# Lecture´s Description

**Independence**In this lecture

**´´**

**Probability and Distributions**

**´´**are explained. Section one is about

**´´Independence´´.**Initially, educator gives information about

**independence: data and variables.**Then explains

**independent and dependent variables**through

**graphs**and

**examples**. After that elaborates

**independence: data and variables**and clears the concepts using different

**examples**. In the end,

**independence matters**are discussed.

**Probability**Section two is about

**‘’Probability’’**. At first educator gives an overview of

**probability**. After that

**rules of probability**are discussed. Then

**interpretation of properties of probability**is given. This is followed by

**examples of probability using coin tossing**. Moreover,

**probability distributions**come under consideration along with

**one coin example**and

**two coin example**. At last

**example for probability distributions**is pursued.

**Binomial and Poisson Distribution**Section three is about

**‘’Binomial and Poisson Distribution’’**. Educator begins by explaining

**binomial distribution: formula.**Then illustrates binomial distribution graphically. Following this, talks about

**Poisson distribution**. After that

**Poisson distribution with different means**is elaborated. Likewise,

**exemption from Poisson distribution**is focused. At last, information about

**Poisson distribution: mean and variance**is conveyed.

**Continuous and Normal Distribution**Section four is about

**‘’Continuous and Normal Distribution’’.**Educator's first theme of discussion here is

**continuous probability distributions**. After that information is delivered about

**interpreting continuous probability distribution**. Then

**normal distribution**is elucidated. Moreover, educator sheds lights on

**converting to standard normal distribution**. Later on,

**normal distribution: calculating probabilities**is thoroughly discussed. Lastly,

**normal distribution: percentage points**is highlighted.

**Central Limit Theorem and Conditional Probability**Section five is about

**‘’Central Limit Theorem and Conditional Probability’’.**Educator primarily focus on

**central limit theorem**. Then tells the

**advantages of using normal distribution**. Likewise,

**consequences of central limit theorem**are elaborated encompassing

**binomial distribution**and

**Poisson distribution**. Next subject of elucidation is

**conditional probability**. In the end of this section,

**Bayes’ theorem**is pursued.