Summary
This video introduces the concept of finding expected values and standard deviation using a discrete probability distribution example of electronic devices per household. It demonstrates how to calculate the expected value by determining the weighted mean of devices and their probabilities. The demonstration includes utilizing Numpy in Google Colab to import data values, calculate the weighted mean, and deriving the expected value. Furthermore, it explains finding the standard deviation by utilizing the variance formula in Numpy and obtaining the standard deviation by taking the square root of the result. This video provides a clear and practical guide for understanding these fundamental statistical concepts in probability distributions.
Introduction to Expected Values in Statistics
Introduction to finding expected values in the standard deviation of a discrete probability distribution using an example of electronic devices per household in a neighborhood.
Calculating Expected Values
Demonstration on how to calculate expected values by listing the number of devices and their corresponding probabilities, then finding the weighted mean to determine the expected value.
Using Numpy for Weighted Mean
Utilizing Numpy in Google Colab to import data values, calculate the weighted mean using numpy average function, and deriving the expected value for the given scenario.
Finding Standard Deviation
Explanation on finding the standard deviation by utilizing the formula of variance in numpy and then taking the square root of the result to obtain the standard deviation.
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