. Prepare a crosstabulation of the data on Industry (rows) and Brand Revenue ($ billions). Use class intervals of 25 starting at 0 for Brand Revenue ($billions) b. Prepare a frequency distribution for the data on Brand revenue ($ billions). c. What conclusions can you draw about the type of industry and the brand revenue? d. Prepare a crosstabulation of the data on Industry (rows) and the 1-year Value change (%). Use class intervals of 20 starting at -60 for 1 year Value Change (%). e. Prepare a frequency distribution for the data on 1 -
. Prepare a crosstabulation of the data on Industry (rows) and Brand Revenue ($ billions). Use class intervals of 25 starting at 0 for Brand Revenue ($billions) b. Prepare a frequency distribution for the data on Brand revenue ($ billions). c. What conclusions can you draw about the type of industry and the brand revenue? d. Prepare a crosstabulation of the data on Industry (rows) and the 1-year Value change (%). Use class intervals of 20 starting at -60 for 1 year Value Change (%). e. Prepare a frequency distribution for the data on 1 -
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
| a. Prepare a crosstabulation of the data on Industry (rows) and Brand Revenue |
| ($ billions). Use class intervals of 25 starting at 0 for Brand Revenue ($billions) |
| b. Prepare a frequency distribution for the data on Brand revenue ($ billions). |
| c. What conclusions can you draw about the type of industry and the brand revenue? |
| d. Prepare a crosstabulation of the data on Industry (rows) and the 1-year Value change |
| (%). Use class intervals of 20 starting at -60 for 1 year Value Change (%). |
| e. Prepare a frequency distribution for the data on 1 - year Value Change (%). |
| f. What conclusions can you draw about the type of industry and the 1-year change in |
| value? |
Show steps in Excel.
| Brand | Industry | Brand Value ($ billions) | 1-Yr Value Change (%) | Brand Revenue ($ billions) |
| Accenture | Other | 9.7 | 10 | 30.4 |
| Adidas | Other | 8.4 | 23 | 14.5 |
| Allianz | Financial Services | 6.9 | 5 | 130.8 |
| Amazon.Com | Technology | 14.7 | 44 | 60.6 |
| American Express | Financial Services | 18 | 5 | 33.8 |
| Audi | Automotive & Luxury | 10.9 | 1 | 55.2 |
| AXA | Financial Services | 5.6 | 10 | 149.3 |
| Bank of America | Financial Services | 7.7 | -9 | 83.3 |
| Boeing | Other | 6.4 | 12 | 81.7 |
| Burberry | Automotive & Luxury | 5.5 | 13 | 3 |
| Canon | Technology | 8.7 | -11 | 41.8 |
| Caterpillar | Other | 8.5 | 21 | 65.9 |
| Chanel | Automotive & Luxury | 7 | 13 | 4.4 |
| Chase | Financial Services | 6.3 | -9 | 56.8 |
| Coach | Automotive & Luxury | 10.5 | 1 | 5.1 |
| Colgate | Consumer Packaged Goods | 7.9 | 7 | 3.9 |
| Credit Suisse | Financial Services | 5.7 | -9 | 42 |
| Danone | Consumer Packaged Goods | 11.1 | 13 | 13.3 |
| Dell | Technology | 6.4 | -23 | 56.9 |
| Ebay | Technology | 8.5 | 14 | 6.3 |
| Ericsson | Technology | 5.9 | -35 | 34.6 |
| ESPN | Other | 12.9 | 12 | 10.4 |
| Estee Lauder | Consumer Packaged Goods | 5.4 | 15 | 4.4 |
| Technology | 13.7 | 4 | 5.1 | |
| FedEx | Other | 5.8 | 10 | 44.3 |
| Ford | Automotive & Luxury | 10.6 | 34 | 130 |
| Fox | Other | 10.2 | 20 | 11.7 |
| Frito-Lay | Consumer Packaged Goods | 11.6 | 6 | 11.1 |
| General Electric | Other | 34.2 | 2 | 132.1 |
| Gillette | Consumer Packaged Goods | 18.3 | 9 | 8 |
| Goldman Sachs | Financial Services | 6.8 | -15 | 41.7 |
| Technology | 47.3 | 26 | 43.5 | |
| Gucci | Automotive & Luxury | 12.1 | 1 | 4.7 |
| H&M | Other | 16.1 | 12 | 18.3 |
| Heineken | Other | 7 | 7 | 6 |
| Heinz | Consumer Packaged Goods | 5.6 | 2 | 4.4 |
| Hermès | Automotive & Luxury | 9.3 | 20 | 4.5 |
| Hewlett-Packard | Technology | 15.3 | -16 | 119.9 |
| Home Depot | Other | 10.1 | 12 | 74.8 |
| HSBC | Financial Services | 15.2 | 0 | 104.9 |
| Hyundai | Automotive & Luxury | 6.5 | 21 | 49.7 |
| IBM | Technology | 50.7 | 5 | 104.5 |
| IKEA | Other | 11.5 | 5 | 35.5 |
| ING | Financial Services | 5.7 | 8 | 118.7 |
| Intel | Technology | 30.9 | -4 | 53.3 |
| J.P. Morgan | Financial Services | 9.1 | -30 | 43.1 |
| John Deere | Other | 6.4 | 14 | 36.2 |
| Kellogg's | Consumer Packaged Goods | 8.7 | -6 | 7.1 |
| Kraft | Consumer Packaged Goods | 9.1 | -10 | 7.1 |
| Lancome | Consumer Packaged Goods | 6.2 | 13 | 4 |
| Lexus | Automotive & Luxury | 7 | 1 | 18.7 |
| L'Oréal | Consumer Packaged Goods | 16.9 | 16 | 10.6 |
| Louis Vuitton | Automotive & Luxury | 28.4 | 16 | 9.4 |
| Marlboro | Other | 16.6 | 9 | 23.6 |
| Mastercard | Financial Services | 7.4 | 8 | 7.4 |
| McDonald's | Other | 39.4 | 5 | 88.3 |
| Microsoft | Technology | 56.7 | 4 | 77.8 |
| MTV | Other | 5.6 | -7 | 3.1 |
| Nestle | Consumer Packaged Goods | 11.8 | 18 | 11.3 |
| NIKE | Other | 18.2 | 14 | 23.7 |
| Nintendo | Technology | 8 | -39 | 7.6 |
| Nissan | Automotive & Luxury | 6.9 | 17 | 108.5 |
| Nokia | Technology | 7 | -55 | 38.8 |
| Pampers | Consumer Packaged Goods | 9.6 | 12 | 10.4 |
| Philips | Other | 7.1 | 14 | 31.9 |
| Porsche | Automotive & Luxury | 5.7 | 16 | 17.8 |
| Prada | Automotive & Luxury | 7.1 | 23 | 3.4 |
| Ralph Lauren | Other | 6.4 | 28 | 6.4 |
| Rolex | Automotive & Luxury | 7.4 | 14 | 4.5 |
| Santander | Financial Services | 6 | -2 | 57.3 |
| SAP | Technology | 19.6 | 28 | 20.3 |
| Siemens | Other | 13.7 | 1 | 100.6 |
| Sony | Technology | 6.6 | -20 | 69.5 |
| Starbucks | Other | 6.9 | 9 | 13 |
| Subway | Other | 5.7 | 17 | 18.1 |
| Thomson Reuters | Other | 9.6 | -10 | 7.2 |
| UPS | Other | 10.2 | 13 | 54.1 |
| Verizon | Other | 19.3 | 0 | 115.8 |
| Visa | Financial Services | 14.2 | 12 | 10.4 |
| Volkswagen | Automotive & Luxury | 8.1 | 16 | 145.7 |
| Wells Fargo | Financial Services | 9 | -14 | 91.2 |
| Zara | Other | 9.4 | 11 | 13.5 |
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