IPPR #: EDUC 530 Lesson Plan: Place Value‚ Integer‚ Computation |Teacher Candidate: |Course: EDUC 530 | |LESSON PREPARATION [before the lesson] | |Topic: Place Value‚ Integer‚ Computation |Concept: Regrouping
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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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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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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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probability questions : 1. A real estate office has been averaging 1.8 sales per day for the past several months. What is the probability that the office will make 4 sales today? .0723 2. A washing machine in a Laundromat breaks down an average of two times per month. What is the probability that the machine will break down more than 28 times in the next year? .1775 3. Flaws occur randomly in a particular fabric with a mean rate of occurance of 1.5 every 5 sqare yards. If you purchase 20 square
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Introduction: The purpose of this lab is to apply Mendel’s laws to predict the probability of the occurrence of a single event‚ of two independent events and of certain traits in offspring of parents exhibiting traits. Gregor Mendel was an Austrian monk in 1866‚ who studied how traits were passed using pea plants. From his studies of inheritance‚ he created three laws of inheritance: the law of dominance‚ the law of segregation‚ and the law of independent assortment. He called genes ‘’factors’’
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companies and the probability of getting a job offer there. These data are tabulated below. The tabulation is in the decreasing order of cost. 1. If the graduate applies to all 10 companies‚ what is the probability that she will get at least one offer? 2. If she can apply to only one company‚ base on cost and success probability criteria alone‚ should she apply to company 5? Why or why not? 3. If she applies to companies 2‚5‚8‚ and 9‚ what is the total cost? What is the probability that she will
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Pedram Ghodrati Math Why do we have to learn algebra? Math is an academic subject which many students struggle to master. I remember when I was in high school‚ I always had a hard time understanding math. In senior year I almost dropped out of school because of the difficulty of high levels of math classes. I just read an article called‚ “Is Algebra Necessary?” by Andrew Hacker‚ an American political scientist and public intellectual. He is currently Professor Emeritus in the Department of Political
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The Poisson probability distribution‚ named after the French mathematician Siméon-Denis. Poisson is another important probability distribution of a discrete random variable that has a large number of applications. Suppose a washing machine in a Laundromat breaks down an average of three times a month. We may want to find the probability of exactly two breakdowns during the next month. This is an example of a Poisson probability distribution problem. Each breakdown is called an occurrence in Poisson
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