Lesson 7 - 3 Applications of the Normal Distribution.

Slides:



Advertisements
Similar presentations
The Standard Normal Distribution
Advertisements

Modular 12 Ch 7.2 Part II to 7.3. Ch 7.2 Part II Applications of the Normal Distribution Objective B : Finding the Z-score for a given probability Objective.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter The Normal Probability Distribution 7.
How do I use normal distributions in finding probabilities?
Continuous Random Variables & The Normal Probability Distribution
Chapter 6 The Normal Distribution
Chapter Five Continuous Random Variables McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Continuous Probability Distributions In this chapter, we’ll be looking at continuous probability distributions. A density curve (or probability distribution.
1. Normal Curve 2. Normally Distributed Outcomes 3. Properties of Normal Curve 4. Standard Normal Curve 5. The Normal Distribution 6. Percentile 7. Probability.
Lesson Normal Distributions.
Ch5 Continuous Random Variables
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.
5-Minute Check on Lesson 2-2a Click the mouse button or press the Space Bar to display the answers. 1.What is the mean and standard deviation of Z? Given.
Review A random variable where X can take on a range of values, not just particular ones. Examples: Heights Distance a golfer hits the ball with their.
Applications of the Normal Distribution
7.3 APPLICATIONS OF THE NORMAL DISTRIBUTION. PROBABILITIES We want to calculate probabilities and values for general normal probability distributions.
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.
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.
Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.
Lesson 2 - R Review of Chapter 2 Describing Location in a Distribution.
Chapter 7 Lesson 7.6 Random Variables and Probability Distributions 7.6: Normal Distributions.
7.2 The Standard Normal Distribution. Standard Normal The standard normal curve is the one with mean μ = 0 and standard deviation σ = 1 We have related.
Chapter 7 Lesson 7.3 Random Variables and Probability Distributions 7.3 Probability Distributions for Continuous Random Variables.
Chapter 3b (Normal Curves) When is a data point ( raw score) considered unusual?
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 7 Section 2 – Slide 1 of 32 Chapter 7 Section 2 The Standard Normal Distribution.
The Standard Normal Distribution Section 5.2. The Standard Score The standard score, or z-score, represents the number of standard deviations a random.
Copyright © 2014, 2013, 2010 and 2007 Pearson Education, Inc. Chapter The Normal Probability Distribution 7.
Lecture 8 Dustin Lueker.  Can not list all possible values with probabilities ◦ Probabilities are assigned to intervals of numbers  Probability of an.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
INTRODUCTORY MATHEMATICAL ANALYSIS For Business, Economics, and the Life and Social Sciences  2011 Pearson Education, Inc. Chapter 16 Continuous Random.
7.2 The Standard Normal Distribution. Standard Normal The standard normal curve is the one with mean μ = 0 and standard deviation σ = 1 We have related.
Chapter 5 Review. Find the area of the indicated region under the standard normal curve. Use the table and show your work. Find the areas to the left.
Chapter 7 The Normal Probability Distribution 7.3 Applications of the Normal Distribution.
AP Statistics: Section 2.2 B. Recall finding a z-score in section 2.1: z =
Lecture 9 Dustin Lueker. 2  Perfectly symmetric and bell-shaped  Characterized by two parameters ◦ Mean = μ ◦ Standard Deviation = σ  Standard Normal.
Chapter 5 Normal Probability Distributions. Chapter 5 Normal Probability Distributions Section 5-3 – Normal Distributions: Finding Values A.We have learned.
Section 5.2: PROBABILITY AND THE NORMAL DISTRIBUTION.
Lesson 2 - R Review of Chapter 2 Describing Location in a Distribution.
Chapter 7 The Normal Probability Distribution 7.1 Properties of the Normal Distribution.
Lesson The Normal Approximation to the Binomial Probability Distribution.
Normal Probability Distributions Chapter 5. § 5.2 Normal Distributions: Finding Probabilities.
Math 3Warm Up4/23/12 Find the probability mean and standard deviation for the following data. 2, 4, 5, 6, 5, 5, 5, 2, 2, 4, 4, 3, 3, 1, 2, 2, 3, 4, 6,
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
Review Continuous Random Variables –Density Curves Uniform Distributions Normal Distributions –Probabilities correspond to areas under the curve. –the.
Chapter 7 The Normal Probability Distribution
CHAPTER 2 Modeling Distributions of Data
CHAPTER 2 Modeling Distributions of Data
CHAPTER 2 Modeling Distributions of Data
Chapter 3: Normal R.V.
Continuous Random Variables
Finding Probabilities
CHAPTER 2 Modeling Distributions of Data
Standard Normal Calculations
The Normal Probability Distribution
Click the mouse button or press the Space Bar to display the answers.
CHAPTER 2 Modeling Distributions of Data
Use the graph of the given normal distribution to identify μ and σ.
CHAPTER 2 Modeling Distributions of Data
Calculating probabilities for a normal distribution
CHAPTER 2 Modeling Distributions of Data
Vital Statistics Probability and Statistics for Economics and Business
CHAPTER 2 Modeling Distributions of Data
Continuous Random Variables
Chapter 6 Continuous Probability Distributions
CHAPTER 2 Modeling Distributions of Data
MBA 510 Lecture 4 Spring 2013 Dr. Tonya Balan 10/30/2019.
CHAPTER 2 Modeling Distributions of Data
Presentation transcript:

Lesson Applications of the Normal Distribution

Quiz Homework Problem: Chapter 7-1 Suppose the reaction time X (in minutes) of a certain chemical process follows a uniform probability distribution with 5 ≤ X ≤ 10. a) draw a graph of the density curve b) P(6 ≤ X ≤ 8) = c) P(5 ≤ X ≤ 8) = d) P(X < 6) = Reading questions: –To find the value of a normal random variable, we use what formula? And which calculator function? –If we use our calculator, do we have to convert to standard normal form? If we use the tables?

Objectives Find and interpret the area under a normal curve Find the value of a normal random variable

Vocabulary None new

Finding the Area under any Normal Curve Draw a normal curve and shade the desired area Convert the values of X to Z-scores using Z = (X – μ) / σ Draw a standard normal curve and shade the area desired Find the area under the standard normal curve. This area is equal to the area under the normal curve drawn in Step 1 Using your calculator, normcdf(-E99,x,μ,σ)

Given Probability Find the Associated Random Variable Value Procedure for Finding the Value of a Normal Random Variable Corresponding to a Specified Proportion, Probability or Percentile Draw a normal curve and shade the area corresponding to the proportion, probability or percentile Use Table IV to find the Z-score that corresponds to the shaded area Obtain the normal value from the fact that X = μ + Zσ Using your calculator, invnorm(p(x),μ,σ)

Example 1 For a general random variable X with  μ = 3  σ = 2 a. Calculate Z b. Calculate P(X < 6) so P(X < 6) = P(Z < 1.5) = Normcdf(-E99,6,3,2) or Normcdf(-E99,1.5) Z = (6-3)/2 = 1.5

Example 2 For a general random variable X with μ = -2 σ = 4 a.Calculate Z b.Calculate P(X > -3) Z = [-3 – (-2) ]/ 4 = P(X > -3) = P(Z > -0.25) = Normcdf(-3,E99,-2,4)

Example 3 For a general random variable X with –μ = 6 –σ = 4 calculate P(4 < X < 11) P(4 < X < 11) = P(– 0.5 < Z < 1.25) = Converting to z is a waste of time for these Normcdf(4,11,6,4)

Example 4 For a general random variable X with –μ = 3 –σ = 2 find the value x such that P(X < x) = 0.3 x = μ + Zσ Using the tables: 0.3 = P(Z < z) so z = x = 3 + 2(-0.525) so x = 1.95 invNorm(0.3,3,2) =

Example 5 For a general random variable X with –μ = –2 –σ = 4 find the value x such that P(X > x) = 0.2 x = μ + Zσ Using the tables: P(Z>z) = 0.2 so P(Z<z) = 0.8 z = x = (0.842) so x = invNorm(1-0.2,-2,4) =

Example 6 For random variable X with μ = 6 σ = 4 Find the values that contain 90% of the data around μ x = μ + Zσ Using the tables: we know that z.05 = x = 6 + 4(1.645) so x = x = 6 + 4(-1.645) so x = P(–0.58 < X < 12.58) = 0.90 a b invNorm(0.05,6,4) = invNorm(0.95,6,4) =

Summary and Homework Summary –We can perform calculations for general normal probability distributions based on calculations for the standard normal probability distribution –For tables, and for interpretation, converting values to Z-scores can be used –For technology, often the parameters of the general normal probability distribution can be entered directly into a routine Homework –pg 390 – 392; 4, 6, 9, 11, 15, 19-20, 30