
MDU Mathematics III  Sem III Section C : NA(Power series, radius and circle of convergence, Taylor's Maclaurin's and Laurent's seriesZeroes and singularities of comple x functions, Residues Evaluation of real integrals using residues (around unit and semi circle only) Probability Distributions and Hypothesis Testing Conditional probability, Bayes theorem and its applications, expected value of a random variable Properties and application of Binomial, Poisson and Normal distributions) 


1. Probability, Random Variables, and Probability Distributions(Clinical Nutrition and Dietetics) 
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The foundation of all statistical methodology is probability theory, which progresses from elementary to the most advanced mathematics. Much of the misunderstanding and abuse of statistics comes from the lack of understanding of its probabilistic foundation. When assumptions of the underlying probabilistic (mathematical) model are grossly violated, derived inferential methods will lead to misleadi
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Title: MSc.CND103 Research Methods and Biostatistics




2. Chapter 4 * Complex Integration(Mathematics II) 
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Line integral  Cauchy's integral theorem  Cauchy's integral formula  Taylor's and Laurent's series  Singularities  Residues  Residue theorem  Application of residue theorem for evaluation of real integrals  Use of circular contour and semicircular contour.
Title: Engineering Mathematics II
Published on: 09/02/19
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110









4. Statistical Measures(Applied Mathematics) 
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The theory of expectation of a random variable is of considerable importance, since it defines an average, as well as the mean square value and other expected values of a random variable. These expected values often represent important features, characteristics, or attributes of a random variable. One of the most widely known averages is the mean value or expected value.
Title: Probability Theory & Stochastic Processes









7. vtu 7th sem ML module 4 notes(Machine Learning) 
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Title: Machine learning
Published on: 04/04/20
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27








8. Contents(Mathematics II) 
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This comprehensive and assimilable text introduces to the students the Mathematical concepts such as Matrices, Vector Calculus, Analytic Functions, Complex Integration and Laplace Transforms. The simple and systematic presentation will enable the students to grasp the concepts easily.
Title: Engineering Mathematics II
Published on: 09/02/19
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10








9. Some Special Distributions(Applied Mathematics) 
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Now let us discuss some special theoretical distributions in which variables are distributed according to some definite probability law which can be expressed mathematically.
Title: Statistics Random Processes and Queuing Theory




10. Contents(Mathematics) 
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Mathematics is the queen of all sciences. It is a tool that is used by engineers for expression of ideas and for communication of results. Mathematics plays a major role in Engineering as it is one of the major factors that determine how far engineers advance in their career. This book titled “Vector Calculus and Complex Analysis” written by Dr. P. Anuradha and Mr. V. Sudhakar is a good
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Title: Vector Calculus and Complex Analysis
Published on: 24/01/20
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13. Probability Theory  II(Mathematics IV) 
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In this chapter, the study of Probability Theory is continued by considering
what are called the Probability Distributions. Some particular probability distributions, known as the Binomial distribution, Poisson distribution, Exponential distribution and the Normal distribution, are also dealt with.
Title: Engineering Mathematics Part  IV




14. Local Properties of Analytical Functions(Mathematics) 
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This unit is devoted to a consideration of functions which are analytic at all points in a bounded domain except at a finite number of points. Such exceptional points are known as singular points. In this content, the first portion will naturally consists of classification of singular points in terms of the behavior of the function in their neighborhood.
Title: Math 1.3 Complex Analysis  I




15. Testing Hypothesis(Executive Communication and Research Methodology) 
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Researchers in social sciences of ten face situations, Where they collect
and analyse data about a population where the direct Knowledge is rare or not available on hand. As it is difficult to decide or to generalize without population parameters, usually probable statements or suppositions will be made on the basis of available information.
Title: MB112 Executive Communication and Research Methodology




16. STATISTICAL MODELS IN SIMULATION(Computer Science) 
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The world the modelbuilder sees is probabilistic rather than deterministic. Some statistical model might well describe the variations.
Title: System Modeling and Simulation
Published on: 05/10/18
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17








17. RANDOMNUMBER GENERATION, RANDOMVARIATE GENERATION(Computer Science) 
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Random numbers are a necessary basic ingredient in the simulation of almost all discrete systems. Most computer languages have a subroutine, object, or function that will generate a random number.
Title: System Modeling and Simulation
Published on: 05/10/18
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Pages:
43








18. RandomProcesses(Applied Mathematics) 
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Now we are going to study about a family of random variables. A family of random variables indexed by a parameter (like time,) is known as a stochastic process or a random process. We know that the r.v. X, is by definition, a function of the possible outcomes S, of an experiment. Now a random process is a function of both time and S. When time t is fixed, the random process represents the random v
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Title: Statistics Random Processes and Queuing Theory




19. Neural Network Approach and Outer Analysis(Computer Science) 
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Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics, Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to
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Title: MSCS516A Data Mining




20. Density Based and Model Based Clustering(Computer Science) 
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Modelbased methods hypothesize a model for each of the clusters and find the best fit of the data to the given model. A model based method hypothesize a model for each of the clusters and find the best fit of the data to the given model A modelbased algorithm may locate clusters
by constructing a density function that reflects the spatial distribution of the data points.
Title: MSCS516A Data Mining




21. SECOND YEAR HANDBOOK (Mechanical Engineering) 
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The Hand Book provides you the detailed Syllabus and Exam Pattern of REVA University
Title: MECHANICAL ENGINEERINGHANDBOOK




22. Tests of Hypothesis(Quantitative Techniques) 
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After studying this unit, students will be able to assess the meaning, importance of Tests of Hypothesis and to explain the procedure of Testing Hypothesis. They will also be able to identify various types of errors in Hypothesis testing and distinguish between type 1 and type 2 errors.
Title: MB105 Quantitative Techniques




23. INPUT MODELING(Computer Science) 
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Input data provide the driving force for a simulation model. In the simulation of a queuing system, typical input data are the distributions of time between arrivals and service times
Title: System Modeling and Simulation
Published on: 05/10/18
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9








24. Chapter 9 * Statistical Analysis of Data(Physics) 
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Experimental methods are best known scientific methods and are investigated within a controlled setup in a laboratory. Experimental methods are adopted in physical and natural sciences. In social sciences, it is difficult to follow the experimental methods as it is not easy to isolate various forces affecting a phenomenon. So, we have to resort to statistical methods in social and management scie
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Title: A Textbook on Research Methodology
Published on: 27/02/19
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42








25. Contents(Mathematics) 
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This book on Mathematics  IV covers the syllabus for the 2nd year 1st Semester course of B.E / B.Tech programmes offered by various major universities and autonomous colleges. It covers the following topics Functions of a Complex Variable, Complex Integration, Evaluation of Integrals, Fourier Series and Transforms and Applications of PDE.
Title: Mathematics IV
Published on: 27/03/20
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Pages:
10










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