Statistics Using Python Tutorial Part 10 | Confidence Interval Statistics - Examples | Data Science

Statistics Using Python Tutorial Part 10 | Confidence Interval Statistics - Examples | Data Science
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Hello and welcome back to another session of statistics tutorial using Python Powered by Acadgild. In the previous video, you have learned the probability mass function, Probability density function, Cumulative distribution function
In this video, you will be able to learn, probability mass function, Probability density function, Cumulative distribution function. Before that, if you have missed the previous, please check the links as follows.
Before that, if you have missed the previous, please check the links as follows.
Central Tendency, Sample, and Population -    • Statistics Using Python Tutorial Part...  
Measure of Spread -    • Statistics Using Python Tutorial Part...  
Descriptive and Inferential Statistics -    • Statistics Using Python Tutorial Part...  
Types of Data in Statistics -    • Statistics Using Python Tutorial Part...  
Data Visualization Using Matplotlib -    • Statistics Using Python Part 5 | Data...  
Seaborn Python Tutorial -    • Statistics Using Python Tutorial Part...  
Types of Distribution in Statistics -    • Statistics Using Python Tutorial Part...  
Central Limit Theorem -    • Statistics Using Python Tutorial Part...  
Probability Mass Function -    • Statistics Using Python Tutorial Part...  

So, what is Confidence Interval?
It is a type of interval estimate computed from statistics observed data that may contain the true value of unknown population parameter. The confidence level represents the frequency or proportion of a possible confidence interval, that contains the true value of the unknown population parameter in another words, if the confidence intervals are constructed using a confidence level from an infinite number of independent sample statistics. The proportion of these intervals that contain the true value of parameter will be equal to the confidence level.
A confidence interval represents how much of an uncertainty would be present in a particular statistics; these are often represented using the margin of error. Confidence interval tells you, how confident you can be the results from the pole or a survey with what you would expect to find out if it were possible to survive the entire population.
Kindly, go through the complete video to learn how we can implement confidence interval in our Jupyter notebook.
#confidenceinterval , #example, #function #Statistics, #datascience

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