Chapter 8 Flashcards
Model
An equation or formula that simplifies and represents reality
Linear model
An equation of a line. To interpret this, we need to know the variables (along with their W’s) and their units.
Predicted value
The value of y hat found for a given x-value in the data. Found by substituting the x-value in a regression equation. Are values on the fitted line; the points (x, y hat) all lie exactly on the fitted line.
Residuals
Difference between data values and the values predicted by the regression model
Residual= observed value-predicted value
Least Squares
the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals
Regression to the mean
Because the correlation is always less that 1.0 in magnitude, each predicted y hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
Regression line/ Line of Best fit
particular linear equation that satisfies the least squares criterion
Slope
b1 gives value of y units per x units
Intercept
the y hat value when x is 0. Gives the starting value in y units
bo
Se
Found by sqr. root (sum of e^2)/(n-2)
R^2
the square of the correlation between X and Y
the overall measure of how successful the regression is in linearly relating y to x
