a) Does there appear to be a positive, negative, or no relationship? How can you

Question

a) Does there appear to be a positive, negative, or no relationship? How can you tell?

b) Is there any evidence of a non-linear relationship? Why or why not, and what effect will this have on the correlation coefficient (in other words, why does this matter)?

c) Is there any evidence of issues such as heteroscedasticity or outliers? Why or why not, and what effect will they likely have if they're there?

d) Roughly, what range would you expect r2 to be in? Why? (You should explain what r2 is on at least your first answer).

e) Given all of your answers above, how accurate would you expect to be in predicting future ? scores based on X (i.e., would you expect the Standard Error of Estimation to be high, low, or moderate)? If you have said that there is no relationship, what would the Standard Error of Estimation be equal to (don't need to calculate a value here - recall that there is an upper limit to the SEoE).

Explanation / Answer

A)

If the data show an uphill pattern as you move from left to right, this indicates a positive relationship between X and Y. As the X-values increase (move right), the Y-values tend to increase

If the data show a downhill pattern as you move from left to right, this indicates a negative relationship between X and Y. As the X-values increase (move right) the Y-values tend to decrease

If the data don’t seem to resemble any kind of pattern (even a vague one), then no relationship exists between X and Y.

in the given data we find relationship between x and y is curvilinear.but there may be quadratic relationship exist.

b)

A linear relationship between X and Y exists when the pattern of X– and Y-values resembles a line, either uphill (with a positive slope) or downhill (with a negative slope).in the given scatter plot we does not find any such kind of pattern so there is no linerar relationship.at some point, the increase in anxiety may cause a person's performance to go down. We call these non-linear relationships curvilinear relationships.

the correlation coefficient is zero when there is a strong curvilinear relationship because it is a measure of a linear relationship.

If the points are close to one another and the width of the imaginary oval is small, this means that there is a strong correlation between the variables. If the points are not close to one another this means that there is a less correlation between the variable.

c)

There will not be hetroscedasticity . also not outliers present.I think there is homogenity occure.when a group is homogeneous, or possesses similar characteristics, the range of scores on either or both of the variables is restricted.

d) adjusted R^2 explains the variability . i think adjusted R square will be in the tange of 65 to 75.we see that the anexity in between range 2 to 6 perform is very effective.and anexity in 0 to 2 and 6 to 8 perform will be very less .after points become close and close.

e)

i think standard error of estimation will be moderate.because there is no strong correlation.we see that the anexity in between range 2 to 6 perform is very effective .

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