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The strength of a correlation is independent of whether the correlation of a scatterplot is positive or negative. A scatterplot with positive correlation can have either weak correlation or strong correlation.

The slope of a line is independent of whether the correlation of a scatterplot has positive or negative correlation. A scatterplot with a negative slope can have either positive correlation or negative correlation.

The nonlinear association of a scatterplot depends on whether it has a weak or strong correlation. A scatterplot with nonlinear association will have a strong correlation.

The nonlinear association of a scatterplot depends on whether it has a positive or negative correlation. A scatterplot with nonlinear correlation will have a negative correlation.

It's a, because if it is positive, it can either be one skinny line, or one thick fat confusing line. Positive isn't perfect.

The statement first "The strength of a correlation is independent of whether the correlation of a scatterplot is positive or negative is true.

What is correlation?

It is defined as the relation between two variables which is a quantitative type and gives an idea about the direction of these two variables.

The correlation between the two variables, the explanatory variable and response variable can have either a negative correlation or a positive correlation depending on the slope of the regression line.

When the dots are along the regression line we can say the correlation is strong.

If the dots are not along the regression line the correlation may have a weak correlation.

Thus, the statement first "The strength of a correlation is independent of whether the correlation of a scatterplot is positive or negative. A scatterplot with a positive correlation can have either a weak correlation or a strong correlation" is true.

Learn more about the correlation here:

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