Lecture 9 Dustin Lueker. 2  Perfectly symmetric and bell-shaped  Characterized by two parameters ◦ Mean = μ ◦ Standard Deviation = σ  Standard Normal.

Slides:



Advertisements
Similar presentations
NORMAL CURVE Needed for inferential statistics. Find percentile ranks without knowing all the scores in the distribution. Determine probabilities.
Advertisements

Chapter 8 – Normal Probability Distribution A probability distribution in which the random variable is continuous is a continuous probability distribution.
Chapter 6 The Normal Distribution
The Normal Probability Distribution
Standard Normal Distribution The Classic Bell-Shaped curve is symmetric, with mean = median = mode = midpoint.
BCOR 1020 Business Statistics Lecture 13 – February 28, 2008.
1. Normal Curve 2. Normally Distributed Outcomes 3. Properties of Normal Curve 4. Standard Normal Curve 5. The Normal Distribution 6. Percentile 7. Probability.
Chapter 11: Random Sampling and Sampling Distributions
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Z Score The z value or z score tells the number of standard deviations the original.
6.3 Use Normal Distributions
Quiz 5 Normal Probability Distribution.
Common Probability Distributions in Finance. The Normal Distribution The normal distribution is a continuous, bell-shaped distribution that is completely.
AP Statistics: Section 2.1 A. Measuring Relative Standing: z-scores A z-score describes a particular data value’s position in relation to the rest of.
The Mean of a Discrete Probability Distribution
In 2009, the mean mathematics score was 21 with a standard deviation of 5.3 for the ACT mathematics section. ReferenceReference Draw the normal curve in.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 1 PROBABILITIES FOR CONTINUOUS RANDOM VARIABLES THE NORMAL DISTRIBUTION CHAPTER 8_B.
Topics Covered Discrete probability distributions –The Uniform Distribution –The Binomial Distribution –The Poisson Distribution Each is appropriately.
Chapter Six Normal Curves and Sampling Probability Distributions.
1 Normal Random Variables In the class of continuous random variables, we are primarily interested in NORMAL random variables. In the class of continuous.
Lecture 9 Dustin Lueker.  Can not list all possible values with probabilities ◦ Probabilities are assigned to intervals of numbers  Probability of an.
Advanced Algebra II Normal Distribution. In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has.
4.3 NORMAL PROBABILITY DISTRIBUTIONS The Most Important Probability Distribution in Statistics.
Continuous distributions For any x, P(X=x)=0. (For a continuous distribution, the area under a point is 0.) Can ’ t use P(X=x) to describe the probability.
Slide 1 © 2002 McGraw-Hill Australia, PPTs t/a Introductory Mathematics & Statistics for Business 4e by John S. Croucher 1 n Learning Objectives –Identify.
Math 10 Chapter 6 Notes: The Normal Distribution Notation: X is a continuous random variable X ~ N( ,  ) Parameters:  is the mean and  is the standard.
7.4 Use Normal Distributions HW Quiz: August Quiz: August 20.
Chapter 6.1 Normal Distributions. Distributions Normal Distribution A normal distribution is a continuous, bell-shaped distribution of a variable. Normal.
Normal Distributions.  Symmetric Distribution ◦ Any normal distribution is symmetric Negatively Skewed (Left-skewed) distribution When a majority of.
Chapter 7 Lesson 7.6 Random Variables and Probability Distributions 7.6: Normal Distributions.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 6 Probability Distributions Section 6.2 Probabilities for Bell-Shaped Distributions.
Using the Empirical Rule. Normal Distributions These are special density curves. They have the same overall shape  Symmetric  Single-Peaked  Bell-Shaped.
Lecture 11 Dustin Lueker. 2  The larger the sample size, the smaller the sampling variability  Increasing the sample size to 25… 10 samples of size.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Z Score The z value or z score tells the number of standard deviations the original measurement is from the mean. The z value is in standard units.
Continuous Random Variables Continuous random variables can assume the infinitely many values corresponding to real numbers. Examples: lengths, masses.
The Standard Normal Distribution Section 5.2. The Standard Score The standard score, or z-score, represents the number of standard deviations a random.
MATB344 Applied Statistics Chapter 6 The Normal Probability Distribution.
Lecture 8 Dustin Lueker.  Can not list all possible values with probabilities ◦ Probabilities are assigned to intervals of numbers  Probability of an.
7.4 Normal Distributions Part II p GUIDED PRACTICE From Yesterday’s notes A normal distribution has mean and standard deviation σ. Find the indicated.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
LECTURE 21 THURSDAY, 5 November STA 291 Fall
INTRODUCTORY MATHEMATICAL ANALYSIS For Business, Economics, and the Life and Social Sciences  2011 Pearson Education, Inc. Chapter 16 Continuous Random.
Introduction to Probability and Statistics Thirteenth Edition Chapter 6 The Normal Probability Distribution.
Normal Distribution Practice with z-scores. Probabilities are depicted by areas under the curve Total area under the curve is 1 Only have a probability.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 6-1 The Normal Distribution.
7.4 Use Normal Distributions p Normal Distribution A bell-shaped curve is called a normal curve. It is symmetric about the mean. The percentage.
© 2010 Pearson Prentice Hall. All rights reserved Chapter The Normal Probability Distribution © 2010 Pearson Prentice Hall. All rights reserved 3 7.
EXAMPLE 3 Use a z-score and the standard normal table Scientists conducted aerial surveys of a seal sanctuary and recorded the number x of seals they observed.
The Abnormal Distribution
Normal Distributions (aka Bell Curves, Gaussians) Spring 2010.
STA Lecture 151 STA 291 Lecture 15 – Normal Distributions (Bell curve)
7.4 Use Normal Distributions p Warm-Up From Page 261 (Homework.) You must show all of your work for credit 1.) #9 2.) #11.
Lecture 11 Dustin Lueker. 2  The larger the sample size, the smaller the sampling variability  Increasing the sample size to 25… 10 samples of size.
Chap 6-1 Chapter 6 The Normal Distribution Statistics for Managers.
Normal Probability Distributions. Intro to Normal Distributions & the STANDARD Normal Distribution.
 A standardized value  A number of standard deviations a given value, x, is above or below the mean  z = (score (x) – mean)/s (standard deviation)
Lecture 10 Dustin Lueker.  The z-score for a value x of a random variable is the number of standard deviations that x is above μ ◦ If x is below μ, then.
Lesson Applications of the Normal Distribution.
7.4 Normal Distributions. EXAMPLE 1 Find a normal probability SOLUTION The probability that a randomly selected x -value lies between – 2σ and is.
THE NORMAL DISTRIBUTION
STA 291 Spring 2010 Lecture 8 Dustin Lueker.
MTH 161: Introduction To Statistics
Standard and non-standard
STA 291 Spring 2008 Lecture 10 Dustin Lueker.
Using the Normal Distribution
Use the graph of the given normal distribution to identify μ and σ.
STA 291 Summer 2008 Lecture 9 Dustin Lueker.
STA 291 Summer 2008 Lecture 10 Dustin Lueker.
STA 291 Spring 2008 Lecture 8 Dustin Lueker.
STA 291 Spring 2008 Lecture 9 Dustin Lueker.
Presentation transcript:

Lecture 9 Dustin Lueker

2  Perfectly symmetric and bell-shaped  Characterized by two parameters ◦ Mean = μ ◦ Standard Deviation = σ  Standard Normal ◦ μ = 0 ◦ σ = 1  Solid Line STA 291 Summer 2010 Lecture 9

3  For a normally distributed random variable, find the following ◦ P(Z>.82) = ◦ P(-.2<Z<2.18) = STA 291 Summer 2010 Lecture 9

4  For a normal distribution, how many standard deviations from the mean is the 90 th percentile? ◦ What is the value of z such that 0.90 probability is less than z?  P(Z<z) =.90 ◦ If 0.9 probability is less than z, then there is 0.4 probability between 0 and z  Because there is 0.5 probability less than 0  This is because the entire curve has an area under it of 1, thus the area under half the curve is 0.5  z=1.28  The 90 th percentile of a normal distribution is 1.28 standard deviations above the mean STA 291 Summer 2010 Lecture 9

5  We can also use the table to find z-values for given probabilities  Find the following ◦ P(Z>a) =.7224  a = ◦ P(Z<b) =.2090  b = STA 291 Summer 2010 Lecture 9

6  When values from an arbitrary normal distribution are converted to z-scores, then they have a standard normal distribution  The conversion is done by subtracting the mean μ, and then dividing by the standard deviation σ

STA 291 Summer 2010 Lecture 97  The z-score for a value x of a random variable is the number of standard deviations that x is above μ ◦ If x is below μ, then the z-score is negative  The z-score is used to compare values from different normal distributions  Calculating ◦ Need to know  x  μ  σ

STA 291 Summer 2010 Lecture 98  SAT Scores ◦ μ=500 ◦ σ=100  SAT score 700 has a z-score of z=2  Probability that a score is above 700 is the tail probability of z=2  Table 3 provides a probability of between mean=500 and 700  z=2  Right-tail probability for a score of 700 equals =  2.28% of the SAT scores are above 700 ◦ Now find the probability of having a score below 450

STA 291 Summer 2010 Lecture 99  The z-score is used to compare values from different normal distributions ◦ SAT  μ=500  σ=100 ◦ ACT  μ=18  σ=6 ◦ What is better, 650 on the SAT or 25 on the ACT?  Corresponding tail probabilities?  How many percent have worse SAT or ACT scores?  In other words, 650 and 25 correspond to what percentiles?

STA 291 Summer 2010 Lecture 910  The scores on the Psychomotor Development Index (PDI) are approximately normally distributed with mean 100 and standard deviation 15. An infant is selected at random. ◦ Find the probability that the infant’s PDI score is at least 100  P(X>100) ◦ Find the probability that PDI is between 97 and 103  P(97<X<103) ◦ Find the probability that PDI is less than 90  Would you be surprised to observe a value of 90?