Normal Distribution u Note: other distributions –hypergoemetric - sampling with replacement –beta –bimodal –VanGenuchten u Normal Probability Density Function.

Slides:



Advertisements
Similar presentations
The Normal Distribution
Advertisements

REVIEW – APPLICATIONS OF THE NORMAL DISTRIBUTION.
Sampling Distributions (§ )
Ka-fu Wong © 2003 Chap 8- 1 Dr. Ka-fu Wong ECON1003 Analysis of Economic Data.
Distribution of Sample Means, the Central Limit Theorem If we take a new sample, the sample mean varies. Thus the sample mean has a distribution, called.
Assuming normally distributed data! Naïve Bayes Classifier.
Chapter Six Sampling Distributions McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Distribution of Sample Means 2011, 10, 20. Today ’ s Topics What is distribution of sample means?** Properties of distribution of sample means* How to.
8 Statistical Intervals for a Single Sample CHAPTER OUTLINE
6-5 The Central Limit Theorem
Modular 13 Ch 8.1 to 8.2.
The Central Limit Theorem For simple random samples from any population with finite mean and variance, as n becomes increasingly large, the sampling distribution.
Sample Distribution Models for Means and Proportions
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
QUIZ CHAPTER Seven Psy302 Quantitative Methods. 1. A distribution of all sample means or sample variances that could be obtained in samples of a given.
What if the population standard deviation, , is unknown? We could estimate it by the sample standard deviation s which is the square root of the sample.
The Central Limit Theorem. 1. The random variable x has a distribution (which may or may not be normal) with mean and standard deviation. 2. Simple random.
(c) 2007 IUPUI SPEA K300 (4392) Outline Normal Probability Distribution Standard Normal Probability Distribution Standardization (Z-score) Illustrations.
AP Statistics 9.3 Sample Means.
Sampling and sampling distibutions. Sampling from a finite and an infinite population Simple random sample (finite population) – Population size N, sample.
Chapter 10 – Sampling Distributions Math 22 Introductory Statistics.
Sampling Distribution of a Sample Mean Lecture 30 Section 8.4 Mon, Mar 19, 2007.
Statistics Workshop Tutorial 5 Sampling Distribution The Central Limit Theorem.
Statistics 300: Elementary Statistics Section 6-5.
Section 5.4 Sampling Distributions and the Central Limit Theorem Larson/Farber 4th ed.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc.. Chap 7-1 Developing a Sampling Distribution Assume there is a population … Population size N=4.
Chapter 6.3 The central limit theorem. Sampling distribution of sample means A sampling distribution of sample means is a distribution using the means.
Distribution of the Sample Mean (Central Limit Theorem)
Slide Slide 1 Section 6-5 The Central Limit Theorem.
Sample Variability Consider the small population of integers {0, 2, 4, 6, 8} It is clear that the mean, μ = 4. Suppose we did not know the population mean.
Section 6-5 The Central Limit Theorem. THE CENTRAL LIMIT THEOREM Given: 1.The random variable x has a distribution (which may or may not be normal) with.
The Central Limit Theorem 1. The random variable x has a distribution (which may or may not be normal) with mean and standard deviation. 2. Simple random.
Stat 112: Notes 2 Today’s class: Section 3.3. –Full description of simple linear regression model. –Checking the assumptions of the simple linear regression.
Sampling Error SAMPLING ERROR-SINGLE MEAN The difference between a value (a statistic) computed from a sample and the corresponding value (a parameter)
Binomial Distributions Mean and Standard Deviation.
Review of Statistical Terms Population Sample Parameter Statistic.
1 Sampling distributions The probability distribution of a statistic is called a sampling distribution. : the sampling distribution of the mean.
Chapter 18: The Central Limit Theorem Objective: To apply the Central Limit Theorem to the Normal Model CHS Statistics.
Lecture 5 Introduction to Sampling Distributions.
1 Copyright © 2015 Elsevier Inc. All rights reserved. Chapter 4 Sampling Distributions.
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
Sampling Distributions
Example A population has a mean of 200 and a standard deviation of 50. A random sample of size 100 will be taken and the sample mean x̄ will be used to.
7.2 Sample Proportions Objectives SWBAT: FIND the mean and standard deviation of the sampling distribution of a sample proportion. CHECK the 10% condition.
Statistics -Continuous probability distribution 2013/11/18.
Ch5.4 Central Limit Theorem
6.39 Day 2: The Central Limit Theorem
Inference: Conclusion with Confidence
6-3The Central Limit Theorem.
Sec. 7-5: Central Limit Theorem
Chapter 7: Sampling Distributions
Warm Up 1) A 2016 study from the State Department found that 46% of American citizens hold a passport. If repeated samples of 40 American citizens are.
CHAPTER 7 Sampling Distributions
The Normal Probability Distribution Summary
Distribution of the Sample Proportion
Sampling Distribution
Sampling Distribution
QQ Plot Quantile to Quantile Plot Quantile: QQ Plot:
Year-3 The standard deviation plus or minus 3 for 99.2% for year three will cover a standard deviation from to To calculate the normal.
CHAPTER 7 Sampling Distributions
Warm Up 1) A 2016 study from the State Department found that 46% of American citizens hold a passport. If repeated samples of 40 American citizens are.
Sampling Distribution of the Mean
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
Sampling Distribution of a Sample Mean
Sampling Distributions (§ )
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
Review of Hypothesis Testing
Presentation transcript:

Normal Distribution u Note: other distributions –hypergoemetric - sampling with replacement –beta –bimodal –VanGenuchten u Normal Probability Density Function –pdf:

u Checking for normality »IQR/s =~ 1.3 »Normal probability plot »Chi-square distribution

u Sampling Distributions –CHKOUT.XLS »500 data points »  = 50.1,  = 49.1 »take a random sample of 5 u compute sample mean u repeat 100 times »now have a distribution of 100 sample means »If we have a large sample size, the distribution of sample means: u is approximately normal, regardless of original distribution u mean of sample means = mean of population u standard deviation of sample means = population standard deviation / n

u Example 6.16 –Before: LCN  = 60,  = 10 –take random sample of 40 concrete blocks –a. if no different after… –b. If after is no different, find the probability that the sample mean exceeds 64 –c. if sample mean = 73, is new better than old?