Question

# 1)Adding an outlier in a scatterplot will do which of the following to the correlation coefficient?...

1)Adding an outlier in a scatterplot will do which of the following to the correlation coefficient? Explain

a) Increase the correlation coefficient

b) Decrease the correlation coefficient

c) Change the correlation coefficient from positive to negative

d) Change the correlation coefficient from negative to positive

e) All options above are possible

2) When the correlation coefficient is close to +1, it indicates. Please explain

a) changes in one variable cause changes in the other, but we don't know which one causes the other to change

b) Changes in the variable on the x-axis cause changes in the variable on the y-axis

c) Changes in the variable on the y-axis cause changes in the variable on the x-axis

d) Nothing regarding the possible cause and effect relationship between the two variables

T/F: The normal model can be used to describe all distributions. Explain

T/F: An observation with a z-score of 0.5 would be considered an outlier. Explain

T/F: If all of the data in a skewed right data set are converted into z-scores, the distribution of these z-scores will be normally distributed. Explain

When a value of x, that is outside the range of values of x used to construct a regression equation, is used to estimate the value of y given that value of x (using that same regression equation), we call this a_________.

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Answering first question, as we are allowed to answer 1 question per post. Please post the other questions separately.

1.

Concept: In most circumstances an outlier deviates from the linear regresison line a lot, and therfore decreases the value of a correlation coefficient and weakens the regression relationship, but it's also possible that in some circumstances an outlier may increase a correlation value and improve regression. If the outlier is exactly on the linear regression then it increases the correlation coefficient.

Hence, e) All options above are possible is correct

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