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The fact that two variables are correlated does not always prove a cause-and-effect relationship between the variables.
Causation: variable A cause variable B
Lurking Variable: Variable C has an impact on the relationship-but it isn't incorporated into the design of the study.
Confounded variable: C is a variable that the researcher was unable to eliminate. It isn't distinguished from the other variables, but has an affect on the relationship.

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You take the amount of data points for x and look for it in the n column.. You then look at the numbers to the right of your n and if your r value is less than the number in the first column, it is not a good representative. If your r value is in between the 0.05 and 0.01 columns, your data is a good representative with 5% error. And if your r value is greater than the number in the 0.01 column, your data is a good representative with 1% error.

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Domain: everything between the x values
Interpolation: prediction inside the domain
Extrapolation: prediction outside the domain
The stronger the correlation, the more accurate the prediction will be.

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A correlation coefficient is a number (r) that describes the strength and direction of a linear relationship.
A line of best fit (or regression line), is a line that best represents the data in a scatter plot.

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Goal with using scatter-plots is to see if there is some correlation between the two variables.
Negative correlation, positive correlation, no correlation
-strong
-moderate
-weak

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Bivariate Data: the values of two different variables obtained from the same population.
1) both categorical: gender & favorite movie: two-way table
2) one categorical-one quantitative: gender & height: comparative boxplot
3) both quantitative (numerical): height & weight: scatterplot