THE NORMAL DISTRIBUTION Lesson 2. Starter: Find P (Z<1.63) To do this we begin with a sketch of the normal distribution. We then mark a line to represent.

Slides:



Advertisements
Similar presentations
C1: Tangents and Normals
Advertisements

THE NORMAL DISTRIBUTION Lesson 1. Objectives To introduce the normal distribution The standard normal distribution Finding probabilities under the curve.
Anthony J Greene1 Computing Probabilities From the Standard Normal Distribution.
Normal distribution (3) When you don’t know the standard deviation.
Statistical Review for Chapters 3 and 4 ISE 327 Fall 2008 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including:
How do I use normal distributions in finding probabilities?
Middle on the Normal distribution. Z = =.1003 What is going on here? It is just an exercise in using.
Lesson #15 The Normal Distribution. For a truly continuous random variable, P(X = c) = 0 for any value, c. Thus, we define probabilities only on intervals.
Lesson #16 Standardizing a Normal Distribution.  X ~ N( ,  2 )   X -   Z = ~ N( ,  )
Continuous Probability Distributions In this chapter, we’ll be looking at continuous probability distributions. A density curve (or probability distribution.
Chapter 6: Some Continuous Probability Distributions:
Statistics Normal Probability Distributions Chapter 6 Example Problems.
Chapter 6: The Normal Probability Distribution This chapter is to introduce you to the concepts of normal distributions.  E.g. if a large number of students.
Using the Standard Normal Distribution to Solve SPC Problems
MATH104- Ch. 12 Statistics- part 1C Normal Distribution.
Section 6.3 Finding Probability Using the Normal Curve HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
Normal 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.
JMB Ch6 Lecture2 Review EGR 252 Spring 2011 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
Chapter 6 Normal Probability Distribution Lecture 1 Sections: 6.1 – 6.2.
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.
The Standard Normal Distribution
Table A & Its Applications - The entry in Table A - Table A’s entry is an area underneath the curve, to the left of z Table A’s entry is a proportion of.
Chapter 3b (Normal Curves) When is a data point ( raw score) considered unusual?
The Standard Normal Distribution Section 5.2. The Standard Score The standard score, or z-score, represents the number of standard deviations a random.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Standard Normal Distribution
What does a population that is normally distributed look like? X 80  = 80 and  =
1 7.5 CONTINUOUS RANDOM VARIABLES Continuous data occur when the variable of interest can take on anyone of an infinite number of values over some interval.
STT Normal Distribution (Background) 6.3 Areas Under the Normal Curve 6.4 Application of the Normal Distribution.
© 2010 Pearson Prentice Hall. All rights reserved Chapter The Normal Probability Distribution © 2010 Pearson Prentice Hall. All rights reserved 3 7.
6.4 Standard Normal Distribution Objectives: By the end of this section, I will be able to… 1) Find areas under the standard normal curve, given a Z-value.
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.
12.6 – Probability Distributions. Properties of Probability Distributions.
Lecture 9 Dustin Lueker. 2  Perfectly symmetric and bell-shaped  Characterized by two parameters ◦ Mean = μ ◦ Standard Deviation = σ  Standard Normal.
Aims: To practice sketching graphs of rational functions To practice sketching graphs of rational functions To be able to solve inequalities by sketching.
STATISTICS “The Normal Probability Distribution” 11.0 The Normal Probability Distribution.
Normal Probability Distributions. Intro to Normal Distributions & the STANDARD Normal Distribution.
Normal Probability Distributions Chapter 5. § 5.2 Normal Distributions: Finding Probabilities.
CHAPTER 5 CONTINUOUS PROBABILITY DISTRIBUTION Normal Distributions.
The Normal Distribution Name:________________________.
Lesson Applications of the Normal Distribution.
Empirical Rule 68% 95% 99.7% % RULE Empirical Rule—restated 68% of the data values fall within 1 standard deviation of the mean in either direction.
15.5 The Normal Distribution. A frequency polygon can be replaced by a smooth curve A data set that is normally distributed is called a normal curve.
7.3 Areas Under Any Normal Curve Example1: Let x have a normal probability distribution with μ = 4 and σ = 2. Find the probability that x value selected.
12.7 Normal Distributions Idleness is not doing nothing. Idleness is being free to do anything.
Chapter 7 The Normal Probability Distribution
Lesson 13.3 graphing square root functions
Finding Probability Using the Normal Curve
Chapter 3: Normal R.V.
Lesson 15-5 The Normal Distribution
5.2 Normal Distributions: Finding Probabilities
2.6 Families of Functions Learning goals
Homework Log Fri 5/27 Lesson 11 – 10 Learning Objective:
Using the Empirical Rule
Normal Distribution Standardising Scores & Reverse
9 x 14 9 x 12 Calculate the value of the following: 1 4 × 5 =
ANATOMY OF A Z-SCORE z-score
Since H is 4 units to the left of the y-axis,
NORMAL PROBABILITY DISTRIBUTIONS
Using the Normal Distribution
How do I use normal distributions in finding probabilities?
Use the graph of the given normal distribution to identify μ and σ.
STA 291 Summer 2008 Lecture 9 Dustin Lueker.
H0: m = m0 Ha: m > m0 Ha: m < m0 Ha: m  m0 “EXTREME”
Chapter 6: Some Continuous Probability Distributions:
LEARNING GOALS FOR LESSON 2.6 Stretches/Compressions
STA 291 Spring 2008 Lecture 9 Dustin Lueker.
Normal Probability Distribution Lecture 1 Sections: 6.1 – 6.2
Presentation transcript:

THE NORMAL DISTRIBUTION Lesson 2

Starter: Find P (Z<1.63) To do this we begin with a sketch of the normal distribution. We then mark a line to represent Z=1.63 P(Z<1.63) is the area under the curve to the left of a. We now use the table to look up this probability P(Z<1.63) =

Remember this from last lesson! Note this result.. P(Z>a) = 1-P(Z<a)

And we found this really useful result P(Z<-a) = 1 - P(Z<a)

Objectives Finding the value of Z from a given probability.

Points to note P(Z<a) is greater than 0.5 a > 0 P(Z<a) is less than 0.5 a <0

Points to note P(Z>a) is less than 0.5 a > 0 P(Z>a) is greater than 0.5 a < 0

Ex 1 Find the value of a such that P(Z<a)=0.7852

Use the table P(Z<a) = a = 0.79 a

Ex 2 Find the value of a such that P(Z>a)=0.01 P(Z>a) = 0.01 P(Z<a) = Look up this result is in the main table. It’s not there! Instead look at the table of Percentage Points to see if p=0.01 is listed. P(Z>a) = 0.01 a = a

Ex 3 Find the value of a such that P(Z>a)= a a P(Z>a) = P(Z a) = = So from the main table a = 1.86

Ex 4 Find the value of a such that P(Z<a) = The table only lists values for z>0 so we need to reflect the problem in the vertical axis… P(Z<z) = 1 – = From the table z = 2.06 Therefore a = p= p= a z

Further Learning: Read through Example 2 on P180 Do Ex 9B on P181