## The y-intercept (b0) represents the

predicted value of y once x = 0.

You are watching: The strength of the linear relationship between two numerical variables may be measured by the

estimated adjust in typical y per unit change in x.
predicted value of y.
variation approximately the line of regression.

## The steep (b1) represents:

predicted value of y as soon as x = 0.
the estimated readjust in mean y every unit change in x.
predicted worth of y.
variation around the line of regression.

## The the very least squares technique minimizes i m sorry of the following?

Sum the the absolute deviations native the line
Sum that the squared deviations indigenous the line
The variance in the it was observed y-values
All that the above

## If the sample correlation coefficient rxy is same to -1.00, then

all the data points have to fall specifically on a right line when the slope equals 1.00.
all the data points need to fall precisely on a right line with a negative slope.
all the data points should fall precisely on a straight line with a optimistic slope.
all the data points must fall specifically on a horizontal right line with a zero slope.

## If the correlation coefficient is same to 0, then

all the data points need to fall exactly on a straight line as soon as the slope equates to 1.00.
all the data points have to fall specifically on a straight line v a an unfavorable slope.
all the data points need to fall precisely on a straight line with a optimistic slope.
all the data points must be scattered in a arbitrarily pattern.

## The strength of the straight relationship in between two number variables might be measure by the

mean.
interquartile range.
coefficient of variation.
correlation coefficient.

## In a basic linear regression problem, rxy and b1

may have opposite signs.
must have actually the exact same sign.
must have opposite signs.
are equal.

## Assuming a linear relationship between X and also Y, if the coefficient the correlation (rxy) equals -0.30

there is no correlation.
the slope (b1) is negative.
variable X is bigger than variable Y.
the variance that X is negative.

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161.386
0.784
- 3.810
- 48.193

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