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EDF 5400

EDF 5400. Albert Oosterhof September 27 and October 2. Create the scatterplot for these scores, then plot the regression line.... Supplement 8. Adding the regression line to the scatterplot. A scatterplot and regression line typically involves more than five cases.

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EDF 5400

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  1. EDF 5400 Albert OosterhofSeptember 27 and October 2

  2. Create the scatterplot for these scores, then plot the regression line....Supplement 8

  3. Adding the regression line to the scatterplot...

  4. A scatterplot and regression line typically involves more than five cases...

  5. Here is the regression equation for predicting posttest scores from pretest scoresSupplement 9

  6. We will switch to z-scores to show how the slope (b) and intercept (a) are determinedSupplement 9

  7. Plot z-scores and regression equationSupplement 9

  8. Estimating Y using z-scoresSupplement 9

  9. Interpret predicted Y scores with respect to standard deviations from the mean?

  10. What would we predict Y to beif the correlation had been....... r = .50 and X was 1.0 SD above the mean? r = .50 and X was 2.0 SDs above the mean? r = .50 and X was 3.0 SDs above the mean? r = .50 and X was 1.0 SD below the mean? r = .50 and X was 3.0 SDs below the mean? r = .50 and X was 0.5 SDs above the mean? r = .50 and X was 0.0 SDs above the mean?

  11. What would we predict Y to beif the correlation had been....... r = .10 and X was 1.0 SD above the mean? r = .10 and X was 3.0 SDs above the mean? r = .10 and X was 1.0 SD below the mean? r = .10 and X was 0.0 SDs above the mean?

  12. What would we predict Y to beif the correlation had been....... r = 1.00 and X was 1.0 SD above the mean? r = 1.00 and X was 3.0 SDs above the mean? r = 1.00 and X was 1.0 SD below the mean? r = 1.00 and X was 0.0 SDs above the mean?

  13. Redundant: What would we predict Y to beif the correlation had been....... • r = .50 and X was 1.0 SD above the mean?

  14. Redundant: What would we predict Y to beif the correlation had been....... r = .50 and X was 1.0 SD above the mean? What if

  15. Redundant: What would we predict Y to beif the correlation had been....... r = .50 and X was 1.0 SD above the mean? What if r = .50 and X was 3.0 SDs above the mean? What if

  16. What would we predict Y to be if ...... r = .10 and X was 1.0 SD above the mean? r = 1.00 and X was 1.0 SD above the mean? r = 1.00 and X was 3.0 SDs above the mean? r = 0.50 and X was 1.0 SD below the mean? r = 0.50 and X was 2.0 SDs above the mean? r = 0.50 and X was at the mean?

  17. Regression towards the mean... +3 +3 …if r = +1.00 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  18. Regression towards the mean... +3 +3 …if r = +1.00 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  19. Regression towards the mean... +3 +3 …if r = +1.00 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  20. Regression towards the mean... +3 +3 …if r = +0.75 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  21. Regression towards the mean... +3 +3 …if r = +0.75 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  22. Regression towards the mean... +3 +3 …if r = +0.75 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  23. Regression towards the mean... +3 +3 …if r = +0.50 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  24. Regression towards the mean... +3 +3 …if r = +0.50 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  25. Regression towards the mean... +3 +3 …if r = +0.50 +2 +2 +1 +1 0 0 -1 -1 -2 -2 -3 -3

  26. Predicting Y when given X.....Supplement 8, page 2

  27. Predicting Y when given X.....Supplement 8, page 2

  28. Predicting Y when given X.....Supplement 8, page 2

  29. Predicting Y when given X.....Supplement 8, page 2

  30. Predicting Y when given X.....Supplement 8, page 2

  31. Predicting Y when given X.....Supplement 8, page 2

  32. Predicting Y when given X.....Supplement 8, page 2

  33. Predicting Y when given X.....Supplement 8, page 2

  34. Predicting Y when given X.....Supplement 8, page 2

  35. Predicting Y when given X.....Supplement 8, page 2

  36. Predicting Y when given X.....

  37. 4 3 2 1 Z-score: WEIGHT 0 -1 -2 -3 -3 -2 -1 0 1 2 3 Z-score: HEIGHT What we have been doing!

  38. For r = .75, sy = 4.47 and sx = 2.24, slope is adjusted from .75 to b = ?

  39. For r = .75, sy = 4.47 and sx = 2.24, slope is adjusted from .75 to b = ?

  40. If b = 1.5, a = ?Take advantage of what we know about regression…...

  41. If b = 1.5, a = ?Take advantage of what we know about regression…... r = .75 and zx = .00, predicted zy = ? r = .75 and zx = .00, predicted zy = .00 r = 1.00 and zx = .00, predicted zy = ? r = 1.00 and zx = .00, predicted zy = .00 r = .00 and zx = .00, predicted zy = ? r = .00 and zx = .00, predicted zy = .00

  42. If b = 1.5, a = ?Taking advantage of what we know about regression, and remembering that if zX = 0, predicted zY = .00Supplement 9, 2nd page – Example 1

  43. Summary of 1st ExampleSupplement 9

  44. 2nd ExampleSupplement 9

  45. Error in prediction (residual)

  46. Error in Prediction, i.e. ResidualSupplement 9

  47. Error in Prediction, i.e. ResidualSupplement 9

  48. Error in Prediction, i.e. ResidualSupplement 9

  49. Standard Error of EstimateSupplement 9

  50. Standard error of estimate… z-scores versus raw-scores

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