ity in real-life applications that they have been given their We will discuss the following distributions: • Binomial • Poisson • Uniform • Normal
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binomial experiment and normal approximation to binomial distribution are described Finally, real life examples of binomial distribution with excel tool
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calculating areas under the curve of the probability density function But for the normal distribution, this is not In the real world there are many
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The normal approximation deals with this by “splitting” the difference For example, in the binomial, P(X ? 6 ) = P(X < 7), since 6 is the next possible value
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mean of 527 and a standard deviation of 112 What is the probability of an individual scoring above 500 on the GMAT? Normal Distribution
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Lecture 2: Discrete Distributions, Normal Distributions Chapter 1 Example: toss a (fair) dice of real life phenomena such as errors,
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These means form the sampling distribution of the sample means Page 14 14 Larson Farber, Elementary Statistics: Picturing the World, 3e
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However, continuous models often approximate real-world situations very well, and continuous mathematics (calculus) is frequently easier to work with than
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Normal Probability Distribution Chance in the definition of a random variable Construct a table describing the probability distribution Example
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