Data Analytics: Probability distribution
Learn about the basics, fundamentals and importance of probability and sampling distribution in this free online course
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A probability distribution is a statistical function that explains the possible values that a random variable can take. This free online course data analytics and probability distribution explain the different ways of assigning probability, including marginal and conditional probabilities. The course describes the various Explain solutions to problems using the laws of probability, including the laws of addition, multiplication and conditional probability.
The course explains how the Bayes’ rule of probability is used in describing the probability of an event occurring, based on prior knowledge of the conditions that might be related to the event. You will learn about the properties of empirical distributions. You will also learn about binomial distributions and their applications. The mean and variance of a discrete random variable are key concepts that are clearly explained in this free online course.
Finally, you will be taken through the importance of simple random sampling. You will learn about the differences between descriptive and inferential statistics. You will be introduced to the differentiating factors between populations and samples. This free online course will be particularly useful for you if you are interested in acquiring key knowledge on the process of making calculations under probability, using sampling distributions. Explore the world of probability distributions by starting this course today.Start Course Now
Introduction to probability
Introduction to probability - Learning Outcomes
Introduction to Probability
Introduction to Probability - Lesson Summary
Sampling and Sampling Distribution
Sampling and Sampling Distribution - Learning Outcomes
Python Demo for Distributions
Sampling and Sampling Distribution
Distribution of Sample Means, Population and Variance
Confidence Interval Estimation
Sampling and Sampling Distribution - Lesson Summary
Upon successful completion of this course, you will be able to:
- Describe a simple random sampling and explain the importance of sampling
- Discuss and apply marginal, union, joint, and conditional probabilities
- Explain solutions to problems using the laws of probability, including the laws of addition, multiplication and conditional probability
- Explain the Bayes' rule of probability
- Explain the differences between descriptive and inferential statistics
- Define the concept of a sampling distribution
- Discuss the mean and standard deviation for a sampling distribution
- Differentiate between populations and samples
- Describe how to make calculations under probability using sampling distributions
- Distinguish between a point estimate and a confidence interval estimate
- Describe the confidence interval estimate for the variance of a normal population
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