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rdlc data matrix

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Maria Wallin, marketing manager at the Loyalty Group, arranged for a direct-mail piece to be sent to 50,000 western Canadian farmers who had been active in the program in the 14 months prior to the campaign In addition, mass media ads for the campaign were placed in six farm publications in western Canada The goal of the campaign was to achieve a direct-mail response rate of 5 percent and to encourage 2 percent of those contacted to make purchases at one more farm merchant than they had used the previous spring Air Miles for Business turned to OgilvyOne Worldwide in Toronto for strategic and creative development of the mailing piece, and to Symcor Direct Response for the laser and letter shop production Using the Air Miles member database, they developed 72 different combinations of letter copy and 2952 certi cate sheet combinations, completely driven by past purchase behavior.

Various economic time series that cover every aspect of the economy, from inflation to housing starts, may improve the odds of trading commodities successfully Do not forget to examine reports and measures that reflect sentiment, such as the Commitment of Traders (COT) releases, bullish and bearish consensus surveys, and put-call ratios Nonquantitative forms of sentiment data, such as news headlines, may also be acquired and quantified for use in systematic tests Nothing should be ignored Mining unusual data often uncovers interesting and profitable discoveries It is often the case that the more esoteric or arcane the data, and the more difficult it is to obtain, the greater its value!.

The offer of bonus reward miles was designed to encourage increased spending during the promotional period There was an additional incentive for each farmer to visit one merchant whom he or she had not visited the previous spring A control group of 10,000 western Canadian farmers who were members of the program but did not receive any mailings was set aside The success of the campaign was measured by the overall response rate, the number of farm merchants used, and the number of reward miles earned by those who received the mailing versus those in the control group who did not The result was that 12 percent of the mail universe responded to at least one of the offers, more than double the rst objective.

11-4

Portfolio Performance with Best In-Sample Parameters for in the Optimization and Verification Samples (Continued)

In addition, the number of reward miles earned by those who received the mailing was 59 percent higher than the number earned by the control group during the period of the promotion The same test.

ues: PI = the threshold, P2 = the number of the neural network within the group of networks trained for the model (these numbers correspond to the numbers used in the file names for the networks shown in Tables 1 l-l through 1 l-3), P3 = not used. In all cases, the threshold parameters (column Pl) shown are those that resulted in the best in-sample performance. Identical parameters are used for verification on the out-of-sample data. The threshold for the time-reversed Slow %K model was optimized for each order type by stepping it from 50 to 90 in increments of 1. For the top and bottom predictor models, the thresholds were stepped from 20 to 80 in increments of 2. In each case, optimization was carried out only using the in-sample data. The best parameters were then used to test the model on both the in-sample and out-of-sample data sets. This follows the usual practice established in this book. Trading Results for the Reverse Slow %K Model The two networks that were selected as most likely to hold up out-of-sample, based on their shrinkage-adjusted multiple correlations with the target, were analyzed for

epithelial tissue such as skin; mucous membranes; and linings of the respiratory, the gastrointestinal, and urogenital systems. It is important for good vision, immunity, and healthy skin and hair as well as cancer prevention. Vitamin A is necessary for proper reproduction, bone growth, and tooth development. Deficiencies have been associated with a higher incidence of certain cancers. Vitamin A works synergistically with several carotenoids; other vitamins such as vitamins C, D, and E; minerals; and other antioxidants to lower cancer rates and to increase longevity. Vitamin A can be obtained as preformed or pro-vitamin A. Preformed vitamins are found in certain meats and animal products, and although beneficial up to a point, they can have toxic side effects when taken in large amounts over a long period of time, especially in adults or during pregnancy. Pro-vitamin A is formed in our bodies from the conversion of certain carotenoids, known as precursor compounds. These carotenoids are found in many fruits and vegetables and have little or no toxicity. Beta-carotene is the major carotenoid converted into active retinol-type vitamin A for use in the body. Other carotenoids such as alpha-carotene, gamma-carotene, beta-zeacarotene, crytoxanthin, and beta-apo-carotenals can be converted to vitamin A to various degrees. But not all carotenoids can be converted into vitamin A. Examples of carotenoids that are not converted to vitamin A include lycopene, lutein, canthaxanthin, zeaxanthin, and capsanthin. Beta-carotene is one of the major precursors to vitamin A, and there are many scientific studies detailing its beneficial effects. Beta-carotene, which has been shown to prevent certain types of cancers, has powerful antioxidant properties and works best in combination with vitamins C and E, minerals such as selenium, and certain phenolic antioxidants to prevent cancer and to increase longevity.