Question: The Return on all Ords Data: Date 30-Day Bill Rate Rm RAMP RHVN RSUN Jan-13 Feb-13 0.0433 -2.8439 3.0075 27.0408 6.3208 Mar-13 0.0375 0.1029 -4.9270

Compute E(R), and Coefficient of Variation for all three-return series over the 01/02/2013 - 10/06/2023 time. Make a semantic object model for a pizza order. The order willThe Return on all Ords Data:

Date30-Day Bill RateRmRAMPRHVNRSUN
Jan-13




Feb-130.0433-2.84393.007527.04086.3208
Mar-130.03750.1029-4.92709.63857.1231
Apr-130.03003.79536.08909.89019.8139
May-130.0233-4.9121-4.0741-14.9717-3.9291
Jun-130.0250-2.8383-17.95371.5937-4.4106
Jul-130.01925.44866.35294.31387.5503
Aug-130.02001.72574.867313.1579-3.8222
Sep-130.02835.8115-2.74265.64787.6789
Oct-130.0317-1.95565.37652.51582.3719
Nov-130.03420.7301-1.68780.4885-1.4200
Dec-130.0483-2.7648-5.7939-2.1672-0.6823
Jan-140.04674.0403-2.7335-5.0633-7.0229
Feb-140.0400-0.229013.11477.0000-0.3284
Mar-140.05271.60283.10562.80379.0031
Apr-140.0213-2.41733.8789-0.60601.1655
May-140.01832.16364.7525-0.62112.5346
Jun-140.0308-1.65550.1890-3.12501.4232
Jul-140.03174.47593.5849-0.32255.5391
Aug-140.03330.02677.103814.88670.9797
Sep-140.0375-2.1264-7.14292.2535-0.7515
Oct-140.0300-3.75849.47814.68324.3447
Nov-140.02921.7138-3.58980.6470-2.1160
Dec-140.03253.0210-2.4823-8.9431-1.9526
Jan-150.02836.24674.909122.31634.6942
Feb-150.0317-0.620516.117811.8987-5.0272
Mar-150.0342-1.5012-3.88060.6787-0.8638
Apr-150.0483-1.26602.0721-1.1236-2.8518
May-150.04671.29983.41629.06963.1643
Jun-150.0400-5.6053-9.6096-4.0425-0.7391
Jul-150.05274.22849.8007-1.10866.2546
Aug-150.0213-8.0821-9.9849-2.6906-10.0911
Sep-150.04171.2657-6.5546-10.5991-4.1876
Oct-150.0500-1.33125.54092.57737.5471
Nov-150.05922.42231.39625.31072.1358
Dec-150.0572-5.38860.34422.4510-9.3353
Jan-160.0572-2.1497-7.89026.2201-4.3657
Feb-160.05444.1209-0.93117.6577-3.5314
Mar-160.03671.09098.8346-1.67369.2004
Apr-160.01832.07374.2991-4.68094.9538
May-160.01172.4793-4.08174.75923.8400
Jun-160.0253-2.5221-8.51061.0965-6.1633
Jul-160.04176.282012.59694.989210.2627
Aug-160.0483-2.0305-9.466411.1570-5.3611
Sep-160.0467-0.07603.0368-3.3458-2.0025
Oct-160.0400-0.4127-13.4469-2.8846-1.1561
Nov-160.05274.68162.8446-2.69064.4277
Dec-160.0213-1.46537.23418.21058.1600
Jan-170.03501.5047-0.7936-2.7237-3.6242
Feb-170.02132.4787-2.40003.00001.8419
Mar-170.01830.00009.1558-12.03882.0384
Apr-170.03080.74193.4749-7.50554.4663
May-170.0317-3.1324-5.7835-7.12810.2899
Jun-170.03330.04692.77221.32637.0809
Jul-170.03750.17183.853614.3979-3.5763
Aug-170.03000.0416-5.3803-6.6361-8.8173
Sep-170.0292-0.0242-2.5338-4.90203.1977
Oct-170.03254.87112.8985-2.57734.0613
Nov-170.02671.83452.81699.45245.5229
Dec-170.0283-0.33731.56563.9900-3.2798
Jan-180.0467-0.47511.15608.3933-1.4430
Feb-180.0542-4.06060.7619-11.2832-0.7321
Mar-180.05250.0000-3.0533-7.98000.8501
Apr-180.04923.4538-19.0381-4.60715.0975
May-180.04580.8548-3.46535.6818-4.2083
Jun-180.04332.7141-8.7180-7.77788.6374
Jul-180.05080.7075-4.49446.92772.6045
Aug-180.04921.4777-1.76471.40843.4736
Sep-180.0500-1.7813-1.6400-0.6451-6.1652
Oct-180.0492-8.9335-22.5705-9.3751-3.1120
Nov-180.0492-0.6940-1.61944.2430-4.9250
Dec-180.05083.99170.82300.0000-5.1802
Jan-190.04925.3964-7.75516.64552.7712
Feb-190.03170.06394.42486.82504.3914
Mar-190.04832.5025-9.482811.66673.7251
Apr-190.03500.63108.09523.7313-3.7010
May-190.04500.5094-4.40532.8376-1.2352
Jun-190.04753.1979-2.3041-2.16343.3768
Jul-190.04252.9749-15.56607.86240.2969
Aug-190.0692-2.9063-5.3073-0.22782.2206
Sep-190.06441.11527.66966.00531.8944
Oct-190.05582.58530.5479-9.7130-1.2854
Nov-190.0433-2.09566.539510.4038-0.5204
Dec-190.03752.5947-2.0460-5.3488-3.1390
Jan-200.03002.0404-4.69973.9312-0.7716
Feb-200.0233-8.5841-8.2192-12.2931-11.8196
Mar-200.0250-21.0799-20.2985-20.4852-17.7839
Apr-200.01928.95347.1161-5.76270.6572
May-200.02005.011013.986022.20370.4352
Jun-200.02832.094213.80368.58900.0000
Jul-200.03174.0758-21.02426.9123-7.3673
Aug-200.0342-3.78814.436818.81728.7719
Sep-200.05002.0618-14.70592.4887-8.1864
Oct-200.05929.927925.4689-1.9867-2.8402
Nov-200.05721.609311.76477.848022.8989
Dec-200.05720.2963-8.77201.7353-3.4688
Jan-210.05441.0144-4.807713.85933.4907
Feb-210.03671.10511.0101-1.8727-1.3889
Mar-210.01833.8961-15.66679.35112.1215
Apr-210.01170.4087-11.8577-4.82896.1616
May-210.02531.17750.89680.94885.8040
Jun-210.04172.40730.00003.0075-0.0899
Jul-210.04830.5893-7.55553.46713.8704
Aug-210.0467-1.04725.7692-5.11468.1456
Sep-210.04001.1828-10.0000-6.50561.2750
Oct-210.0527-0.67689.0909-1.3916-6.6879
Nov-210.02132.2643-4.16665.3278-7.6792
Dec-210.0350-6.3267-2.4154-2.56412.3105
Jan-220.04500.9012-13.3664-0.6073-0.2710
Feb-220.04754.41937.99996.9246-2.4457
Mar-220.04251.71982.11641.90485.2623
Apr-220.0692-0.831920.2073-1.40643.0576
May-220.0644-3.4901-5.6034-13.9489-0.9599
Jun-220.0558-9.5061-12.7854-15.2968-3.2599
Jul-220.04336.333713.612511.59031.9126
Aug-220.03750.72907.37332.1739-2.5916
Sep-220.0300-7.5725-6.0085-4.4917-6.6118
Oct-220.023312.005015.06852.970313.7587
Nov-220.02500.56147.93656.66124.4698
Dec-220.01922.1721-3.3088-2.82351.0067
Jan-230.0200-3.00671.52098.23244.0698
Feb-230.0283-1.0959-22.4719-13.87022.5539
Mar-230.03171.73191.4493-7.0130-3.3061
Apr-230.0342-1.931710.77084.48953.0553
May-230.0544-1.1229-3.5242-6.64826.5705
Jun-230.03671.75983.19633.26411.4286
 

Make a semantic object model for a pizza order. The order will have a phone number which will uniquely identify it. It will also have an address to where the pizza is to be delivered (a group attribute). Finally, it will have one or more pizzas, another semantic object. Each pizza will have an order number that uniquely identifies it (distinct from the phone number of the order) and a size (small, medium, or large for example). In addition, each pizza will have a pepperoni attribute (no topping, one topping, double topping) as well as mushroom and sausage attributes (also no topping, one topping, double topping). Part 2. Draw an entity-relationship diagram to capture the above pizza order and pizza. Part 3. Convert either the above semantic object model or entity-relationship model into a relational model.

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