Your expressions are confusing. Also you should define Y1 and Y2 and their relationship (if any).jasper90 said:I think this is right, just want to double check.
Cov(Y1+Y2, Ʃ n i=2 Yi)= Cov(Y1, Ʃ n i=2) + Cov(Y2, Ʃ n i=2 Yi) = 0 + Var(Y2) = σ^2
is this right?
Covariance is a statistical measure that describes the relationship between two variables. It indicates whether the variables tend to move in the same direction (positive covariance) or in opposite directions (negative covariance).
Checking covariance helps to understand the relationship between variables and can provide insights into how they may affect each other. It is also important in determining the strength and direction of a correlation between variables.
Covariance is calculated by multiplying the deviations of each data point from its respective mean for two variables and then dividing by the total number of data points.
A high covariance value indicates a strong relationship between two variables, with a positive value indicating that they tend to move in the same direction and a negative value indicating they tend to move in opposite directions.
No, covariance alone cannot determine causation between two variables. It only shows the relationship between them, but additional analysis and evidence are needed to establish a causal relationship.