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After factoring the common term, (b or d), is factored out to a separate intermediate node. The results are shown here:
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Fast for signals (such as speech) that produce difference signals with large fluctuations Slow for signals (such as voiceband data and tones) that produce difference signals with small fluctuations
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<!ELEMENT ITEM (PRODUCT,NUMBER,PRICE)>
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<NUMBER>2</NUMBER>
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10. The correct answer is (c). This is exactly what is meant by a 95% confidence interval. The other answers all involve some sort of probability statement about the constructed interval. Once the interval is constructed, the true mean is either in the interval (probability = 1) or it isn t (probability = 0). Note the difference between the statements, The probability is 0.95 that this interval contains the true mean and The probability is 0.95 that an interval constructed in this manner will contain the true mean. 11. The correct answer is (c). It appears from the graph that most of the data will lie in the interval (5, 35), which means the range of values is about 30. In a normal curve, almost all of the data are within three standard deviations of the mean, or a range of six standard deviations total. Thus, 30/6 = 5 is the best estimate of the standard deviation.
Principal cells 1 Reabsorb sodium (stimulated by aldosterone) 2 Secrete potassium (stimulated by several signals, including aldosterone) 3 Reabsorb water (stimulated by antidiuretic hormone) Comment: Processes 1 and 2 are linked by a basolateral membrane Na-K-ATPase. Type A intercalated cells 1 Secrete hydrogen ions, which effect reabsorption of bicarbonate and excretion of titratable acid (stimulated by increased Pco2 and decreased extracellular pH) 2 Reabsorb potassium Comment: These 2 processes are linked by a luminal membrane H-K-ATPase. Type B intercalated cells 1 Reabsorb chloride ( stimulated by chloride depletion) 2 Secrete bicarbonate (stimulated by increased extracellular pH) Comment: These 2 processes are linked by a luminal membrane Cl-bicarbonate countertransporter.
With the client configuration utility, the network administrator can set parameters that control how and when the client adapter transmits and receives data (see Figure 5-5). Data Rate In this field, the rate at which the client adapter handles packets to or from APs (in infrastructure mode) or other clients (in ad hoc mode) is set. For infrastructure mode, automatic rate selection is recommended; the client uses the 11 Mbps data rate whenever possible, but adjusts to lower rates when necessary. Selecting a specific data rate is recommended for ad hoc mode. Selecting 1 Mbps offers the greatest range but the lowest throughput, whereas selecting 2 Mbps offers less range but greater throughput. The 5.5 Mbps option offers less range but still greater throughput, whereas selecting 11 Mbps offers the greatest throughput but
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14. Where fresh river water joins salty ocean water, it is known as (A) (B) (C) (D) (E) polluted pure silted brackish backwash
Every time we add structured content into an unstructured document, we open up the possibilities of data mining the unstructured repository for useful, hidden information. The easy part of this problem is analyzing the popularity of content or common keywords. More sophisticated data mining would be able to analyze the full-text repository for keywords and trends, or extracting the data in embedded tables, lists, or microformats. Data mining unstructured content is a very recent concept; besides keyword extraction, there are few tools available that help you with this. In the future, we anticipate that the demand for this functionality will lead to new innovations. Some believe that you need to convert all content into a semantically rich format such as XML before you can extract the structured content. This is true if you wish to extract data from embedded tables and charts, but less true if all you want are keywords and microformats. Others believe that the search engine s index itself is the place to target for the data mining of unstructured content. Since the search engine has already extracted keywords, and counted how often they are used, it s a natural place to do trend analysis, as well as semantic analysis to analyze what a document is about. Unfortunately, getting a computer to understand what a document is about is a very difficult problem to solve, which explains why there are so few new innovations in this area.
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