1) explain correlation and convaraince 2) how logistic differ from linear regression

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Correlation measures the strength and direction of a linear relationship between two variables, while covariance measures the extent to which two variables change together.

Correlation ranges from -1 to 1, where 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship.
Covariance can be positive, negative, or zero. A positive covariance indicates that as one variable increases, the other variable also tends to increase.
Logistic regression is used for binary classification problems, while linear regression is used for regression problems where the output is continuous.
Logistic regression uses a logistic function to model the probability of a binary outcome, while linear regression models the relationship between the independent and dependent variables using a linear equation.

1) explain correlation and convaraince 2) how logistic differ from linear regression

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