2019 2 55,679,481 16,280,536 71,960,017
2019 3 70,234,129 20,295,947 90,530,076
2019 4 66,938,654 20,019,304 86,957,958
2019 5 71,364,145 20,995,113 92,359,258
2019 6 72,790,418 22,747,453 95,537,871
2019 7 75,281,916 23,907,859 99,189,775
2019 8 72,729,199 23,239,180 95,968,379
2019 9 63,991,365 18,842,310 82,833,675
2019 10 69,936,836 18,982,654 88,919,490
2019 11 64,827,417 17,575,113 82,402,530
2019 12 69,737,438 19,859,635 89,597,073
2020 1 61,638,893 18,955,398 80,594,291
2020 2 59,879,630 15,829,198 75,708,828
2020 3 34,420,555 8,956,945 43,377,500

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