BBA182 Applied Statistics Week 7 (1)Discrete random variables – expected variance and standard deviation Discrete Probability Distributions

Cumulative Probability Function, F(x_0) Practical application

Cumulative Probability Function, F(x0) Practical application: Car dealer

Cumulative Probability Function, F(x0) Practical application

Cumulative Probability Function, F(x0) Practical application

Properties of discrete random variables: Expected value

Expected value for a discrete random variable Exercise

Expected value for a discrete random variable

Expected variance of a Discrete Random Variables

Variance of a discrete random variable

Variance and Standard Deviation

Exercise: n n Expected value,E[X], and variance, σ, of car sales

Calculation of variance of discrete random variable. Car sales – example

Class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Quizz

Khan Academy – Empirical Rule

Stating that two events are statistically independent means that the probability of one event occurring is independent of the probability of the other event having occurred.

The time it takes a car to drive from Istanbul to Sinop is an example of a discrete random variable

Probability is a numerical measure about the likelihood that an event will occur.

Suppose that you enter a lottery by obtaining one of 20 tickets that have been distributed. By using the relative frequency method, you can determine that the probability of your winning the lottery is 0.15.

If we flip a coin three times, the probability of getting three heads is 0.125.

The number of products bought at a local store is an example of a discrete random variable.

Empirical rule – Khan Academy

Binomial Probability Distribution Bi-nominal (from Latin) means: Two-names

Possible Binomial Distribution examples

The Binomial Distribution

The Binomial Distribution

Example: Calculating a Binomial Probability

Binomial probability - Calculating binomial probabilities

Solving Problems with the Binomial Formula

Class exerise

P( X = 3) ?

Creating a probability distribution with the Binomial Formula – house sale example

Binomial Probability Distribution house sale example n = 5, P= .4

The binomial distribution is used to find the probability of a specific or cumulative number of successes in n trials. Let’s look at the cumulative probability: P (x < 2 houses), P(x ≥ 3)

The binomial distribution is used to find the probability of a specific or cumulative number of successes in n trials. Let’s look at the cumulative probability: P (x < 2 houses), P(x ≥ 3)

Shape of Binomial Distribution

Binomial Distribution shapes

Using Binomial Tables instead of to calculating Binomial probabilites

Solving Problems with Binomial Tables

Solving Problems with Binomial Tables

BBA182 Applied Statistics Week 7 (1)Discrete random variables – expected variance and standard deviation Discrete Probability Distributions

Cumulative Probability Function, F(x_0) Practical application

Cumulative Probability Function, F(x0) Practical application: Car dealer

Cumulative Probability Function, F(x0) Practical application

Cumulative Probability Function, F(x0) Practical application

Properties of discrete random variables: Expected value

Expected value for a discrete random variable Exercise

Expected value for a discrete random variable

Expected variance of a Discrete Random Variables

Variance of a discrete random variable

Variance and Standard Deviation

Exercise: n n Expected value,E[X], and variance, σ, of car sales

Calculation of variance of discrete random variable. Car sales – example

Class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Dan’s computer Works – class exercise

Quizz

Khan Academy – Empirical Rule

Stating that two events are statistically independent means that the probability of one event occurring is independent of the probability of the other event having occurred.

The time it takes a car to drive from Istanbul to Sinop is an example of a discrete random variable

Probability is a numerical measure about the likelihood that an event will occur.

Suppose that you enter a lottery by obtaining one of 20 tickets that have been distributed. By using the relative frequency method, you can determine that the probability of your winning the lottery is 0.15.

If we flip a coin three times, the probability of getting three heads is 0.125.

The number of products bought at a local store is an example of a discrete random variable.

Empirical rule – Khan Academy

Binomial Probability Distribution Bi-nominal (from Latin) means: Two-names

Possible Binomial Distribution examples

The Binomial Distribution

The Binomial Distribution

Example: Calculating a Binomial Probability

Binomial probability - Calculating binomial probabilities

Solving Problems with the Binomial Formula

Class exerise

P( X = 3) ?

Creating a probability distribution with the Binomial Formula – house sale example

Binomial Probability Distribution house sale example n = 5, P= .4

The binomial distribution is used to find the probability of a specific or cumulative number of successes in n trials. Let’s look at the cumulative probability: P (x < 2 houses), P(x ≥ 3)

The binomial distribution is used to find the probability of a specific or cumulative number of successes in n trials. Let’s look at the cumulative probability: P (x < 2 houses), P(x ≥ 3)

Shape of Binomial Distribution

Binomial Distribution shapes

Using Binomial Tables instead of to calculating Binomial probabilites

Solving Problems with Binomial Tables

Solving Problems with Binomial Tables

2.01M

Category: $mathematics$ mathematics

Discrete random variables – expected variance and standard deviation. Discrete Probability Distributions. Week 7 (1)

1. BBA182 Applied Statistics Week 7 (1)Discrete random variables – expected variance and standard deviation Discrete Probability Distributions

DR SUSANNE HANSEN SARAL
EMAIL: [email protected]
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WWW.KHANACADEMY.ORG
DR SUSANNE HANSEN SARAL
1

2. Cumulative Probability Function, F(x_0) Practical application

Cumulative Probability Function, F(

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