...Analytic functions Introduction I was interested in doing my Internal assessment in functions particularly in analytic functions as I was much fascinated with topic (functions)during the study of mathematics during the course. The theory part that includes Taylor series as well as its coverage on complex functionality. In this exploration I surveyed on the theorems associated with analytic functions as well as its functions. This has helped me widen my knowledge and mathematical skills on complex numbers, calcus and functions as topics studied during the course. I have always to kept correct justification on the theories and have used required mathematical models and correct interpretation in the results that I got in the theory processing. Analytic functions has a very wider application on Analytic modulated system whereas there is a general theory of analytic modulation system and they are developed in the transmitted signal σ(t) = Re {eiwctf(z(t)}.Due to this noticeable physical application in life, it motivated me to write a this maths exploration. In mathematics, an analytic function could be simply defined as a function that is locally given by a convergent power series. There exist two parts namely real analytic functions and complex analytic functions, functioning differently. These functions are infinitely differentiable, But as said above functions of each type are infinitely differentiable, having the complex analytic functions exhibiting properties that...
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...semester, you will see a wide variety of symbols used repeatedly from week to week. These symbols are usually defined when the text introduces them, but it is sometimes hard to remember exactly what symbols/operators mean what. Here are two handy reference tables to keep them straight. This list is not exhaustive, rather it is designed to include symbols that are used often, not just as a "one-off." Also note that in some occasions, the text will sometimes use certain symbols/variables for purposes other than the ones listed here. For example, π usually refers to the population proportion and r usually refers to the coefficient of correlation. However, on occasions the textbook will use π to refer to the numerical constant 3.1415926... and in chapter 11, r is used as the number of factors in a 2 way ANOVA. You should ensure that the usage in the text matches the description given here. The first table contains symbols, the second operators. In this first table, the first column contains the symbol itself and the second column a brief definition of what that symbol refers to. In cases where there is a particular equation defined by the symbol, a page reference can be found in the third column. Some of the symbols rely upon having a browser that will render HTML 4.0 and a unicode font installed on your computer, so if you have trouble seeing any of the symbols you may need to switch browsers or update your browser. Symbol | Meaning/Definition | Page Ref. | N | Population Size...
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...|10 | |DQ’s (All Weeks – 2% each) |10 | |Weekly Summaries (All Weeks - 1% each) |5 | |Business Research Paper (week One) |4 | |Survey (Week Two) |6 | |Assignments from the Text - – Section Exercises (Week Two) |6 | |Statistical Symbols and Definitions Matching Assignment – It is included in this syllabus (NOT the one in the |4 | |e-source) after weekly individual assignments below - (Week Three) | | |Assignments from the Text – Section Exercises (Week Three)...
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...University of Phoenix Material Statistical Symbols and Definitions Matching Assignment Match the letter of the definition on the right to the appropriate symbol on the left. |Symbols |Definitions | |( (Uppercase Sigma) B.__ |Null hypothesis | |( (Mu) H.___ |Summation | |( (Lowercase Sigma) E.___ |Factorial | |( (Pi) I.___ |Nonparametric hypothesis test | |( (Epsilon) G.__ |Population standard deviation | |(2 (Chi Square) D.___ |Alternate hypothesis | |! C.___ |Maximum allowable error | |H0 A.__ |Population mean | |H1 F.__ |i. Probability of success in a binomial...
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...Course Syllabus RES/341 RESEARCH AND EVALUATION I Welcome to RES/341, let’s do everything we can to ensure that the next six weeks will be an enlightening and enjoyable learning experience for all of us. Please print a copy of this syllabus for handy reference. Whenever there is a question about what assignments are due, this syllabus is considered the ruling document. Classroom Management Policies Breaks in the On Campus classes will be when deemed necessary. Please leave the classroom clean. Phones: Turn them off or keep them in silent mode. ***DO NOT answer the phone in the classroom. Laptop/notebook computers: If I determine the use of a laptop during class time is disruptive behavior that hinders or interferes with the educational process, you will be required to turn it off. Technical Support Technical Support is available 24 hours a day, 365 days a year. Call 1-877-832-4867, or use the e-mail support form. For answers to the most common issues, go to “Knowledge Base” by clicking Help, found at the top of every student Web site. Course Description See eCampus. Course Topics & Objectives See eCampus. Course Materials See eCampus. Participation In an intensive, collaborative learning environment such as that of University of Phoenix, class attendance is perhaps the most obvious and objective starting point as a measure for participation. If you are not in attendance, you miss out on many opportunities for learning. Consequently, if you...
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... |College of Natural Sciences | | |MTH/233 Version 2 | | |Statistics | Copyright © 2010, 2006 by University of Phoenix. All rights reserved. Course Description This course surveys descriptive and inferential statistics with an emphasis on practical applications of statistical analysis. The principles of collecting, analyzing, and interpreting data are covered. It examines the role of statistical analysis, statistical terminology, the appropriate use of statistical techniques and interpretation of statistical findings through applications and functions of statistical methods. Policies Faculty and students/learners will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies...
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...processes in which entropy increases; such as a smell diffusing in a room, ice melting in lukewarm water, salt dissolving in water, and iron rusting. The laws of thermodynamics govern the direction of a spontaneous process, ensuring that if a sufficiently large number of individual interactions (like atoms colliding) are involved then the direction will always be in the direction of increased entropy (since entropy increase is a statistical phenomenon). Entropy is a chemical concept that is very difficult to explain, because a one-sentence definition will not lead to a comprehensive statement. Thus, few people understand what entropy really is. You are not alone if you have some difficulty with this concept. The word entropy is used in many other places and for many other aspects. We confine our discussion to thermodynamics (science dealing with heat and changes) and to chemical and physical processes. We have defined energy as the driving force for changes; entropy is also a driving force for physical and chemical changes (reactions). Entropy, symbol S, is related to energy, but it a different aspect of energy. This concept was developed over a long period of time. Human experienced chemical and physical changes that cannot be explained by energy...
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...regions of higher temperature to regions of lower temperature. English language speakers refer to an object of low temperature as being cold, and associate various degrees of higher temperature to terms such as luke-warm, warm, hot, and others. Historically, two equivalent concepts of temperature have developed, the thermodynamic description and a microscopic explanation based on statistical physics. Since thermodynamics deals entirely with macroscopic measurements, the thermodynamic definition of temperature, first stated by Lord Kelvin, is stated entirely in empirical, measurable variables. Statistical physics provides a deeper understanding of thermodynamics by describing matter as a collection of a large number of particles, and derives thermodynamic (i.e. macroscopic) parameters as statistical averages of the microscopic parameters of the particles. In statistical physics, it is shown that the thermodynamic definition of temperature can be interpreted as a measure of the average energy in each degree of freedom of the particles in the thermodynamic system. Because its temperature is seen as a statistical property, a system must contain a large number of particles for temperature to have a useful...
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...CHAPTER ONE INTRODUCTION 1.1 Background of the study Every society shapes its children in the image of its own culture(Berger and Berger 1979). In ancient Sparta, young boys were taught discipline, obedience, physical prowess and self-denial through harsh treatment and deprivation. In nearby Athens , parents raised their sons to be artistically sensitive and broadly educated as well as athletic. These practices produced quite different individuals as well as societies( Berger and Berger , 1979). The process of instilling such fundamental elements of culture in a society’s new members is called socialization. The nature of the human animal both allows and requires socialization(Elkin and Handel, 1984,p.18). Through socialization people learn to participate effectively in the communities to which they belong. When people from all walks of life with different cultures converge at one place for educational purposes there is usually a clash of cultures and through interaction and new socialization it is possible to create a new culture where these people can learn to co-exist with their differences, they learn from each other and learn to integrate new norms and values with their existing ones. This is known as resocialization or secondary socialization. Secondary socialization takes place outside the home. It is where children and adults learn how to act in a way that is appropriate for the situations that they are in. schools require very different behavior from the...
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...PROFESSIONAL WORK ETHIC OF PHARMACISTS IN GOVERNMENT TERTIARY HOSPITALS IN BAGUIO AND BENGUET ______________ A Thesis Proposal Presented to the Faculty of the Graduate School Baguio Central University Baguio City ______________ In Partial Fulfillment of the Requirements for the Degree Master in Public Administration _____________ Sharon M. Cuyugan January 2015 APPROVAL SHEET This thesis proposal entitled, “PROFESSIONAL WORK ETHIC OF PHARMACISTS IN GOVERNMENT TERTIARY HOSPITALS IN BAGUIO AND BENGUET”, prepared and submitted by SHARON M. CUYUGAN, in partial fulfillment of the requirements for the degree, MASTER IN PUBLIC ADMINISTRATION (MPA), has been reviewed and examined and is hereby endorsed for acceptance and approval for proposal defense. LOUELLA M. BROWN, Ed.D. Professor 300-A PROPOSAL EXAMINATION COMMITTEE JOSE R. BALCANAO, Ph.D. Chairman LOUELLA M. BROWN, Ed.D. ESTRELLA V. BISQUERRA, Ph.D. Member Member ACKNOWLEDGMENT The researcher wishes to thank the following for their assistance in the completion of this dissertation proposal: The Good Lord, for giving the researcher sufficient encouragement so she may pursue this challenging endeavor; Her family, for all the support, understanding and unconditional love; Members of the proposal committee, Dr. Jose R. Balcanao, Dr. Estrella V. Bisquerra, and Dr. Louella M. Brown, for their critiques and helpful suggestions;...
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...| (1) | | | | (2) | | | | (3) | | | | (4) | so the distribution of angle is given by | (5) | This is normalized over all angles, since | (6) | and | | | (7) | | | | (8) | | | | (9) | The general Cauchy distribution and its cumulative distribution can be written as | | | (10) | | | | (11) | where is the half width at half maximum and is the statistical median. In the illustration about, . The Cauchy distribution is implemented in the Wolfram Language as CauchyDistribution[m, Gamma/2]. The characteristic function is | | | (12) | | | | (13) | The moments of the distribution are undefined since the integrals | (14) | diverge for . If and are variates with a normal distribution, then has a Cauchy distribution with statistical median and full width | (15) | The sum of variates each from a Cauchy distribution has itself a Cauchy distribution, as can be seen from | | | (16) | | | | (17) | where is the characteristic function and is the inverse Fourier transform, taken with parameters . Definition | | Properties | The distribution is symmetric about the parameter a.The parameter b determines the width of the distributionThe central moments are undefined for Cauchy distributed data, which is a consequence of the long tails of the density function (see example below).The median and the mode are equal to the location parameter a.The standard Cauchy...
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...communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. There are subfields which are focussed on the solution of specific problems, on one of several possible approaches, on the use of widely differing tools and towards the accomplishment of particular applications. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described...
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...CHAPTER 1 INTRODUCTION Background of the Study Poetry is perpetually re-creating language. It helps understand the world by sharpening our own senses, by making us more sensitive to life. Poetry is thought that is felt. Aristotle says, “There is nothing in the intellect that is not first in the senses”. The poet uses figures of speech and creates images-imitations of life, words that evoke mental pictures and appeal to our senses. The essence of poetry is, according to the different types of minds, either quite worthless or of infinite importance (Herbert, 2000). Poetry may be described as rhythmic imaginative language expressing the invention, thought, imagination, taste, passion and insight of the human soul. Its purpose is “enthrallment”. William Wordsworth describes it as “the spontaneous overflow of powerful feelings” taking its origin from “emotion recollected in tranquility”. For Edgar Allan Poe, poetry is “the rhythmical creation of beauty”. Poets, from their own store of felt, observed or imagined experiences, select, combine, and recognize. They create significant new experiences for the readers-significant because the focused and formed in which they may gain a greater awareness and understanding of the world. Poetry can be recognized only by the response made to it by a good reader, someone who has acquired some sensitivity to poetry. There is indeed an ideal reader or listener as well as an ideal poem; and it is useful to think about them all and to consider...
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...STATISTICAL METHODS STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA92093-0961, La Jolla, USA. Email: arno@salk.edu. Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics represents that body of methods by which characteristics of a population are inferred through observations made in a representative sample from that population. Since scientists rarely observe entire populations, sampling and statistical inference are essential. This article first discusses some general principles for the planning of experiments and data visualization. Then, a strong emphasis is put on the choice of appropriate standard statistical models and methods of statistical inference. (1) Standard models (binomial, Poisson, normal) are described. Application of these models to confidence interval estimation and parametric hypothesis testing are also described, including two-sample situations when the purpose is to compare two (or more) populations with respect to their means or variances. (2) Non-parametric inference tests are also described in cases where the data sample distribution is not compatible with standard parametric distributions. (3) Resampling methods using many randomly computer-generated samples are finally introduced for estimating characteristics of a distribution and for statistical inference. The following section deals with methods...
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...Six Sigma Brief History of Six Sigma The roots of Six Sigma as a measurement standard can be traced back to Carl Frederick Gauss (1777-1855) who introduced the concept of the normal curve. Six Sigma as a measurement standard in product variation can be traced back to the 1920′s when Walter Shewhart showed that three sigma from the mean is the point where a process requires correction. Many measurement standards (Cpk, Zero Defects, etc.) later came on the scene but credit for coining the term “Six Sigma” goes to a Motorola engineer named Bill Smith (“Six Sigma” is a federally registered trademark of Motorola). The evolution of six sigma began in the late 1970s, when a Japanese firm took over a Motorola factory that manufactured television sets in the United States and the Japanese promptly set about making drastic changes to the way the factory operated. Motorola recognized that its quality was poor and management decided to take quality seriously. When Bob Galvin became Motorola's CEO in 1981, he challenged his company to achieve a tenfold improvement in performance over a five-year period. On January 15, 1987, Galvin launched a long term quality program, called “The Six Sigma Quality Program”. The program was a corporate program which established Six Sigma as the required capability level to approach the standard of 3.4 Defects per Million Opportunities (DPMO). The Corporate Policy Committee of Motorola then updated their quality goal as follows: “Improve product and...
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