Upgrade to remove ads
Chapter 8: Linear Regression
Terms in this set (11)
An equation or formula that simplifies and represents reality
An equation of a line. To interpret a linear model, we need to know the variables along with their W's and their units.
The value of (y hat) found for a given x-value in the data. A predicted value is found by substituting the x-value in the regression equation. The predicted values are the values on the fitted line; the poins (x,yhat) all lie exactly on the fitted line.
The difference between data values and the corresponding values predicted by the regression model- or, more generally, values predicted by any model
Residual = observed value - predicted value
Specifies the unique line that minimizes the variance of the residuals, or, equaivalently, the sum of the squared residuals.
Regression to the Mean
Because the correlation is always les than 1.0 in magnitude, each predicted y-hat tends to be fewer standard deviatiions from its mean than its corersponding x was from its mean.
Line of Best Fit
The particular linear equation
y-hat = b-not + b1x
that satisfies the least squares criterion. Casually, we often just call it the regression line, of the line of best fit.
The slope, gives a value in y-units per x-unit. Changes of one unit in x are associated with the changes of b1 units in predicted values of y.
The intercept, b0, gives a starting value in y-units. Its the y(hat)- value when x is 0.
The standard deviation of the residuals. When the assumption and conditions are met, the residuals can be well described by using the standard deviation and the 68-95-99.7 Rule
The square of the correlation between y and x.
Gives the fraction of the variability of y accounted for by the least squares linear regression on x.
An overall measure of how successful the regression is in linearly relating y to x
YOU MIGHT ALSO LIKE...
Chapter 7: Linear Regression
AP Stats Vocab (7,8,9,10)
Unit 2 vocab
OTHER SETS BY THIS CREATOR
Chapter 15: Probability Rules!
Chapter 7: A Tale of Two Variables -- Scatterplots…
Chapter 6: The Standard Deviation as a R…
Ch 5: Stories Quantitative Data Tell