The normal probability distribution

After studying this chapter you will be able to: Introduction to statistics, methods for describing data, probability, discrete probability distributions, the normal probability distribution, confidence interval, hypothesis testing.

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Lecture "Probability & statistics - Chapter 6: Continuous probability" has contents: Continuous random variable, density function, parameter, uniform distribution, normal distribution, cutoff point. Invite you to consult the content.

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Thesis with the aim of focusing on two main issues. The first is time series modeling by states in which each state is a deterministic probability distribution (normal distribution). Based on the experimental results to assess the suitability of the model. Second, combine Markov chains and fuzzy time series into new models to improve forecast accuracy. Expand the model with high-level Markov chains to be compatible with seasonal data.

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Chapter 6 - Continuous random variables. After mastering the material in this chapter, you will be able to: Define a continuous probability distribution and explain how it is used, use the uniform distribution to compute probabilities, describe the properties of the normal distribution and use a cumulative normal table, use the normal distribution to compute probabilities,...

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