Question

11. If “correlation does not imply causation,” what does it imply?

12. What are some of the possible reasons for large correlations between a pair of variables, X and Y?

17.What assumptions are required for a correlation to be a valid description of the relation between X and Y?

Answer 1

CORRELATION::- Correlation is a statistical technique that can show whether and how strongly pairs of variables are related.

CASUATION::-The term “causality” has a nice intuitive definition, but has eluded being well-defined for decades. Consider your commute to work. We have an intuitive understanding that traffic will cause you to be late for work. We also know that if your alarm doesn’t go off, it will cause you to be late to work.

This implies in an informational collection where you measure days on which there is activity, and whether your alert goes off on those days you'll discover a relationship between's the two. We know there's no causal impact of your alert going off on regardless of whether there's movement (accepting you drive like a normal individual when you're late), or the other way around. This is the pith of "relationship does not suggest causation". At the point when there is a typical reason between two factors, at that point they will be corresponded. This is a piece of the thinking behind the less-known expression, "There is no relationship without causation"[1]. In the event that neither A nor B causes the other, and the two are associated, there must be some normal reason for the two

12)

Correlation is a proportion of the quality of the straight connection between two factors. Quality alludes to how straight the relationship is, not to the slant of the relationship. Straight implies that connection says nothing in regards to conceivable nonlinear connections; specifically, autonomous irregular factors are uncorrelated.

The correlation of X and Y is the normalized covariance: Corr(X,Y) = Cov(X,Y) / σ_Xσ_Y .

17)

Level of estimation alludes to every factor. For a Pearson connection, every factor ought to be nonstop. On the off chance that either of the factors are ordinal in estimation, at that point a Spearman connection could be directed.

Related sets alludes to the sets of factors. Every member or perception ought to have a couple of qualities. So if the connection was somewhere in the range of weight and stature, at that point every perception utilized ought to have both a weight and a tallness esteem.

Linearity and homoscedasticity refer to the shape of the values formed by the scatterplot. For linearity, a “straight line” relationship between the variable should be formed. If a line were to be drawn between all the dots going from left to right, the line should be straight and not curved. Homoscedasticity refers to the distance between the points to that straight line. The shape of the scatterplot should be tube-like in shape. If the shape is cone-like, then homoskedasticity would not be met.