MGT 601: Statistical Inference Lecture 03 Dr. MUMTAZ AHMED Objectives of Current Lecture In the current lecture: Introduction to Probability Definition and Basic concepts of probability Some basic questions related to probability Laws of probability Conditional probability Independent and Dependent Events Related Examples 2 Probability Probability (or likelihood) is a measure or estimation of how likely it is that something will happen or that a statement is true. For example
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Narendra Damodardas Modi ([nəreːnd̪rə d̪ɑːmoːd̪ərəd̪ɑːs moːd̪iː] ( listen)‚ born 17 September 1950) is the 15th and current Prime Minister of India. Modi‚ a leader of the Bharatiya Janata Party (BJP)‚ also served as Chief Minister of Gujarat from 2001–14. He represents Varanasi as its Member of Parliament (MP). Modi was a key strategist for the BJP in the successful 1995 and 1998 Gujarat state election campaigns. He became Chief Minister of Gujarat in October 2001 and served longer by far in that
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DECISION ANALYSIS LECTURER : DR. SYARIZA ABDUL RAHMAN email: syariza@uum.edu.my tel: 04 – 9286975/ 016-4127923 1. COURSE SYNOPSIS Mathematical tools have been applied for thousands of years; however‚ the formal study and application of quantitative techniques to practical decision making is largely a product of the twentieth century. Decision analysis refers to a body of techniques that allows a decision-maker to evaluate uncertainty‚ risk‚ and multiple objectives decision
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1 Why probability and statistics? Is everything on this planet determined by randomness? This question is open to philosophical debate. What is certain is that every day thousands and thousands of engineers‚ scientists‚ business persons‚ manufacturers‚ and others are using tools from probability and statistics. The theory and practice of probability and statistics were developed during the last century and are still actively being refined and extended. In this book we will introduce the basic notions
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Probability Concepts 1. Fundamental Concepts of Probability 2. Mutually Exclusive and Collectively Exhaustive 3. 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
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“APPLE “ Daily? Since Apple Inc. produce iphone‚ it seems that iphone change our defination of smart phone. Iphone create a culture seems that people cannot live without it anymore‚ people use it anytime and anywhere. Iphone has been in the name of smartphones. It can be treat as a new cultural artefact and the latest medium of modern culture‚ it changes our way of communication‚ social connection‚ and having an iphone seems to be a lifestyle. The new forms of communication IPhone is a mobile
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Chapter 3 Probability True/False 1. A contingency table is a tabular summary of probabilities concerning two sets of complementary events. Answer: True Difficulty: Medium 2. An event is a collection of sample space outcomes. Answer: True Difficulty: Easy 3. Two events are independent if the probability of one event is influenced by whether or not the other event occurs. Answer: False Difficulty: Medium 4. Mutually exclusive events have a nonempty
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A group of investors wants to develop a chain of fast-food restaurants. In determining potential costs for each facility‚ they must consider‚ among other expenses‚ the average monthly electric bill. They decide to sample some fast-food restaurants currently operating to estimate the monthly cost of electricity. They want to be 90% confident of their results and want the error of the interval estimate to be no more than $100. They estimate that such bills range from $600 to $2‚500. How large a sample
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Homework #4 - SQL Use the following (denormalized) database schema (and the attached tables) to write the queries. Publishers (custid‚ name‚ city‚ phone‚ creditcode) Bookjobs (jobid‚ custid‚ jobdate‚ descr‚ jobtype) POS (jobid‚ poid‚ podate‚ vendorid) Items (itemid‚ descr‚ onhand‚ price) Po_Items (jobid‚ poid‚ itemid‚ quantity) For each question‚ turn in the Oracle SQL query and the output. You should feel free to do these by hand (paper and pencil)‚ or you may actually run them
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classical and empirical probabilities. a. Classical probabilities are based on assumptions; Empirical probabilities are based on observations. b. Classical probabilities do not require an action to take place; Empirical probabilities have to have been “performed”. 2) Gather 16 to 30 coins. Shake and empty bag of coins 10 times and tally up how many head and tails are showing. Number of coins: 20 * Consider the first toss‚ what is the observed probability of tossing a head? Of
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