Chapter 11 - Regression Analysis Worksheet

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Across
  1. 2. Equal to 1 minus the Pearson's correlation squared, the proportion of variance in the dependent variable that is not explained by the independent variable.
  2. 4. Estimates of the unknown population parameters and describe the relationship between a predictor variable and the response. Also known as the regression coefficient.
  3. 10. A risk function corresponding to the expected value of the squared error loss which measures the average of the squares of the errors of an estimator (the average squared difference between the estimated values and the actual value).
  4. 11. A statistical procedure for predicting a dependent variable on the basis of several independent variables.
  5. 13. Measures the level of variance in the error term, or residuals, of a regression model. The smaller the error sum of squares, the better your model fits your data; the greater the error sum of squares, the poorer your model fits your data.
  6. 14. A techinique employed in predicting values of one variable (Y) from the knowledge of values of another variable (X).
Down
  1. 1. Equal to the Pearson's correlation squared, the proportion of variance in the dependent variable that is explained by the independent variable.
  2. 3. A statistical procedure for prediciting a dichotomous dependent variable on the basis of one or more independent variables.
  3. 5. The presence of strong intercorrelations among the predictor variables in a multiple regression that limits the ability to measure their separate impact on the dependent variable.
  4. 6. The mean squared error tells you how close a regression line is to a set of points and whether the terms in the model are significant.
  5. 7. A statistical technique used in regression analysis to determine the dispersion of data points.
  6. 8. In regression, the point where the regression line crosses the Y axis. The Y-intercept is the predicted value of Y for an X value of zero.
  7. 9. A straight line drawn through the scatter plot that represents the best possible fit for making predictions of Y from X.
  8. 12. The residual portion of a score that cannot be predicted by the independent variable. Also the distance of a point from the regression line.