## The y-intercept (b0) represents the

predicted value of y once x = 0.

You are watching: If the correlation coefficient r 1.00 then

predicted worth of y.
variation around the line of regression.

## The slope (b1) represents:

predicted worth of y once x = 0.
predicted value of y.
variation about the heat of regression.

## The least squares an approach minimizes i m sorry of the following?

Sum that the pure deviations from the line
Sum of the squared deviations from the line
The variance in the it was observed y-values
All of the above

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

all the data points must fall exactly on a right line when the slope equates to 1.00.
all the data points should fall exactly on a right line with a an adverse slope.
all the data points must fall precisely on a straight line through a positive slope.
all the data points need to fall specifically on a horizontal directly line through 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 equals 1.00.
all the data points should fall precisely on a directly line with a an unfavorable slope.
all the data points should fall exactly on a right line through a confident slope.
all the data points need to be scattered in a arbitrarily pattern.

## The strength of the direct relationship between two numerical variables may be measure by the

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

## In a an easy linear regression problem, rxy and also b1

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

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

there is no correlation.
the steep (b1) is negative.
variable X is larger than change Y.
the variance the X is negative.

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161.386
0.784
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- 48.193

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