The value depends on the slope of the line.
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One of the main reasons for doing so is to check that the assumptions of the errors being independent and identically distributed is true. If that is not the case then the simple linear regression is not an appropriate model.
The given statement is true. Reason: High multicollinearity can make it difficult to determine the individual significance of predictors in a model.
True , it would have been false only if it was mentioned no relationship . But as it mentions linear it is true.
That is not true. It is possible for a data set to have a coefficient of determination to be 0.5 and none of the points to lies on the regression line.
To take a simple case, let's suppose you have a set of pairs (x1, y1), (x2, y2), ... (xn, yn). You have obtained these by choosing the x values and then observing the corresponding y values experimentally. This set of pairs would be called a sample.For whatever reason, you assume that the y's and related to the x's by some function f(.), whose parameters are, say, a1, a2, ... . In far the most frequent case, the y's will be assumed to be a simple linear function of the x's: y = f(x) = a + bx.Since you have observed the y's experimentally they will almost always be subject to some error. Therefore, you apply some statistical method for obtaining an estimate of f(.) using the sample of pairs that you have.This estimate can be called the sample regression function. (The theoretical or 'true' function f(.) would simply be called the regression function, because it does not depend on the sample.)