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The previous example had one column in the GROUP BY ROLLUP(), but just as the GROUP BY can organize by multiple columns, so can the GROUP BY ROLLUP(). The next example builds a more detailed summary of the data, with subtotals for each grouping of category and region:

described as Da. The most interesting aspect of the McGurk effect is that it never goes away. If you watch the video with your eyes closed, it is clear that the sound is Ba ; opening your eyes produces Da. Even after watching the tape for hundreds of times, and even knowing the true sound, the effect exists. We cannot use our rational brains to override the nonconscious preprocessing of the information. The McGurk effect demonstrates that preconscious processing affects our fundamental perception of the world. Obviously, the prefrontal cortex cannot be in complete control if it has information that is altered and shaped by the other parts of the brain. What have we learned so far If we divide the brain into the prefrontal cortex and the lizard brain, we have seen that our prefrontal cortexes are often far from perfect. Next, we have seen that the lizard brain has a powerful influence on our behavior. We are built to make errors and fall into a variety of traps. Finally, we are built to create a cohesive story about our behavior, which makes it hard to understand the sources of our own actions.

< xml version="1.0" standalone="yes" > <!DOCTYPE locations [ <!ELEMENT locations (place)*> <!ELEMENT place (name | directions)*> <!ELEMENT name (#PCDATA)> <!ELEMENT directions (#PCDATA)> ]> <locations> <place> <name>Fishing hole</name> <directions>Turn left at bridge</directions> </place> </locations>

SELECT i.InventoryCode, it.Value FROM dbo.InventoryTransaction AS it INNER JOIN dbo.Inventory AS i ON i.InventoryID = it.InventoryID;

LISTING 29-1

A traditional use of data mining is to train a data mining model using data for which an outcome is already known and then use that model to predict the outcome of new data as it becomes available This use of data mining requires several steps, only some of which happen within Analysis Services: Business and data understanding: Understand the questions that are important and the data available to answer those questions Insights gained must be relevant to business goals to be of use Data must be of acceptable quality and relevance to obtain reliable answers Prepare data: The effort to get data ready for mining can range from simple to painstaking depending on the situation Some of the tasks to consider include the following: Eliminate rows of low data quality.

Here, the measure of quality is domain speci c, but it may include too small an underlying sample size, values outside of expected norms, or failing any test that proves the row describes an impossible or highly improbable case General cleaning by scaling, formatting, and so on; and by eliminating duplicates, invalid values, or inconsistent values Analysis Services accepts a single primary case table, and optionally one or more child nested tables If the source data is spread among several tables, then denormalization by creating views or preprocessing will be required Erratic time series data may bene t from smoothing Smoothing algorithms remove the dramatic variations from noisy data at the cost of accuracy, so experimentation may be necessary to choose an algorithm that does not adversely impact the data mining outcome Derived attributes can be useful in the modeling process, typically either calculating a value from other attributes (e.

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Figure 10-7: JavaBank applet transfer screen. The control names include: transferAccountNo a text field for the recipient s account number transferAmount a text field for the amount of money to transfer transferYesButton a button to commit the transfer transferEndButton a button to exit the session statusBar a noneditable text field to display various messages