BINOMIAL THEOREM OBJECTIVES Recognize patterns in binomial expansions. Evaluate a binomial coefficient. Expand a binomial raised to a power. Find a particular term in a binomial expansion Understand the principle of mathematical induction. Prove statements using mathematical induction. Definition: BINOMIAL THEOREM Patterns in Binomial Expansions A number of patterns‚ as follows‚ begin to appear when we write the binomial expansion of a b n‚ where n is a positive integer
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ECON 100A Public Goods and Coase theorem April 29-May 2 Part I Public Goods A good is a (pure) public good if once produced it meets two criteria: 1. Non-rival - A good is non-rival if consumption of additional units of the good involves zero social marginal costs of production. 2. Non-excludable - A good is non-excludable if it impossible‚ or very costly‚ to exclude individuals from benefiting from the good. Taking these two criteria we can categorize goods into four groups. Rival
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Prachi Dewan ECE Department GTBIT. LIST OF EXPERIMENTS (Electrical Science Lab-I) Branch:- EEE /ECE 1. Introduction to various Basic Instruments of Electrical Science 2. To verify Superposition Theorem. 3. To verify Thevenin Theorem and find out Thevenin’s Equivalent circuit using DC Sources. 4. To verify Maximum Transfer Theorm for D.C source. 5. To study R-L-C series circuit and draw its phasor diagram. 6. Measurement of energy and calibration
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NORTH SOUTH UNIVERSITY Spring 2013 EXPERIMENT Maximum Power Transfer Theorem Course: EEE 141 Section: 1 Faculty: MAA Instructor: RKK Date of performance: 23rd March 2013 Date of submission: 25th March 2013 Group – 6 |# |Name |ID | |1 |Md. Al Kaiser |1230032043
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Statistically Independent and Dependent Events 4. Bayes’Theorem Learning Objectives • Understand the basic foundations of probability analysis • Learn the probability rules for conditional probability and joint probability • Use Bayes’ theorem to establish posterior probabilities Reference: Text Chapter 2 Introduction • Life is uncertain; we are note sure what the future will being • Probability is a numerical statement about the likelihood that an event will occur Fundamental
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XXXXXXX I. ABSTRACT The following experiment confirms the validity of the Superposition Theorem. In the experiment in order to acquire the results by means of mathematically deriving them‚ one must use not only the Superposition Theorem but also Nodal Analysis and Current Division to get the same results as getting them experimentally. II. INTRODUCTION The idea behind the Superposition Theorem is that in a circuit that contains several independent sources‚ one wants to know the effects
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Thevenin’s Theorem 1. What is Thevenin’s theorem? What purpose does it serve? Thevenin’s theorem states that any resistive circuit or network‚ no matter how complex‚ can be represented as a voltage source in series with a source resistance. It is used to simplify the load analysis of series-parallel circuits. 2. What is VTH ? How is it measured? Thevenin voltage. In a series-parallel circuit is the voltage present at the output terminals of the circuit when the load is removed. Even though it
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Bayesian Theory: An Introduction Bayesian theory is increasingly being adopted by the data scientists and analysts across the world. Most of the times the data set available or the information is incomplete. To deal with this realm of inductive logic‚ usage of probability theory becomes essential. As per the new perceptions‚ probability theory today is recognized as a valid principle of logic that is used for drawing inferences related to hypothesis of interest. E.T. Jaynes in the late 20th century
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Syllabus for written Recruitment Examination for the post of Post Graduate Assistant in Tamil Nadu Higher Secondary Educational Service. ghl¤Â£l« : jäœ - (Subject Code P01) ÃçÎ 1 - bkhê MuhŒ¢Áæ‹ njh‰w« - bkhê Ïd§fŸ - Âuhél bkhêfŸ - tlbkhê Âuhél bkhêfS¡»ilna cŸs ntWghLfŸ. jäê‹bjh‹ik - ca® jå¢ br«bkhê - fhyªnjhW« jäœ - bjhšfh¥Ãa® fhy¤ jäœ gšyt®‚ gh©oa®‚ nrhH® fhy¤ jäœ. ÃçÎÃçÎ-2 brh‰bwhl® mik¥ò - brh‰bghUŸ kh‰w« - ng¢R¤ jäG«‚ vG¤J¤ jäG« - fl‹ th§fš fiy¢ brhšyh¡f«.. ÃçÎ 3 - vG¤J - brhš Ïy¡fz« - ah¥ò
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prior state probabilities‚ we use Bayes’ Theorem. So the posterior probability of the high performance state is P(H│GN) = P(H)P(GN│H) / P(H)P(GN│H) + P(L)P(GN│L) where P(H│GN) is posterior probability of high state given good news F/S P(H) is prior probability of high state P(GN│H) is probability F/S shows good news in high state P(L) is prior probability of low state P(GN│L) is probability F/S shows good news in low state Thus‚ from Bayes’ Theorem‚ we get P(H│GN) = (0.5 x 0.6) / [(0
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