Statistical analysis with

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  • If you need to create and interpret statistics in business or classroom settings, this easy-to-use guide is just what you need. It shows you how to use Excel's powerful tools for statistical analysis, even if you've never taken a course in statistics. Learn the meaning of terms like mean and median, margin of error, standard deviation, and permutations, and discover how to interpret the statistics of everyday life. You'll learn to use Excel formulas, charts, PivotTables, and other tools to make sense of everything from sports stats to medical correlations....

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  • What do you need to calculate? Manufacturing output? A curve for test scores? Sports stats? You and Excel can do it, and this non-intimidating guide shows you how. It demystifies the different types of statistics, how Excel functions and formulas work, the meaning of means and medians, how to interpret your figures, and more - in plain English.

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  • Author John M. Quick Reviewers Ajay Ohri Joshua Wiley Project Coordinator Acquisition Editor Douglas Paterson Development Editor Meeta Rajani Technical Editor Vanjeet D'souza Indexer Tejal Daruwale Cover Work Aparna Bhagat Jovita Pinto Proofreaders Aaron Nash Chris Smith Graphics Nilesh Mohite Production Coordinator Aparna Bhagat Editorial Team Leader Akshara Aware Project Team Leader Priya Mukherji

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  • This paper introduces a new statistical calculator which provides a step-by-step guide to data analysis of the research data generated in agriculture, biological sciences and in other fields. The process used to develop the program is quite involved with statistical theories and is not understandable to those who do not have any background in statistics and data analysis. Here we will explain the procedure adopted and the programme used, step-bystep so that it becomes easily comprehensible. We have provided examples in each spreadsheet for analysis.

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  • The first question that I ask an environmental science student who comes seeking my advice on a data analysis problem is Have you looked at your data? Very often, after some beating around the bush, the student answers, Not really. The student goes on to explain that he or she loaded the data into an analysis package provided by an advisor, or downloaded off the web, and it didn’t work. The student tells me, Something is wrong with my data! I then ask my second question: Have you ever used the analysis package with a dataset that did work? After some further beating around the bush,...

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  • We discuss Feature Latent Semantic Analysis (FLSA), an extension to Latent Semantic Analysis (LSA). LSA is a statistical method that is ordinarily trained on words only; FLSA adds to LSA the richness of the many other linguistic features that a corpus may be labeled with. We applied FLSA to dialogue act classification with excellent results. We report results on three corpora: CallHome Spanish, MapTask, and our own corpus of tutoring dialogues.

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  • Lectures "Applied statistics for business - Chapter 1: Data and statistics" provides students with the knowledge: Applications in business and economics, data, data sources, descriptive Statistics, statistical Inference, computer and statistical analysis, data mining, ethical guidelines for statistical practice. Invite you to refer to the disclosures.

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  • (bq) part 2 book "better business decisions from data - statistical analysis for professional success" has contents: relationships with numerical data, relationships with descriptive data, multivariate data, forecasting from known distributions, control charts,...and other contents.

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  • Tensile properties of cooked meat sausages and their correlation with textureprofile analysis (TPA) parameters and physico-chemical characteristics has many contents: Description of the samples, Physico-chemical analysis, Textural analysis, Statistical analysis, Physico-chemical analysis, Textural analysis, Linear regression analysis.

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  • In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/ 2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

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  • This paper deals with studying and modeling static friction coefficient (SFC) and dynamic friction coefficient (DFC) of wheat grain as affected by several treatments. Significance of single effect (SE) and dual interaction effect (DIE) of treatments (moisture content and contact surface) on SFC and, SE, DIE, and triple interaction effect (TIE) of treatments (moisture content, contact surface and sliding velocity) on DFC were determined using statistical analysis methods. Multiple linear regression (MLR) modeling was employed to predict SFC and DFC on different contact surfaces.

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  • Even though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Simple Statistical Analysis of the Impact of Some Nonidealities in Downstream VDSL with Linear Precoding

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  • Provide short courses in experimental design and statistical analysis. An introductory course will be run at Hanoi Agricultural University, with a subsequent more advanced course delivered at Can Tho University Provide Visiting Scholarships to four Vietnamese participants to visit the University of Sydney for upto 13 weeks, to evaluate teaching methods used in applied statistics (biometry) courses, and to provide the opportunity to initiate common research interests.

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  • The help() command prints documentation for the requested topic. The sleep dataset is a built-in dataset in R. It comes from William Sealey Gosset’s article under the pseudonym Student (1908). It contains the effects of two drugs, measured as the extra hours of sleep as compared to controls. The vectors x1 and x2 are assigned the values of the extra hours of sleep in drugs 1 and 2, respectively. (a less than sign followed by a minus sign,

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  • (BQ) Electrical discharge machining (EDM) is a widespread process which works very effectively in machining of difficult-to-cut materials and alloys in die and aerospace industries with high dimensional accuracies. However, this capability could be deteriorated due to electrode wear leading to decrease of process productivity. In this study, the effect of machining parameters of EDM process including on-time, current, voltage, the engaging time between workpiece and electrode, and pre-EDM roughing on electrode wear were experimentally investigated.

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  • This study was carried out to evaluate the bending moment resistance and stiffness of case-type furniture corner joints constructed with wood biscuits, and to determine the effects on these joints of some factors including the spacing between wood biscuit hole centers, the distance from the center of the biscuit hole to the corresponding outside edge of the board material, the method of loading, the type of composite board, and the gluing technique.

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  • The International Union of Geological Sciences (IUGS) has proposed recommendations for the classification of relatively fresh volcanic rocks, but with no specific instructions for altered volcanic rocks, other than discouraging the use of the total alkalis versus silica diagram.

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  • Distillate of upgraded palm biodiesel was blended in different volume percentages (5, 10, 15, and 20%) with jet A-1. The mixture can be used as a replacement for petroleum Jet fuel. Physical properties of blends were measured and compared with those of jet A-1. Empirical equations were developed to predict the properties of blended fuel, including density, kinematic viscosity, freezing point, H/C ratio, and acid value. The statistical analysis indicated that the proposed equations predictions agree well with the experimental data. The predicted model shows an (R2 ) between 0.99–0.

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  • In this study, the effects of various factors (weight fraction of the SiO2, Al2O3, Fe2O3, Na2O, K2O, CaO, MgO, Cl, SO3, and the Blaine of the cement particles) on the concrete compressive strength and also initial setting time have been investigated. Compressive strength and setting time tests have been carried out based on DIN standards in this study. Interactions of these factors have been obtained by the use of analysis of variance and regression equations of these factors have been obtained to predict the concrete compressive strength and initial setting time.

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