Unit 2 Vocabulary
Scatterplot- used to observe and visually represent the relationship between two quantitative variables.
Association- the relationship between two variables.
Direction- if one variable increases and so does the other, the direction is positive. If one variable decreases as the other increases, the direction is negative.
Form- the general shape of a relationship between two variables.
Linear Relationship- a straight-line relationship between two variables.
Strength- the measure of how much scatter there is.
Unusual Features- unexpected observations on a scatterplot.
Outliers- a point that does not fit the overall pattern seen in the scatterplot.
Response Variable- the y-variable that you hope to predict or explain.
Explanatory Variable- the x-variable that accounts for, explains, predicts, or is otherwise responsible for the y-variable. 
Quantitative Variable- a variable that can be counted or measured.
Qualitative Variable- a variable that is not numerical and is not a measurement.
Correlation- a numerical measure of the direction and strength of a linear association.
Lurking Variable- a variable other than x or y that simultaneously affects both variables, accounting for the correlation between the two.
Linear Model- an equation of a straight-line that passes through the data.
Predicted Value- the value of the variable that is predicted based on the regression analysis.
Residual- the difference between observed data values and the corresponding data values predicted by the model.
Least Squares- the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals.
Regression Line (Line of Best Fit)- the linear equation that satisfies the least squares data.
Standard Deviation- a measure of the amount of variation in the data.
Regression to the Mean- each predicted y^ tends to be fewer standard deviations from its mean than its corresponding x was from its mean.
Variance- a measurement of how far the data is spread out.
Intercept- the y^ value when x is 0.
Correlation Coefficient (R^2)- the fraction of the variability of y accounted for by the least squares linear regression on x.
Slope- the value of y-units per x-units.
Extrapolation- an attempt to predict future data by relying on historical data.
Leverage- data points whose x-values are far from the mean of x. These points pull the line of best fit close to them and therefore, effect the slope and intercept.
Influential Point- a point that gives a very different model when omitted from the analysis.
Re-expression- used to straighten a relationship to make it easier to understand.
Ladder of Powers- a tool used to change the shape of a relationship so that it becomes more normal.

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