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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.
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.