May help predict or explain changes in a response variable.
Shows the relationship between two quantitative variables measured on the same individuals. The values of one variable appear on the horizontal axis, and the values of the other variable appear on the vertical axis. Each individual in the data set appears as a point in the graph.
Two variables have a THIS when values of one variable tend to increase as the values of the other variable increase.
Two variables have a THIS when values of one variable tend to decrease as the values of the other variable increase.
There is THIS between two variables if knowing the value of one variable does not help us predict the value of the other variable.
For a linear association between two quantitative variables, THIS measures the direction and strength of the association.
A line that models how a response variable y changes as an explanatory variable x changes. THESE are expressed in the form ŷ = a + bx where ŷ (pronounced “y-hat”) is the predicted value of y for a given value of x.
The use of a regression line for prediction outside the interval of x values used to obtain the line. The further we DO THIS, the less reliable the predictions.
The difference between the actual value of y and the value of y, predicted by the regression line.
In the regression equation ŷ = a + bx — a is the THIS, the predicted value of y when x = 0.
least-squares regression line
The line that makes the sum of the squared residuals as small as possible.
A scatterplot that displays the residuals on the vertical axis and the explanatory variable on the horizontal axis.
standard deviation of the residuals s
Measures the size of a typical residual. That is, THIS measures the typical distance between the actual y values and the predicted y values.
coefficient of determination r²
Measures the percent reduction in the sum of squared residuals when using the least-squares regression line to make predictions, rather than the mean value of y. In other words, THIS measures the percent of the variability in the response variable that is accounted for by the least-squares regression line.
Points with THIS in regression have much larger or much smaller x values than the other points in the data set.
THIS in regression is a point that does not follow the pattern of the data and has a large residual.
THIS in regression is any point that, if removed, substantially changes the slope, y intercept, correlation, coefficient of determination, or standard deviation of the residuals.
In the regression equation ŷ = a + bx — b is the THIS, the amount by which the predices value of y changes when x increases by 1 unit.