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In addition to reading charts, Raschke spends a lot of time studying sentiment indicators Aside from your technical toolbox, sentiment readings can be very useful in understanding if the market has upside or downside potential, she says You can look at indicators like Investors Intelligence and the Put/Call Ratio, she continues In general, when everyone is bearish, the bearish sentiment has extreme readings, which is how a market bottom is created When there are fairly high pessimistic sentiment readings, it means there is still a lot of cash that can be committed to the market Conversely, she says, the market will rally until there are overly bullish sentiments That usually means everybody has already bought To get more clues, she suggests going on the Internet and looking at the historical sentiment readings for the last five or ten years to get an idea of how investors reacted in the past The idea is to understand crowd behavior We cannot predict the behavior of an individual, she says, but we can predict the

file security 469 file structure tree 354 File Systems 306 file timestamps 469 FILEGROUP clause 446 filegroups 420, 423 424 backups 435 filegroup backup 435 filegroup configuration 435 free space 422 log files 360 FILENAME path 435 FileName property 363 Filepath 371 filesystem 345 fill factor 617 Fill methods 265 filler column 235 FillSchema 308 filtered index 24, 25 filtered queries 381 filtered unique indexes 12, 15, 26 filtering 93 filters 422, 660 661 firmware 456 first normal form 4, 243 first partition 416 five nines availability 456 fixed-size vhds 521 flags 469 Flat File 740 connection manager 737 739, 741 flat files 646, 715, 743 FLWOR expressions 127 128, 132 let clause 128 order by clause 128 sort direction 129 where clause 129 document order 129 Foot, Peter 305 FOR ATTACH 292 FOR XML clause 171, 173 FOR XML PATH clause 282 Foreach ADO Enumerator 740 Foreach Loop Container 737, 740 741 foreign key constraints 4, 12, 16, 24 26, 29 foreign key relationships 29 foreign key violation 486 Form_Load event handler 654 formatting 89, 92, 289

FORMSOF 182 183 INFLECTIONAL 183 THESAURUS 183, 186 four-part naming 661 four-part nomenclature 661 FQDN 499 500 See also fully qualified domain name fragment counts 386 fragment searches 252 fragmentation 245, 247, 334, 386, 521, 561, 584 level 334 335 fragmented index partition 422 fragmented indexes 334 fragments 237, 239 240, 242 243, 253 least common fragment 239 free pages 613 free space 407 FREETEXT 182, 184, 186, 200, 202 stemming 182 thesaurus 182 FREETEXTTABLE 183 184, 203 KEY column 184 FROM 86, 89, 90, 91, 92, 101, 184, 496, 517 FTC. See Full-Text Catalog FTP sites 743 full backup 356, 453 455, 459 full cache mode 748 full database backup 459 460 FULL JOIN 96, 99 FULL OUTER JOIN 87, 212 full partition 415 full population command 180 full recovery 238 FULL recovery model 449, 453, 455 full restore 459 460 full scan 335 full-duplex 262 Full-Text Catalog 177 full-text catalogs 176 creating 177 full-text commands 177 full-text engine 176, 183 word breaker 184 full-text indexes 176 177, 178, 179, 192, 199, 202, 234 ALTER command 179 creating 178 DROP 181 fragments 195

To work with all a worksheet s cells that contain data, use the used range property of the sheet object The used range is the smallest rectangle that includes all the cells that contain data For example, the following statement selects the used range on the active worksheet:

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keywords 196 maintaining 178, 180 naming 178 performance 179 populating 179 queries 182, 192, 199 querying 180, 181 rebuilding 180 removing 181 single-column key 176 stoplist 190 syncing with source table 180 unique key 176 updating 179 WITH CHANGE_ TRACKING 179 full-text indexing 176, 178, 189 columns 193 varbinary(max) 176 full-text performance 195 full-text search 401 patterns 184 search engine 176 177, 188 tilde 183 full-text searching 176 178 pattern tag 188 stoplist 193 text-based data types 176 unique index 193 FullTextCatalogProperty 194 fully logged operation 102 fully qualified domain name 499 500 functional dependencies 28 30 already-identified 33 business rules 33 combinations 40 data set 33 dependent attribute 31 finding 40 independent attributes 34 method to find 28, 30 missing dependency 40 model 28 multiple relations 40 multiple test 40 pitfalls in finding 32 repetitiveness 36 rules for finding 28 single-attribute dependencies 30 testing 34 36, 38 unknown dependency 34 Functional Dependency profile 712