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.