In order to test the best fit of assumed distribution of scores with the observed data, the appropriate statistical method is ?->(Show Answer!)

1. In order to test the best fit of assumed distribution of scores with the observed data, the appropriate statistical method is

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MCQ->In order to test the best fit of assumed distribution of scores with the observed data, the appropriate statistical method is....

MCQ-> Study the following information carefully and answer the questions given below :When a word and number arrangement machine is given an input line of words and numbers, it arranges them following a particular rule. The following is an illustration of input and rearrangement. (All the numbers are two digit numbers) Input : 24 method 87 67 of data 34 collection 45 12 specified now Step I : 12 method 87 67 of data 34 collection 45 specified now 24 Step II : 34 12 method 87 67 of data collection specified now 24 45 Step III: 67 34 12 method of data collection specified now 24 45 87 Step IV : collection 67 34 12 method of specified now 24 45 87 data Step V : method collection 67 34 12 of specified 24 45 87 data now Step VI: of method collection 67 34 12 24 45 87 data now specified Step VII is the last step of the above arrangement as the intended arrangement is obtained. As per the rules followed in the given steps, find out the appropriate steps for the given input. Input : chemical 68 11 reaction 87 is 21 hard to 53 92 detectIn which step are the elements to 92 detect 21′ found in the same order?
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MCQ-> Read the passage carefully and answer the given questionsThe complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. . . . The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. . . .Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases - those that the current forest gets wrong. This ensures even more diversity and accurate forests.Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to breakthroughs.Which of the following conditions, if true, would invalidate the passage’s main argument?
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MCQ-> Read passage carefully. Answer the questions by selecting the most appropriate option (with reference to the passage). PASSAGE 4While majoring in computer science isn't a requirement to participate in the Second Machine Age, what skills do liberal arts graduates specifically possess to contribute to this brave new world? Another major oversight in the debate has been the failure to appreciate that a good liberal arts education teaches many skills that are not only valuable to the general world of business, but are in fact vital to innovating the next wave of breakthrough tech-driven products and services. Many defenses of the value of a liberal arts education have been launched, of course, with the emphasis being on the acquisition of fundamental thinking and communication skills, such as critical thinking, logical argumentation, and good communication skills. One aspect of liberal arts education that has been strangely neglected in the discussion is the fact that the humanities and social sciences are devoted to the study of human nature and the nature of our communities and larger societies. Students who pursue degrees in the liberal arts disciplines tend to be particularly motivated to investigate what makes us human: how we behave and why we behave as we do. They're driven to explore how our families and our public institutions-such as our schools and legal systems-operate, and could operate better, and how governments and economies work, or as is so often the case, are plagued by dysfunction. These students learn a great deal from their particular courses of study and apply that knowledge to today's issues, the leading problems to be tackled, and various approaches for analyzing and addressing those problems. The greatest opportunities for innovation in the emerging era are in applying evolving technological capabilities to finding better ways to solve human problems like social dysfunction and political corruption; finding ways to better educate children; helping people live healthier and happier lives by altering harmful behaviors; improving our working conditions; discovering better ways to tackle poverty; Improving healthcare and making it more affordable; making our governments more accountable, from the local level up to that of global affairs; and finding optimal ways to incorporate intelligent, nimble machines into our work lives so that we are empowered to do more of the work that we do best, and to let the machines do the rest. Workers with a solid liberal arts education have a strong foundation to build on in pursuing these goals. One of the most immediate needs in technology innovation is to invest products and services with more human qualities. with more sensitivity to human needs and desires. Companies and entrepreneurs that want to succeed today and in the future must learn to consider in all aspects of their product and service creation how they can make use of the new technologies to make them more humane. Still, many other liberal arts disciplines also have much to provide the world of technological innovation. The study of psychology, for example, can help people build products that are more attuned to our emotions and ways of thinking. Experience in Anthropology can additionally help companies understand cultural and individual behavioural factors that should be considered in developing products and in marketing them. As technology allows for more machine intelligence and our lives become increasingly populated by the Internet of things and as the gathering of data about our lives and analysis of it allows for more discoveries about our behaviour, consideration of how new products and services can be crafted for the optimal enhancement of our lives and the nature of our communities, workplaces and governments will be of vital importance. Those products and services developed with the keeneSt sense of how they' can serve our human needs and complement our human talents will have a distinct competitive advantage. Much of the criticism of the liberal arts is based on the false assumption that liberal arts students lack rigor in comparison to those participating in the STEM disciplines and that they are 'soft' and unscientific whereas those who study STEM fields learn the scientific method. In fact the liberal arts teach many methods of rigorous inquiry and analysis, such as close observation and interviewing in ways that hard science adherents don't always appreciate. Many fields have long incorporated the scientific method and other types of data driven scientific inquiry and problem solving. Sociologists have developed sophisticated mathematical models of societal networks. Historians gather voluminous data on centuries-old household expenses, marriage and divorce rates, and the world trade, and use data to conduct statistical analyses, identifying trends and contributing factors to the phenomena they are studying. Linguists have developed high-tech models of the evolution of language, and they've made crucial contributions to the development of one of the technologies behind the rapid advance of automation- natural language processing, whereby computers are able to communicate with the, accuracy and personality of Siri and Alexa. It's also important to debunk the fallacy that liberal arts students who don't study these quantitative analytical methods have no 'hard' or relevant skills. This gets us back to the arguments about the fundamental ways of thinking, inquiring, problem solving and communicating that a liberal arts education teaches.What is the central theme of the passage?
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