Statistical tables

Combines a cookbook approach with the use of PCs and programmable calculators. Contains statistics suitable for the low number of samples, highpressure situations commonly found in established analytical methods with algorithms to eliminate statistical table handling, sample programs and data sets th
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In the Essentials of Behavioral Science series, our goal is to provide readers with books that will deliver key practical information in an efficient, accessible style. The series features books on a variety of topics, such as statistics, psychological testing, and research design and methodology, to name just a few. For the experienced professional, books in the series offer a concise yet thorough review of a specific area of expertise, including numerous tips for best practices.
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A: Basically, statistics is the “science of data.” There are three main tasks in statistics: (A) collection and organization, (B) analysis, and (C) interpretation of data. (A) Collection and organization of data: We will see several methods of organizing data: graphically (through the use of charts and graphs) and numerically (through the use of tables of data). The type of organization we do depends on the type of analysis we wish to perform.
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We estimate the parameters of a phrasebased statistical machine translation system from monolingual corpora instead of a bilingual parallel corpus. We extend existing research on bilingual lexicon induction to estimate both lexical and phrasal translation probabilities for MTscale phrasetables. We propose a novel algorithm to estimate reordering probabilities from monolingual data. We report translation results for an endtoend translation system using these monolingual features alone.
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When you finish this chapter, you should be able to: Discuss the general idea of analysis of variance, discuss the general idea of analysis of variance, conduct a test of hypothesis to determine whether the variances of two populations are equal, organize data into a oneway and a twoway ANOVA table.
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In this chapter, you learned to: Define the terms state of nature, event, decision alternatives, payoff, and utility; organize information in a payoff table or a decision tree; compute opportunity loss and utility function; find an optimal decision alternative based on a given decision criterion; assess the expected value of additional information.
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Some Statistical Machine Translation systems never see the light because the owner of the appropriate training data cannot release them, and the potential user of the system cannot disclose what should be translated. We propose a simple and practical encryptionbased method addressing this barrier.
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This paper proposes to use monolingual collocations to improve Statistical Machine Translation (SMT). We make use of the collocation probabilities, which are estimated from monolingual corpora, in two aspects, namely improving word alignment for various kinds of SMT systems and improving phrase table for phrasebased SMT. The experimental results show that our method improves the performance of both word alignment and translation quality significantly.
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We present four techniques for online handling of OutofVocabulary words in Phrasebased Statistical Machine Translation. The techniques use spelling expansion, morphological expansion, dictionary term expansion and proper name transliteration to reuse or extend a phrase table. We compare the performance of these techniques and combine them. Our results show a consistent improvement over a stateoftheart baseline in terms of BLEU and a manual error analysis.
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This paper presents an effective approach to discard most entries of the rule table for statistical machine translation. The rule table is ﬁltered by monolingual key phrases, which are extracted from source text using a technique based on term extraction. Experiments show that 78% of the rule table is reduced without worsening translation performance. In most cases, our approach results in measurable improvements in BLEU score. that a source phrase is either a ﬂat phrase consists of words, or a hierarchical phrase consists of both words and variables. ...
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We present a searchbased approach to automatic surface realization given a corpus of domain sentences. Using heuristic search based on a statistical language model and a structure we introduce called an inheritance table we overgenerate a set of complete syntacticsemantic trees that are consistent with the given semantic structure and have high likelihood relative to the language model. These trees are then lexicalized, linearized, scored, and ranked. This model is being developed to generate realtime navigation instructions. ...
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Lectures "Applied statistics for business  Chapter 2: Tabular and graphical presentations" provides students with the knowledge: Summarising data for a categorical variable, summarising data for a quantitative variable, summarising data for two variables using tables,... Invite you to refer to the disclosures.
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Chapter 6  Continuous random variables. After mastering the material in this chapter, you will be able to: Define a continuous probability distribution and explain how it is used, use the uniform distribution to compute probabilities, describe the properties of the normal distribution and use a cumulative normal table, use the normal distribution to compute probabilities,...
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Chapter 8  Confidence intervals. After mastering the material in this chapter, you will be able to: Calculate and interpret a zbased confidence interval for a population mean when σ is known, describe the properties of the t distribution and use a t table, calculate and interpret a tbased confidence interval for a population mean when σ is unknown,...
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Chapter 2  Describing data: Frequency tables, frequency distributions, and graphic presentation. When you have completed this chapter, you will be able to: Organize qualitative data into a frequency table, present a frequency table as a bar chart or a pie chart, organize quantitative data into a frequency distribution, present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
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Chapter 4  Describing data: Displaying and exploring data. When you have completed this chapter, you will be able to: Develop and interpret a dot plot; develop and interpret a stemandleaf display; compute and understand quartiles, deciles, and percentiles; construct and interpret box plots; compute and understand the coefficient of skewness; draw and interpret a scatter diagram; construct and interpret a contingency table.
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Chapter 12  Analysis of variance. In this chapter, the learning objectives are: List the characteristics of the F distribution, conduct a test of hypothesis to determine whether the variances of two populations are equal, discuss the general idea of analysis of variance, organize data into a oneway and a twoway ANOVA table, conduct a test of hypothesis among three or more treatment means,...
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Chapter 14  Multiple regressions and correlation analysis. In this chapter, the learning objectives are: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination; conduct a test of hypothesis to determine whether regression coefficients differ from zero;...
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Chapter 20  An introduction to decision theory. When you have completed this chapter, you will be able to: Define the terms state of nature, event, decision alternative, and payoff; organize information in a payoff table or a decision tree; find the expected payoff of a decision alternative; compute opportunity loss and expected opportunity loss; assess the expected value of information.
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Chapter 2  Describing data: Frequency tables, frequency distributions, and graphic presentation. After completing this unit, you should be able to: Organize qualitative data into a frequency table, present a frequency table as a bar chart or a pie chart, organize quantitative data into a frequency distribution, present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
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