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Frank was pleasantly surprised when he ran his test ad in the Wall Street Journal: Our cost per order was the lowest of any outside list we ve ever used, and I realized that a single advertisement held the key to the rapid growth of our company. But Frank Schultz didn t stop there. He continued to advertise in the Wall Street Journal, the New York Times, Parade, and TV Guide, to name only a few. When you re a farmer you always worry about the crop. It s growing too slow you worry. It s growing too fast you really worry, said Frank later in a letter to me. I nd it to be about the same when a farmer gets into space advertising. The orders are coming in so good from our space ads, I m beginning to worry. A high-class worry, I ll admit. In December 1980, an article on Schultz appeared in Texas Monthly magazine. The story talked about his wonderful copy and how the copy made his business seem small and personal. Yet it also explained that the business was quite large by then. He harvested 26,000 tons of grapefruit in 1979 with only 4 percent passing Schultz s rigorous standards for Royal Ruby Red grapefruit. The rest were sold to the grocery chains. And he now had 80,000 customers in the 48 contiguous states. He owned 14,000 acres of orchards spread out from Brownsville to McAllen, Texas, and had hundreds of employees. He truly was a success story all created from the power of his pen. And yet, his customers visualized Frank with his picking ring out in the orchard picking those perfect Royal Ruby Red grapefruit free of sheep nose and wind scarring.

blocker, protection against so-called drive-by downloads, a new Manage Addons applet, and other security-oriented features. Manage Add-ons was significantly enhanced in IE7 and Windows Vista, and of course IE8 adds even more security controls. We look at Internet Explorer in detail in 20.

The result is the same as the crosstab results in previous examples, but if there were additional regions, they would appear as new columns in the crosstab query.

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This algorithm operates by finding attributes that appear together in cases with sufficient frequency to be significant. These attribute groupings are called itemsets, which are in turn used to build the rules used to generate predictions. While Association Rules can be used for many tasks, it is specially suited to market basket analysis. Generally, data will be prepared for market basket analysis using a nested table, whereby the parent row is transaction (e.g., Order) and the nested table contains the individual items. Three viewers provide insight into a trained model: Itemsets: Displays the list of itemsets discovered in the training data, each with its associated size (number of items in the set) and support (number of training cases in which this set appears). Several controls for filtering the list are provided, including the Filter Itemset text box, which will search for any string entered (e.g., Region = Europe will display only itemsets that include that string).

%favorites = ( fruit, banana, vegetable, carrot );

W,, (t, = 2 u)

(10.41)

n Length increments: Whenever you click one of the spin arrows on a dimension or number setting, it increments by this value. The exceptions are features such as the tolerance for Fit Splines, where the increment values are determined by the scale of the geometry.

As you can see, you re still trying to get the objects to do the hard work; the code in your main program file should be kept to a minimum. So, let s head back to Invoice.rb to implement that method call:

pi(t) = p(t,si),

physical view can be seen in the context of the circuit schematic view shown in Figure 8.31. Comparison of these figures with Figures 8.20 and 8.21 for the SVG cell shows that the schematic diagrams and the views along the channel are the same for the two arrays if the erase gate is ignored for the TPVG approach. Indeed, these two approaches operate in the same manner in the read and program modes. The difference is in the erase mode in which the erase gate of the TPVG approach is biased to a high voltage with all other nodes grounded to cause electrons to tunnel from the edge of the floating gate to the erase gate in the same way as the FETIF approach. Because the TPVG approach is intended for mass storage applications, there is no attempt to provide small block erase as the FETIF approach does. Although it is not important from a device physics point of view, the TPVG approach has adopted some circuit design features to further optimize it for mass storage applications. These are described in the references.

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