Question

A correlation coefficient measures the degree of linear relationship between two variables the magnitude of the difference between two variables the estimated effect size between two variables the degree of relationship between two variables A negative correlation means that a high score on one variable is usually associated with a low score on the other A positive correlation means that a high score on one variable is usually associated with a high score on the other none of the above If the points in a scatter diagram generally tend from upper left to lower right, you would conclude that the correlation was probably negative Which of the following values for a correlation coefficient would indicate the strongest degree of relationship? a. -.69 b. -.35 C. +.03 d. +.59 If ΣZxZy = 0, r is zero If ΣZxZy= 4 and n = 8, r is equal to: a. -.50 b. 0 C. +.50 d. impossible to determine from the information provided If Y' = Ȳ at all levels of X, the correlation coefficient would probably be zero Exhibit 31 Suppose you are interested in marksmanship. You have 50 army enlistees fire a shotgun at 100 targets each, and then have the same 50 individuals fire a rifle at 100 targets each. Thus, each individual gets a shotgun score (number of hits out of 100 targets) and a rifle score (number of hits out of 100 targets). You compute the Pearson product moment correlation coefficient for these subjects and wish to test whether it is significantly different from zero. Refer to Exhibit 31. The number of degrees of freedom for this test is a. 100 99 98 50 48 Two factors that can lower the correlation coefficient are The nonlinearity of a relationship, restriction of range Sample size, restriction of range and The sample size of variable 1, the sample size of variable 2 What does a correlation of .67 between lack of sleep and illness imply? Lack of sleep and illness are highly related. Linear regression determines the best straight line through a data set is used to predict Y from X both a. and b. none of the above The error of prediction for the ith individual is given by ei= Yi - Y'i The least squares criterion for the regression line states that the best regression line produces the smallest sum of squared errors of prediction the variance of the X variable must be less than the variance of the Y variable the variance of the Y variable must be less than the variance of the X variable the best regression line has the steepest slope

          A correlation coefficient measures
the degree of linear relationship between two variables
the magnitude of the difference between two variables
the estimated effect size between two variables
the degree of relationship between two variables

A negative correlation means that
a high score on one variable is usually associated with a low
score on the other

A positive correlation means that
a high score on one variable is usually associated with a high
score on the other

none of the above

If the points in a scatter diagram generally tend from upper
left to lower right, you would conclude that the correlation was
probably
negative

Which of the following values for a correlation coefficient
would indicate the strongest degree of relationship?
a.
-.69
b.
-.35
C.
+.03
d.
+.59

If ΣZxZy = 0, r is
zero

If ΣZxZy= 4 and n = 8,
r is equal to:
a.    -.50
b.    0
C.      
+.50
d.    impossible to
determine from the information provided

If Y' = Ȳ at all levels of X, the correlation coefficient would
probably be
zero

Exhibit 31
Suppose you are
interested in marksmanship. You have 50 army enlistees fire a
shotgun at 100 targets each, and then have the same 50 individuals
fire a rifle at 100 targets each. Thus, each individual gets a
shotgun score (number of hits out of 100 targets) and a rifle score
(number of hits out of 100 targets). You compute the Pearson
product moment correlation coefficient for these subjects and wish
to test whether it is significantly different from zero.

Refer to Exhibit 31. The number of degrees of freedom for this
test is
a.    100
99
98
50
48

Two factors that can lower the correlation coefficient
are     
The nonlinearity of a relationship, restriction of range
Sample size, restriction of range

and     
The sample size of variable 1, the sample size of variable
2

What does a correlation of .67 between lack of sleep and
illness imply?
Lack of sleep and illness are highly related.

Linear regression
determines the best straight line through a data set
is used to predict Y from X
both a. and b.
none of the above

The error of prediction for the ith individual is given by
ei= Yi - Y'i

The least squares criterion for the regression line states that
the best regression line produces the smallest sum of squared
errors of prediction
the variance of the X variable must be less than the
variance of the Y variable
the variance of the Y variable must be less than the variance of
the X variable
the best regression line has the steepest slope

Added by Jesus T.

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