An analog signal is band limited to B Hz sampled at Nyquist rate and samples are quantized into 4 levels each with probability . The information rate is ?->(Show Answer!)

1. An analog signal is band limited to B Hz sampled at Nyquist rate and samples are quantized into 4 levels each with probability . The information rate is

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MCQ-> Questions are based on a set of conditions. In answering some of the questions, it may be useful to draw a rough diagram. Choose the response that most accurately and completely answers each question. Seven bands were scheduled to perform during the week long music festival at XLRI. The festival began on a Monday evening and ended on the Sunday evening. Each day only one band performed. Each band performed only once. The organizing committee had the task of scheduling the performances of the seven bands - Cactus, Axis, Enigma, Boom, Fish, Dhoom and Bodhi Tree. The festival schedule followed the following conditions: the performance of Bodhi. Tree, the home band of XLRI, did not precede the performance of any other band. Among the visiting bands three were rock bands and the other three were fusion bands. All three bands of the same genre were not allowed to perform consecutively. Boom, which was a rock band, refused to perform immediately before or after Fish. Meet, who was a lead vocalist with a rock band, refused to perform after Angelina. Angelina, the only female lead vocalist in the music fest besides Bony, was with the band Enigma. Angelina refused to perform after Thursday citing personal reasons. Ali, who was the lead vocalist of a rock band, was not with the band Dhoom, and did not perform on Saturday. Sid, the lead vocalist of the rock band Cactus, could perform only on Monday. Rupam, the only male among the lead vocalists of the fusion bands, was with Fish and performed on Wednesday. None of the bands performed in absence of their lead vocalist.All of the following statements can be true except:
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MCQ-> People are continually enticed by such "hot" performance, even if it lasts for brief periods. Because of this susceptibility, brokers or analysts who have had one or two stocks move up sharply, or technicians who call one turn correctly, are believed to have established a credible record and can readily find market followings. Likewise, an advisory service that is right for a brief time can beat its drums loudly. Elaine Garzarelli gained near immortality when she purportedly "called" the 1987 crash. Although, as the market strategist for Shearson Lehman, her forecast was never published in a research report, nor indeed communicated to its clients, she still received widespread recognition and publicity for this call, which was made in a short TV interview on CNBC. Still, her remark on CNBC that the Dow could drop sharply from its then 5300 level rocked an already nervous market on July 23, 1996. What had been a 40-point gain for the Dow turned into a 40-point loss, a good deal of which was attributed to her comments.The truth is, market-letter writers have been wrong in their judgments far more often than they would like to remember. However, advisors understand that the public considers short-term results meaningful when they are, more often than not, simply chance. Those in the public eye usually gain large numbers of new subscribers for being right by random luck. Which brings us to another important probability error that falls under the broad rubric of representativeness. Amos Tversky and Daniel Kahneman call this one the "law of small numbers.". The statistically valid "law of large numbers" states that large samples will usually be highly representative of the population from which they are drawn; for example, public opinion polls are fairly accurate because they draw on large and representative groups. The smaller the sample used, however (or the shorter the record), the more likely the findings are chance rather than meaningful. Yet the Tversky and Kahneman study showed that typical psychological or educational experimenters gamble their research theories on samples so small that the results have a very high probability of being chance. This is the same as gambling on the single good call of an advisor. The psychologists and educators are far too confident in the significance of results based on a few observations or a short period of time, even though they are trained in statistical techniques and are aware of the dangers.Note how readily people over generalize the meaning of a small number of supporting facts. Limited statistical evidence seems to satisfy our intuition no matter how inadequate the depiction of reality. Sometimes the evidence we accept runs to the absurd. A good example of the major overemphasis on small numbers is the almost blind faith investors place in governmental economic releases on employment, industrial production, the consumer price index, the money supply, the leading economic indicators, etc. These statistics frequently trigger major stock- and bond-market reactions, particularly if the news is bad. Flash statistics, more times than not, are near worthless. Initial economic and Fed figures are revised significantly for weeks or months after their release, as new and "better" information flows in. Thus, an increase in the money supply can turn into a decrease, or a large drop in the leading indicators can change to a moderate increase. These revisions occur with such regularity you would think that investors, particularly pros, would treat them with the skepticism they deserve. Alas, the real world refuses to follow the textbooks. Experience notwithstanding, investors treat as gospel all authoritative-sounding releases that they think pinpoint the development of important trends. An example of how instant news threw investors into a tailspin occurred in July of 1996. Preliminary statistics indicated the economy was beginning to gain steam. The flash figures showed that GDP (gross domestic product) would rise at a 3% rate in the next several quarters, a rate higher than expected. Many people, convinced by these statistics that rising interest rates were imminent, bailed out of the stock market that month. To the end of that year, the GDP growth figures had been revised down significantly (unofficially, a minimum of a dozen times, and officially at least twice). The market rocketed ahead to new highs to August l997, but a lot of investors had retreated to the sidelines on the preliminary bad news. The advice of a world champion chess player when asked how to avoid making a bad move. His answer: "Sit on your hands”. But professional investors don't sit on their hands; they dance on tiptoe, ready to flit after the least particle of information as if it were a strongly documented trend. The law of small numbers, in such cases, results in decisions sometimes bordering on the inane. Tversky and Kahneman‘s findings, which have been repeatedly confirmed, are particularly important to our understanding of some stock market errors and lead to another rule that investors should follow.Which statement does not reflect the true essence of the passage? I. Tversky and Kahneman understood that small representative groups bias the research theories to generalize results that can be categorized as meaningful result and people simplify the real impact of passable portray of reality by small number of supporting facts. II. Governmental economic releases on macroeconomic indicators fetch blind faith from investors who appropriately discount these announcements which are ideally reflected in the stock and bond market prices. III. Investors take into consideration myopic gain and make it meaningful investment choice and fail to see it as a chance of occurrence. IV. lrrational overreaction to key regulators expressions is same as intuitive statistician stumbling disastrously when unable to sustain spectacular performance.....

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MCQ-> Analyze the following passage and provide appreciate answers for the questions that follow. Ideas involving the theory probability play a decisive part in modern physics. Yet we will still lack a satisfactory, consistence definition of probability; or, what amounts to much the same, we still lack a satisfactory axiomatic system for the calculus of probability. The relations between probability and experience are also still in need of clarification. In investigating this problem we shall discover what will at first seem an almost insuperable objection to my methodological views. For although probability statements play such a vitally important role in empirical science, they turn out to be in principle impervious to strict falsification. Yet this very stumbling block will become a touchstone upon which to test my theory, in order to find out what it is worth. Thus, we are confronted with two tasks. The first is to provide new foundations for the calculus of probability. This I shall try to do by developing the theory of probability as a frequency theory, along the lines followed by Richard von Mises, But without the use of what he calls the ‘axiom of convergence’ (or ‘limit axiom’) and with a somewhat weakened ‘axiom of randomness’ The second task is to elucidate the relations between probability and experience. This means solving what I call the problem of decidability statements. My hope is that the investigations will help to relieve the present unsatisfactory situation in which physicists make much use of probabilities without being able to say, consistently, what they mean by ‘probability’.The statement, “The relations between probability and experience are still in need of clarification” implies that:
....