Because the correlation coefficient is represented by a r, this formula states that it equals the covariance between the variables divided by the product of each variable's standard deviations.
Covariance and correlation are two concepts that are diametrically opposed and are both employed in statistics and regression analysis. Covariance shows us how the two variables differ from each other, whilst correlation shows us how the two variables are related.
Both statistics and regression analysis utilise the phrases covariance and correlation, which are diametrically opposed. The covariance of two variables reveals how they differ, but the correlation shows how they are related.
In statistics and probability theory, the terms variance and covariance are widely employed. The dispersion of a data set about its mean value is known as variance, whereas the measure of the directional relationship between two random variables is known as covariance.
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1.easy to understand all topic
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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nice ☺️👍
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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Nikhil Fapale
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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