Lecture Probability & statistics

Lectures "Applied statistics for business - Chapter 4: Introduction to probability" provides students with the knowledge: Experiments, counting rules and assigning probabilities, events and their probabilities, some basic relationships of probability, conditional probability, bayes’ theorem. Invite you to refer to the disclosures.

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Lectures "Applied statistics for business - Chapter 5: Discrete probability distributions" provides students with the knowledge: Random variables, developing discrete probability distributions, expected value and variance, expected value and variance financial portfolios,... Invite you to refer to the disclosures.

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Lecture Monte carlo simulations: Application to lattice models, part I - Basics. The main contents of this chapter include all of the following: Introduction, thermodynamics and statistical mechanics, phase transition, probability theory.

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Lecture "Probability & statistics - Chapter 1: Intruction" has contents: Statistics is the fact, statistics, branches of statistics, data sources, population and sample, types of variable types of variable,... Invite you to consult the content.

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Lecture "Probability & statistics - Chapter 2: Tables & charts" has contents: Frequency, relative frequency, cumulative table; pie chart; bar chart, column chart; histogram; line chart; radar chart; rcatter plot, bubble chart.

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Lecture "Probability & statistics - Chapter 3: Numerical summary" has contents: Data measurements, locations, variability measures, shape, arithmetic mean, compare the mean, mode, quartile, range, variance & standard deviation.... Invite you to consult the content.

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Lecture "Probability & statistics - Chapter 4: Basic probability" has contents: Probability, outcome – event complement event, intersection event, union event mutually exclusive, independent, collectively exhausive, partitions bernoulli formula total probability, bayes’ theorem.

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Lecture "Probability & statistics - Chapter 5: Discrete probability" has contents: Random variable, probability distribution, expected value, variance – standard deviation, bivariate probability, binomial distribution.

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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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Lecture "Probability & statistics - Chapter 7: Sampling" has contents: Sampling, sampling distribution, point estimate, acceptance interval, random sample, sampling distribution, acceptance interval.

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Lecture "Probability & statistics - Chapter 8: Estimation" has contents: Concept of estimate, point estimate, maximum likelihood estimate, interval estimate, for mean, for proportion, for variance. Invite you to consult the content.

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Lecture "Probability & statistics - Chapter 9: Hypothesis testing" has contents: Statistical hypothesis, error types, T-test for mean, Z-test for population, chi-sq test for variance. Invite you to consult the content.

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Lecture "Probability & statistics - Chapter 10: Two populations testing" has contents: Two independent population, T- test for Means, Z - test for Proportion. Invite you to consult the content.

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Chapter 5 - Discrete random variables. After mastering the material in this chapter, you will be able to: Explain the difference between a discrete random variable and a continuous random variable, find a discrete probability distribution and compute its mean and standard deviation, use the binomial distribution to compute probabilities,...

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Chapter 4 - Probability. After completing this unit, you should be able to: Define a probability and a sample space, list the outcomes in a sample space and use the list to compute probabilities, use elementary probability rules to compute probabilities, compute conditional probabilities and assess independence,...

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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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Chapter 9 - Hypothesis testing. After mastering the material in this chapter, you will be able to: Set Up appropriate null and alternative hypotheses, describe Type I and Type II errors and their probabilities, use critical values and p-values to perform a z test about a population mean when s is known,...

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Chapter 13 - Chi-square tests. After mastering the material in this chapter, you will be able to: Test hypotheses about multinomial probabilities by using a chi-square goodness-of-fit test, perform a goodness-of-fit test for normality, decide whether two qualitative variables are independent by using a chi-square test for independence.

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Chapter 19 - Decision theory. After studying this chapter you will be able to: Make decisions under uncertainty and under risk and assess the value of perfect information, make decisions using posterior analysis and assess the value of sample information, make decisions using utility theory.

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