Questions
Question 2. The following tables provide some example data that will be kept in the database....

Question 2. The following tables provide some example data that will be kept in the database. Write the INSERT commands necessary to place the following data in the tables that were created in Question 1. Alternatively provide the text files (copy and pasted into your final report) and the open/insert from file commands..

Table: actor

act_id |      act_fname       |      act_lname       | act_gender
    101 | James                | Stewart              | M
    102 | Deborah              | Kerr                 | F
    103 | Peter                | OToole               | M
    104 | Robert               | De Niro              | M
    105 | F. Murray            | Abraham              | M
    106 | Harrison             | Ford                 | M
    107 | Nicole               | Kidman               | F
    108 | Stephen              | Baldwin              | M
    109 | Jack                 | Nicholson            | M
    110 | Mark                 | Wahlberg             | M
    111 | Woody                | Allen                | M
    112 | Claire               | Danes                | F
    113 | Tim                  | Robbins              | M
    114 | Kevin                | Spacey               | M
    115 | Kate                 | Winslet              | F
    116 | Robin                | Williams             | M
    117 | Jon                  | Voight               | M
    118 | Ewan                 | McGregor             | M
    119 | Christian            | Bale                 | M
    120 | Maggie               | Gyllenhaal           | F
    121 | Dev                  | Patel                | M
    122 | Sigourney            | Weaver               | F
    123 | David                | Aston                | M
    124 | Ali                  | Astin                | F

Table: movie_cast

act_id | mov_id |              role

    101 |    901 | John Scottie Ferguson

    102 |    902 | Miss Giddens

    103 |    903 | T.E. Lawrence

    104 |    904 | Michael

    105 |    905 | Antonio Salieri

    106 |    906 | Rick Deckard

    107 |    907 | Alice Harford

    108 |    908 | McManus

    110 |    910 | Eddie Adams

    111 |    911 | Alvy Singer

    112 |    912 | San

    113 |    913 | Andy Dufresne

    114 |    914 | Lester Burnham

    115 |    915 | Rose DeWitt Bukater

    116 |    916 | Sean Maguire

    117 |    917 | Ed

    118 |    918 | Renton

    120 |    920 | Elizabeth Darko

    121 |    921 | Older Jamal

    122 |    922 | Ripley

    114 |    923 | Bobby Darin

    109 |    909 | J.J. Gittes

    119 |    919 | Alfred Borden

Table: movie

mov_id |                     mov_title                      | mov_year | mov_time |    mov_lang     | mov_dt_rel | mov_rel_country
    901 | Vertigo                                            |     1958 |      128 | English         | 1958-08-24 | UK
    902 | The Innocents                                      |     1961 |      100 | English         | 1962-02-19 | SW
    903 | Lawrence of Arabia                                 |     1962 |      216 | English         | 1962-12-11 | UK
    904 | The Deer Hunter                                    |     1978 |      183 | English         | 1979-03-08 | UK
    905 | Amadeus                                            |     1984 |      160 | English         | 1985-01-07 | UK
    906 | Blade Runner                                       |     1982 |      117 | English         | 1982-09-09 | UK
    907 | Eyes Wide Shut                                     |     1999 |      159 | English         |            | UK
    908 | The Usual Suspects                                 |     1995 |      106 | English         | 1995-08-25 | UK
    909 | Chinatown                                          |     1974 |      130 | English         | 1974-08-09 | UK
    910 | Boogie Nights                                      |     1997 |      155 | English         | 1998-02-16 | UK
    911 | Annie Hall                                         |     1977 |       93 | English         | 1977-04-20 | USA
    912 | Princess Mononoke                                  |     1997 |      134 | Japanese        | 2001-10-19 | UK
    913 | The Shawshank Redemption                           |     1994 |      142 | English         | 1995-02-17 | UK
    914 | American Beauty                                    |     1999 |      122 | English         |            | UK
    915 | Titanic                                            |     1997 |      194 | English         | 1998-01-23 | UK
    916 | Good Will Hunting                                  |     1997 |      126 | English         | 1998-06-03 | UK
    917 | Deliverance                                        |     1972 |      109 | English         | 1982-10-05 | UK
    918 | Trainspotting                                      |     1996 |       94 | English         | 1996-02-23 | UK
    919 | The Prestige                                       |     2006 |      130 | English         | 2006-11-10 | UK
    920 | Donnie Darko                                       |     2001 |      113 | English         |            | UK
    921 | Slumdog Millionaire                                |     2008 |      120 | English         | 2009-01-09 | UK
    922 | Aliens                                             |     1986 |      137 | English         | 1986-08-29 | UK
    923 | Beyond the Sea                                     |     2004 |      118 | English         | 2004-11-26 | UK
    924 | Avatar                                             |     2009 |      162 | English         | 2009-12-17 | UK
    926 | Seven Samurai                                      |     1954 |      207 | Japanese        | 1954-04-26 | JP
    927 | Spirited Away                                      |     2001 |      125 | Japanese        | 2003-09-12 | UK
    928 | Back to the Future                                 |     1985 |      116 | English         | 1985-12-04 | UK
    925 | Braveheart                                         |     1995 |      178 | English         | 1995-09-08 | UK

Table: director

dir_id |      dir_fname       |      dir_lname
    201 | Fred                 | Caravanhitch
    202 | Jackie               | Claytonburry
    203 | Greene               | Lyon
    204 | Miguel               | Camino
    205 | George               | Forman
    206 | Antartic             | Scott
    207 | Stanlee              | Carbrick
    208 | Bryon                | Sanger
    209 | Roman                | Polanski
    210 | Paul                 | Thomas Anderson
    211 | Woody                | Allen
    212 | Hayao                | Miyazaki
    213 | Frank                | Darabont
    214 | Sam                  | Mendes
    215 | James                | Cameron
    216 | Gus                  | Van Sant
    217 | John                 | Boorman
    218 | Danny                | Boyle
    219 | Christopher          | Nolan
    220 | Richard              | Kelly
    221 | Kevin                | Spacey
    222 | Andrei               | Tarkovsky
    223 | Peter                | Jackson

Table: movie_direction

dir_id | mov_id
    201 |    901
    202 |    902
    203 |    903
    204 |    904
    205 |    905
    206 |    906
    207 |    907
    208 |    908
    209 |    909
    210 |    910
    211 |    911
    212 |    912
    213 |    913
    214 |    914
    215 |    915
    216 |    916
    217 |    917
    218 |    918
    219 |    919
    220 |    920
    218 |    921
    215 |    922
    221 |    923

Table: genres

gen_id |      gen_title
   1001 | Action
   1002 | Adventure
   1003 | Animation
   1004 | Biography
   1005 | Comedy
   1006 | Crime
   1007 | Drama
   1008 | Horror
   1009 | Music
   1010 | Mystery
   1011 | Romance
   1012 | Thriller
   1013 | War

Table: movie_genres

mov_id | gen_id
    922 |   1001
    917 |   1002
    903 |   1002
    912 |   1003
    911 |   1005
    908 |   1006
    913 |   1006
    926 |   1007
    928 |   1007
    918 |   1007
    921 |   1007
    902 |   1008
    923 |   1009
    907 |   1010
    927 |   1010
    901 |   1010
    914 |   1011
    906 |   1012
    904 |   1013

Table: rating

mov_id | rev_id | rev_stars | num_o_ratings
    901 |   9001 |      8.40 |        263575
    902 |   9002 |      7.90 |         20207
    903 |   9003 |      8.30 |        202778
    906 |   9005 |      8.20 |        484746
    924 |   9006 |      7.30 |
    908 |   9007 |      8.60 |        779489
    909 |   9008 |           |        227235
    910 |   9009 |      3.00 |        195961
    911 |   9010 |      8.10 |        203875
    912 |   9011 |      8.40 |
    914 |   9013 |      7.00 |        862618
    915 |   9001 |      7.70 |        830095
    916 |   9014 |      4.00 |        642132
    925 |   9015 |      7.70 |         81328
    918 |   9016 |           |        580301
    920 |   9017 |      8.10 |        609451
    921 |   9018 |      8.00 |        667758
    922 |   9019 |      8.40 |        511613
    923 |   9020 |      6.70 |         13091

Table: reviewer

rev_id |            rev_name
   9001 | Righty Sock
   9002 | Jack Malvern
   9003 | Flagrant Baronessa
   9004 | Alec Shaw
   9005 |
   9006 | Victor Woeltjen
   9007 | Simon Wright
   9008 | Neal Wruck
   9009 | Paul Monks
   9010 | Mike Salvati
   9011 |
   9012 | Wesley S. Walker
   9013 | Sasha Goldshtein
   9014 | Josh Cates
   9015 | Krug Stillo
   9016 | Scott LeBrun
   9017 | Hannah Steele
   9018 | Vincent Cadena
   9019 | Brandt Sponseller
   9020 | Richard Adams

In: Computer Science

Plot the data on air travel delays. Can you see seasonal patterns? Explain. Use Megastat to...

Plot the data on air travel delays. Can you see seasonal patterns? Explain. Use Megastat to calculate estimated seasonal indices and trend. Which months have the most delays? The fewest? Is this logical? Is there a trend in the deseasonalized data?

National Airspace Total System Delays, 2002-2006
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 14,158 13,821 20,020 24,027 28,533 33,770 32,304 29,056 24,493 25,266 17,712 22,489
2003 16,159 18,260 25,387 17,474 26,544 27,413 32,833 37,066 28,882 21,422 34,116 31,332
2004 28,104 32,274 34,001 32,459 50,800 52,121 46,894 43,770 30,412 37,271 35,234 32,446
2005 32,121 30,176 34,633 25,887 30,920 48,922 58,471 45,328 32,949 34,221 34,273 29,766
2006 29,463 24,705 37,218 35,132 40,669 48,096 47,606 46,547 48,092 51,053 43,482

39,797
In Column Format
Year Month Delays
2002 Jan 14158
Feb 13821
Mar 20020
Apr 24027
May 28533
Jun 33770
Jul 32304
Aug 29056
Sep 24493
Oct 25266
Nov 17712
Dec 22489
2003 Jan 16159
Feb 18260
Mar 25387
Apr 17474
May 26544
Jun 27413
Jul 32833
Aug 37066
Sep 28882
Oct 21422
Nov 34116
Dec 31332
2004 Jan 28104
Feb 32274
Mar 34001
Apr 32459
May 50800
Jun 52121
Jul 46894
Aug 43770
Sep 30412
Oct 37271
Nov 35234
Dec 32446
2005 Jan 32121
Feb 30176
Mar 34633
Apr 25887
May 30920
Jun 48922
Jul 58471
Aug 45328
Sep 32949
Oct 34221
Nov 34273
Dec 29766
2006 Jan 29463
Feb 24705
Mar 37218
Apr 35132
May 40669
Jun 48096
Jul 47606
Aug 46547
Sep 48092
Oct 51053
Nov 43482
Dec 39797

In: Statistics and Probability

The following data is provided for the S&P 500 Index: Year Total Return Year Total Return...

The following data is provided for the S&P 500 Index:

Year Total Return Year Total Return
1988 16.81% 1998 28.58%
1989 31.49% 1999 21.04%
1990 -3.17% 2000 -9.11%
1991 30.55% 2001 -11.88%
1992 7.67% 2002 -22.10%
1993 9.99% 2003 28.70%
1994 1.31% 2004 10.87%
1995 37.43% 2005 4.91%
1996 23.07% 2006 15.80%
1997 33.36% 2007 5.49%

Refer to the information above. Calculate the 20-year arithmetic average annual rate of return on the S&P 500 Index.

Question 22 options:

13.04%

11.81%

10.56%

none of the above

In: Finance

(a) Develop a three-year moving average. (b) Develop a four-year moving average.

Question 1 Sales for the Forever Young Cosmetics Company (in $ millions) are as follows:

Year

Sales ($ millions)

Year

Sales ($ Millions)

Year

Sales ($ Milions

1996

2.4

2003

4.4

2010

4.5

1997

2.7

2004

4.8

2011

4.8

1998

3.3

2005

5.1

2012

5.1

1999

4.6

2006

5.3

2013

5.5

2000

3.2

2007

5.2

2014

5.7

2001

3.9

2008

4.6

2002

4

2009

4.5


(a) Develop a three-year moving average.

(b) Develop a four-year moving average.

(c) Develop a five-year moving average.

(d) Develop a seven-year rmoving average.

In: Statistics and Probability

Sales for the Forever Young Cosmetics Company (in $ millions) are as follows:

Sales for the Forever Young Cosmetics Company (in $ millions) are as follows:

 

Year

Sales ($ millions)

Year

Sales ($ Millions)

Year

Sales ($ Milions

1996

2.4

2003

4.4

2010

4.5

1997

2.7

2004

4.8

2011

4.8

1998

3.3

2005

5.1

2012

5.1

1999

4.6

2006

5.3

2013

5.5

2000

3.2

2007

5.2

2014

5.7

2001

3.9

2008

4.6

    

2002

4

2009

4.5

    


(a) Develop a three-year moving average.

(b) Develop a four-year moving average.

(c) Develop a five-year moving average.

(d) Develop a seven-year rmoving average.

In: Statistics and Probability

The following table provides the Dow Jones Industrial Average (DJIA) opening index value on the first...

The following table provides the Dow Jones Industrial Average (DJIA) opening index value on the first working day of 1991–2010:

YEAR DJIA YEAR 2 DJIA

2010 10,431 2000 11,502

2009 8,772 1999 9,213

2008 13,262 1998 7,908

2007 12,460 1997 6,448

2006 10,718 1996 5,117

2005 10,784 1995 3,834

2004 10,453 1994 3,754

2003 8,342 1993 3,301

2002 10,022 1992 3,169

2001 10,791 1991 2,634

• Develop a trend line and use it to predict the opening DJIA index value for years 2011, 2012, and 2013. Find the MSE for this model.

In: Statistics and Probability

Please answer as soon as possible You’ve learned in this course that the IRS views large...

Please answer as soon as possible

You’ve learned in this course that the IRS views large charitable contribution deductions as prima facie suspicious. So when a tax return client just hands you a conclusory list of cash (or especially noncash) contribution totals for the year, and those totals seem high relative to the client’s income level, how should you react…???

Actually, let’s consider that question in the context of a more specific scenario. Remember Paul and Anita Tucker, the taxpayers who claimed that they had giv-en almost $20,000 to their church? Although we weren’t told their income level, we do recognize that this wasn’t a negligible amount of money.*** Consult SSTS No. 3 and discuss.

*** The tax year involved in Tucker was 2002. Their contribution, stated in 2020 dollars, would be almost $29,000.

In: Economics

The following selected transactions relate to liabilities of United Insulation Corporation. United’s fiscal year ends on...

The following selected transactions relate to liabilities of United Insulation Corporation. United’s fiscal year ends on December 31.

2018

Jan. 13 Negotiated a revolving credit agreement with Parish Bank that can be renewed annually upon bank approval. The amount available under the line of credit is $20.0 million at the bank’s prime rate.
Feb. 1 Arranged a three-month bank loan of $3.2 million with Parish Bank under the line of credit agreement. Interest at the prime rate of 10% was payable at maturity.
May 1 Paid the 10% note at maturity.
Dec. 1 Supported by the credit line, issued $13.6 million of commercial paper on a nine-month note. Interest was discounted at issuance at a 9% discount rate.
31 Recorded any necessary adjusting entry(s).

2019

Sept. 1 Paid the commercial paper at maturity.


Required:
Prepare the appropriate journal entries through the maturity of each liability 2018 and 2019. (If no entry is required for a transaction/event, select "No journal entry required" in the first account field. Do not round intermediate calculations. Enter your answers in whole dollars.)

1. Record a revolving credit agreement negotiated with Parish Bank that can be renewed annually upon bank approval. The amount available under the line of credit is $20.0 million at the bank’s prime rate.

2. Record a three-month bank loan of $3.2 million with Parish Bank under the line of credit agreement. Interest at the prime rate of 10% was payable at maturity.

3. Record the payment of the 10% note at maturity.

4. Record the issuance of $13.6 million of commercial paper on a nine-month note, supported by the credit line. Interest was discounted at issuance at a 9% discount rate.

5. Record necessary adjusting entry to accrue interest on December 31.

6. Record interest on commercial paper in 2019.

7. Record the repayment of commercial paper at maturity.

In: Accounting

Practical Exercise: Recipe of Success! Few British knew about Dr Oetker a German and European leading...

Practical Exercise: Recipe of Success!
Few British knew about Dr Oetker a German and European leading food manufacturer who entered the UK market with a frozen pizza named "Pizza Ristorante". So, who is Dr Oetker?
Dr Oetker was a pharmacist from Biefeild in Germany who established the Oetker Group in 1891. Nowadays, the company is one of the largest family businesses in Germany with revenue of 3.5 billion euro per annum. The key ingredient of the company's success is quality; whether in management or product.
Quality of the best recipe:
Oetker management rose the curtain of Pizza Ristorante in Britain, in 2020 and astonishingly was the first investment for a huge and well-reputed food and beverage company from Germany. The promise has been to offer an authentic pizza taste even if it is frozen. In no months, Pizza Ristorante became a popular product across the UK as research pointed out that 76% of consumers give Pizza Ristorante preferences over its competitors' pizzas. Till recent years, the brand made a tremendous journey of success and well established in the UK market.
Recipe of Success:
Dr Oetker is an experienced company when it comes to introducing products in new markets, and food and beverage market, including its frozen pizza brand, a leader in the 23 European countries. Similarly, Ristorante frozen pizza enjoys success in the UK market. Due to its philosophy in quality, Pizza Ristorante is made from high-quality ingredients to satisfy customers who are interested in buying frozen pizzas. Moreover, before entering a market, the company thoroughly study the specific market needs and the nature of its competitors. Therefore, the company through its marketing research found that the dominate taste of the pizza was a thin and crispy segment, accordingly, the company decided to possibly add value by offering high quality and with competitive price of frozen pizza. The goal was to encourage consumers to revisit the frozen pizza category by tasting samples of an authentic pizzeria pizza of Ristorante Pizza.
Onwards and upwards.
As a result of Ristorante brand success, Dr Oetker launched several new products in the UK market; yoghurt, dessert brands 'Onken' is now established and doing well. Dr Oetker's new venture was acquiring SuperCook range of baking and cake decorating products. For now, both companies are in the phase of merging and re-introducing SuperCook with new packaging and promotional material.
Once again, Dr Oetker is a well-established company in the area of baking products back in Germany and EU with a long history of providing baking products. After major success in the frozen pizza segment in the UK, Dr Oekter may go ahead investing more resources in its newly developed product (SuperCook) and again Dr Oetker is aiming to become number one in baking product segment too by using its recipe of success, the one used when launching Ristorante Pizza. However, British baking products are popular for their traditional taste, thus, many UK bakers do not like to have a new thing in their baking process. Moreover, they are suspicious of the innovation in the baking material though this brand has been there in the UK for a long time.
Another task to be taken into consideration is to persuade the UK retailer and super grocery shops to spare shelves for SuperCook (after re-launching product this task may not be easy as it is seen). In other words, SuperCook needs the super grocery continuous support and to allocate shelf space as they used to do before Dr Oetker acquires the business.
The crucial part of the success of re-launch is recruiting a well-trained new salesforce.
Source: Adapted from articles originally in The Grocer, 18 May 2002, p. 30; 13 July 2002, p. 48; and the website: http://www.talkingretail.com/products, 22 February 2008.
2. The sales manager also understands the importance of giving the right sales incentives to the salesforce to have a smooth relaunch of SuperCook in the market. Therefore, the sales manager understands the pivotal role of sales budget in encouraging and controlling the salesforce. In the light of these facts, suggest best approaches to set up sales quotas or sales targets for the salespeople for the relaunched brand and how these are used as meterstick when measuring achievement.

In: Economics

The accompanying data table show the percentage of tax returns filed electronically in a city from...

The accompanying data table show the percentage of tax returns filed electronically in a city from 2000 to 2009. Complete parts a through e below.

Year   Percentage
2000   25
2001   33
2002   37
2003   38
2004   48
2005   50
2006   55
2007   59
2008   62
2009   64

a) Forecast the percentage of tax returns that will be electronically filed for 2010 using exponential smoothing with alpha= 0.1.

​b) Calculate the MAD for the forecast in part a.

c) Forecast the percentage of tax returns that will be electronically filed for 2010 using exponential smoothing with trend adjustment. Set alpha= 0.3 and beta= 0.4.

​d) Calculate the MAD for the forecast in part c.

In: Statistics and Probability