'Normal approximation' presentation slideshows

Statistics

Statistics. Continuous Probability Distributions. Continuous Probability Distributions. Uniform Probability Distribution Normal Probability Distribution Normal Approximation of Binomial Probabilities Exponential Probability Distribution. Exponential. f ( x ). Uniform. f ( x ).

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STT 315

STT 315. Ashwini Maurya. This lecture is based on Chapter 4.6-4.11. Acknowledgement: Author is thankful to Dr. Ashok Sinha, Dr . Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit some of their slides. Normal Distribution. Normal random variable.

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Chapter 10

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Chapter 5 Sampling Distributions

Chapter 5 Sampling Distributions 5.1 Sampling distributions for counts and proportions 5.2 Sampling distributions of a sample mean Chapter 5.1 Sampling distributions for counts and proportions Objectives Binomial distributions for sample counts

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THE CENTRAL LIMIT THEOREM

THE CENTRAL LIMIT THEOREM. The World is Normal Theorem. Sampling Distribution of x- normally distributed population. n=10. Sampling distribution of x: N ( ? , ? /?10). ?/?10. Population distribution: N( ? , ? ). ?. Normal Populations. Important Fact:

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Lecture 9

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The Binomial Distribution

The Binomial Distribution. ST-L5 Objectives: To solve problems using Binomial Probabilities. Learning Outcome B-4. Normal Distributions deal with Continuous Data, and typically analyze how individual observations deviate from central values.

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MA-250 Probability and Statistics

MA-250 Probability and Statistics. Nazar Khan PUCIT Lecture 4. The Normal Curve. An approximation to data distribution that is normally quite accurate Normally data follows such a distribution. The Normal Curve. Standard Units. Express the data in terms of standard deviation

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Wilcoxon Signed Rank Test

Wilcoxon Signed Rank Test . Signed Rank Test Example. You work in the finance department. Is the new financial package faster ( .05 level)? You collect the following data entry times: User Current New Donna 9.98 9.88 Santosha 9.88 9.86 Sam 9.90 9.83 Tamika 9.99 9.80 Brian 9.94 9.87

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POINT ESTIMATION AND INTERVAL ESTIMATION

POINT ESTIMATION AND INTERVAL ESTIMATION. DEFINITIONS.

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Chris Morgan, MATH G160 csmorgan@purdue.edu March 19 , 2011 Lecture 23

Chris Morgan, MATH G160 csmorgan@purdue.edu March 19 , 2011 Lecture 23. Chapter 7.3, 7.4, & 7.5: Point Estimation, Sampling Distribution of x-bar and p-bar . Sampling Distribution of.

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Lecture 9

Lecture 9. Normal Distribution, Central Limit Theorem, Gamma Distribution. The Reproductive Property of Normal Distribution. Examples

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8.4 Normal Approximation to the Binomial Distribution

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Normal Approximation to Binomial Distribution

Normal Approximation to Binomial Distribution. Consider the binomial distribution with n trials, and probability of success is p This distribution is approximately normal if np > 5 and nq > 5. In this case it is approximated by a normal distribution with Mean = np and Variance = npq.

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Class 15. The Central Limit Theorem

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Meta-Study

Meta-Study. Representation of the Sampling Distribution of Y?. The Sampling Distribution of . Example: Sampling Distribution. In a certain population of fish, the lengths of individual fish follow a normal distribution with mean 54 mm and standard deviation 4.5 mm

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CHAPTER 23

CHAPTER 23. BOOTSTRAPPING: LEARNING FROM THE SAMPLE. NONPARAMETRIC STATISTICAL INFERENCE. Assumptions we make about models, such as the Classical Linear Regression Model assumptions, may not be tenable in practice.

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other paired-comparison statistics

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