REVIEW – APPLICATIONS OF THE NORMAL DISTRIBUTION.

Slides:



Advertisements
Similar presentations
Exponential Distribution. = mean interval between consequent events = rate = mean number of counts in the unit interval > 0 X = distance between events.
Advertisements

Binomial Distributions
Sampling distributions. Example Take random sample of students. Ask “how many courses did you study for this past weekend?” Calculate a statistic, say,
The Central Limit Theorem Section Starter Assume I have 1000 pennies in a jar Let X = the age of a penny in years –If the date is 2007, X = 0 –If.
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.
Central Limit Theorem and Normal Distribution EE3060 Probability Yi-Wen Liu November 2010.
Chapter Six Sampling Distributions McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Standard Normal Distribution
Lesson #17 Sampling Distributions. The mean of a sampling distribution is called the expected value of the statistic. The standard deviation of a sampling.
1 Business 90: Business Statistics Professor David Mease Sec 03, T R 7:30-8:45AM BBC 204 Lecture 18 = Start Chapter “The Normal Distribution and Other.
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
Continuous Probability Distribution  A continuous random variables (RV) has infinitely many possible outcomes  Probability is conveyed for a range of.
Lesson 12-1 Algebra Check Skills You’ll Need 12-4
Review of normal distribution. Exercise Solution.
From Last week.
1 Applied Calculus II Confidence Tests Slides subject to change.
Normal Distribution u Note: other distributions –hypergoemetric - sampling with replacement –beta –bimodal –VanGenuchten u Normal Probability Density Function.
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.
In this chapter we will consider two very specific random variables where the random event that produces them will be selecting a random sample and analyzing.
Poisson Random Variable Provides model for data that represent the number of occurrences of a specified event in a given unit of time X represents the.
Sampling and sampling distibutions. Sampling from a finite and an infinite population Simple random sample (finite population) – Population size N, sample.
Section 9.3 Distribution of Sample Means AP Statistics February 5, 2010 Berkley High School.
Expected Value and Standard Error for a Sum of Draws (Dr. Monticino)
Sampling Distribution of the Sample Mean. Example a Let X denote the lifetime of a battery Suppose the distribution of battery battery lifetimes has 
Chapter 10 – Sampling Distributions Math 22 Introductory Statistics.
Section 5.2 The Sampling Distribution of the Sample Mean.
7.3 and 7.4 Extra Practice Quiz: TOMORROW THIS REVIEW IS ON MY TEACHER WEB PAGE!!!
Sampling Distribution of a Sample Mean Lecture 30 Section 8.4 Mon, Mar 19, 2007.
Sampling Distributions-Chapter The Central Limit Theorem 7.2 Central Limit Theorem with Population Means 7.3 Central Limit Theorem with Population.
Statistics 300: Elementary Statistics Section 6-5.
Chapter 6.3 The central limit theorem. Sampling distribution of sample means A sampling distribution of sample means is a distribution using the means.
BUS304 – Chapter 6 Sample mean1 Chapter 6 Sample mean  In statistics, we are often interested in finding the population mean (µ):  Average Household.
Chapter 7: Introduction to Sampling Distributions Section 2: The Central Limit Theorem.
Distribution of the Sample Mean (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.
1 Since everything is a reflection of our minds, everything can be changed by our minds.
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.
Estimation Chapter 8. Estimating µ When σ Is Known.
6.3 THE CENTRAL LIMIT THEOREM. DISTRIBUTION OF SAMPLE MEANS  A sampling distribution of sample means is a distribution using the means computed from.
Using the Tables for the standard normal distribution.
By Satyadhar Joshi. Content  Probability Spaces  Bernoulli's Trial  Random Variables a. Expectation variance and standard deviation b. The Normal Distribution.
Review Normal Distributions –Draw a picture. –Convert to standard normal (if necessary) –Use the binomial tables to look up the value. –In the case of.
7.2 Notes Central Limit Theorem. Theorem 7.1 – Let x be a normal distribution with mean μ and standard deviation σ. Let be a sample mean corresponding.
Chapter 6 The Normal Distribution Section 6-3 The Standard Normal Distribution.
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.
Normal Normal Distributions  Family of distributions, all with the same general shape.  Symmetric about the mean  The y-coordinate (height) specified.
STA 2023 Section 5.4 Sampling Distributions and the Central Limit Theorem.
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.
Ch5.4 Central Limit Theorem
Central Limit Theorem Sample Proportions.
Sec. 7-5: Central Limit Theorem
Binomial Fixed number trials Independent trials Only two outcomes
Normal Density Curve. Normal Density Curve 68 % 95 % 99.7 %
Using the Tables for the standard normal distribution
Lecture Slides Elementary Statistics Twelfth Edition
Lecture Slides Elementary Statistics Twelfth Edition
CHAPTER 15 SUMMARY Chapter Specifics
Sampling Distribution of the Mean
Section 9.3 Distribution of Sample Means
Exam 2 - Review Chapters
If the question asks: “Find the probability if...”
Central Limit Theorem Accelerated Math 3.
Theorem 5.3: The mean and the variance of the hypergeometric distribution h(x;N,n,K) are:  = 2 = Example 5.10: In Example 5.9, find the expected value.
Sample Means Section 9.3.
Central Limit Theorem cHapter 18 part 2.
Statistics 101 Chapter 8 Section 8.1 c and d.
Presentation transcript:

REVIEW – APPLICATIONS OF THE NORMAL DISTRIBUTION

What is the mean and standard deviation of a Standard Normal Distribution? Why do we need a Standard Normal Distribution? The Standard Normal Distribution

The life span of domesticated cats is normally distributed with a mean of 15.7 years and a standard deviation of 1.6 years. What is the probability that a cat will live to be older than 16 years? Finding Probabilities

The life span of domesticated cats is normally distributed with a mean of 15.7 years and a standard deviation of 1.6 years. How old would a cat have to be for it to be in the top 10% of cat ages? Finding Values

The life span of domesticated cats is normally distributed with a mean of 15.7 years and a standard deviation of 1.6 years. If we sampled 9 cats, what is the probability that their mean age would exceed 16 years? The Central Limit Theorem

The average number of cats “singing” outside my window every night is 4. What is the probability that on a randomly selected night, there will be 5 cats “singing”? The Binomial and Poisson Distributions

In the US, about 63% of all domesticated cats have been “de-clawed”. In a random sample of 320 cats, what is the probability that at least 200 have been declawed? Normal Approximation to a Binomial Distribution.

Unit 5 – Review (The Normal Distribution)  The Standard Normal Distribution  Finding Probabilities  Finding Values  The Central Limit Theorem  The Poisson and Binomial Distribution  Approximating the Binomial Distribution with a Normal Distribution  The Benchmark Assessment will be Tuesday, February 26th  Assignment  (Quick Quiz) Page 319 Questions 1 – 10  (Review Exercises) Page 319 Questions 1 – 5  Page 320 Questions 7, 8, 9, 11