Stat lesson 5.1 probability distributions
description
Transcript of Stat lesson 5.1 probability distributions
![Page 1: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/1.jpg)
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin
Probability DistributionsChapter 6
![Page 2: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/2.jpg)
GOALS
Define the terms probability distribution and random variable.
Distinguish between discrete and continuous probability distributions.
Calculate the mean, variance, and standard deviation of a discrete probability distribution.
Describe the characteristics of and compute probabilities using the binomial probability distribution.
Describe the characteristics of and compute probabilities using the hypergeometric probability distribution.
Describe the characteristics of and compute probabilities using the Poisson
2
![Page 3: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/3.jpg)
What is a Probability Distribution?
3
Experiment: Toss a coin three times. Observe the number of heads. The possible results are: zero heads, one head, twoheads, and three heads. What is the probability distribution for the number of heads?
![Page 4: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/4.jpg)
Probability Distribution of Number of Heads Observed in 3 Tosses of a Coin
4
![Page 5: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/5.jpg)
Characteristics of a Probability Distribution
5
![Page 6: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/6.jpg)
Random Variables
Random variable - a quantity resulting from an experiment that, by chance, can assume different values.
6
![Page 7: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/7.jpg)
EXAMPLE
EXPERIMENT
Rolling a die
Monitoring daily absences per week
Tossing 2 coins and recording number or heads
RANDOM VARIABLE
Number of dots
Number of absentees per day
Number of heads
7
![Page 8: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/8.jpg)
Types of Random Variables
Discrete Random Variable can assume only certain clearly separated values. It is usually the result of counting something
Continuous Random Variable can assume an infinite number of values within a given range. It is usually the result of some type of measurement
8
![Page 9: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/9.jpg)
Discrete Random Variables - Examples
The number of students in a class.
The number of children in a family.
The number of cars entering a carwash in a hour.
Number of home mortgages approved by Coastal Federal Bank last week.
9
![Page 10: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/10.jpg)
Continuous Random Variables - Examples
The distance students travel to class.
The time it takes an executive to drive to work.
The length of an afternoon nap.
The length of time of a particular phone call.
10
![Page 11: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/11.jpg)
Features of a Discrete Distribution
The main features of a discrete probability distribution are:
The sum of the probabilities of the various outcomes is 1.00.
The probability of a particular outcome is between 0 and 1.00.
The outcomes are mutually exclusive.
11
![Page 12: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/12.jpg)
The Mean of a Probability Distribution
12
MEAN•The mean is a typical value used to represent the central location of a probability distribution.•The mean of a probability distribution is also referred to as its expected value.
![Page 13: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/13.jpg)
The Variance, and StandardDeviation of a Probability Distribution
13
Variance and Standard Deviation• Measures the amount of spread in a distribution• The computational steps are:
1. Subtract the mean from each value, and square this difference.2. Multiply each squared difference by its probability.3. Sum the resulting products to arrive at the variance.The standard deviation is found by taking the positive square root of the variance.
![Page 14: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/14.jpg)
Mean, Variance, and StandardDeviation of a Probability Distribution - Example
John Ragsdale sells new cars for Pelican Ford. John usually sells the largest number of cars on Saturday. He has developed the following probability distribution for the number of cars he expects to sell on a particular Saturday.
14
![Page 15: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/15.jpg)
Mean of a Probability Distribution - Example
15
![Page 16: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/16.jpg)
Variance and StandardDeviation of a Probability Distribution - Example
16
![Page 17: Stat lesson 5.1 probability distributions](https://reader034.fdocuments.us/reader034/viewer/2022051109/547f0b12b4af9f5a188b4588/html5/thumbnails/17.jpg)
Try This:Compute the mean, variance and sd:
Prize Amount, x Probability, p(x)
P500 0.50
P1000 0.40
P1500 0.08
P2000 0.02
17
In a card scratching lottery promo in a department store, the following are the probabilities set by the company for each prize.