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SIMPLE AND MULTIPLE REGRESSION Chris Stiff [email_address]
LEARNING OBJECTIVES ,[object Object],[object Object],[object Object],[object Object],[object Object]
REGRESSION ,[object Object],[object Object],[object Object],[object Object]
SIMPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object],[object Object]
SIMPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object]
SIMPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object],[object Object]
LINE OF BEST FIT Amount of alcohol Stupid behaviour
LINE OF BEST FIT – POOR EXAMPLE Stupid behaviour Number of pairs of socks ?
SIMPLE REGRESSION USING SPSS ,[object Object]
SPSS OUTPUT
SPSS OUTPUT R  = correlation between amount drunk and stupid behaviour R square  = proportion of variance in outcome (behaviour) accounted for by the predictor (amount drunk) Adjusted R square  = takes into account the sample size and the number of predictor variables
THE  R 2 ,[object Object],[object Object],[object Object],[object Object],[object Object]
SPSS OUTPUT
SPSS OUTPUT Beta  = standardised regression coefficient and shows the degree to which a unit increase in the predictor variable produces a standard deviation change in the outcome variable with all other things constant
REPORTING THE RESULTS OF SIMPLE REGRESSION ,[object Object],Beta value t  value and associate  df  and  p   R  square
GENERATING  DF  AND  T ,[object Object],[object Object],[object Object],[object Object]
ASSUMPTIONS OF SIMPLE REGRESSION ,[object Object],[object Object]
SUMMARY OF SIMPLE REGRESSION ,[object Object],[object Object],[object Object]
MULTIPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object]
USES OF MULTIPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object]
MULTIPLE REGRESSION  - EXAMPLE Attendance at lectures Books read Motivation Exam  Performance  (Grade) What might predict exam performance?
MULTIPLE REGRESSION USING SPSS ,[object Object]
MULTIPLE REGRESSION: SPSS OUTPUT
MULTIPLE REGRESSION: SPSS OUTPUT
MULTIPLE REGRESSION: SPSS OUTPUT For overall model:  F(2, 42) = 12.153, p<.001
MULTIPLE REGRESSION: SPSS OUTPUT Number of books read is significant predictor b=.33, t(42) = 2.24, p<.05 Lectures attended is a significant predictor b=.36, t(42) = 2.41, p<.05
MAJOR TYPES OF MULTIPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],} } Statistical model building Theory-based model building
STANDARD MULTIPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object]
EXAMPLE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Attendance at lectures Books read Motivation Exam  Performance  (Grade)
ENTER OUTPUT
ENTER OUTPUT R square = proportion of variance in outcome accounted for by the predictor variables  Adjusted R square = takes into account the sample size and the number of predictor variables
ENTER OUTPUT
ENTER OUTPUT Beta = standardised regression coefficient and shows the degree to which the predictor variable predicts the outcome variable with all other things constant
HIERARCHICAL MULTIPLE REGRESSION ,[object Object],[object Object],[object Object]
Don’t forget  to choose the  r-square change  option from the  Statistics  menu
BLOCK ENTRY OUTPUT
BLOCK ENTRY OUTPUT NB – this will be in one long line in the output!
BLOCK ENTRY OUTPUT
BLOCK ENTRY OUTPUT
STATISTICAL MULTIPLE REGRESSION ,[object Object]
STATISTICAL MULTIPLE REGRESSION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
SUMMARY OF MODEL SELECTION TECHNIQUES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ASSUMPTIONS OF REGRESSION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OTHER IMPORTANT ISSUES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OUTCOMES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
REFERENCES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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My regression lecture mk3 (uploaded to web ct)

  • 1. SIMPLE AND MULTIPLE REGRESSION Chris Stiff [email_address]
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. LINE OF BEST FIT Amount of alcohol Stupid behaviour
  • 8. LINE OF BEST FIT – POOR EXAMPLE Stupid behaviour Number of pairs of socks ?
  • 9.
  • 10.
  • 12. SPSS OUTPUT R = correlation between amount drunk and stupid behaviour R square = proportion of variance in outcome (behaviour) accounted for by the predictor (amount drunk) Adjusted R square = takes into account the sample size and the number of predictor variables
  • 13.
  • 15. SPSS OUTPUT Beta = standardised regression coefficient and shows the degree to which a unit increase in the predictor variable produces a standard deviation change in the outcome variable with all other things constant
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22. MULTIPLE REGRESSION - EXAMPLE Attendance at lectures Books read Motivation Exam Performance (Grade) What might predict exam performance?
  • 23.
  • 24.
  • 27. MULTIPLE REGRESSION: SPSS OUTPUT For overall model: F(2, 42) = 12.153, p<.001
  • 28. MULTIPLE REGRESSION: SPSS OUTPUT Number of books read is significant predictor b=.33, t(42) = 2.24, p<.05 Lectures attended is a significant predictor b=.36, t(42) = 2.41, p<.05
  • 29.
  • 30.
  • 31.
  • 33. ENTER OUTPUT R square = proportion of variance in outcome accounted for by the predictor variables Adjusted R square = takes into account the sample size and the number of predictor variables
  • 35. ENTER OUTPUT Beta = standardised regression coefficient and shows the degree to which the predictor variable predicts the outcome variable with all other things constant
  • 36.
  • 37.
  • 38. Don’t forget to choose the r-square change option from the Statistics menu
  • 40. BLOCK ENTRY OUTPUT NB – this will be in one long line in the output!
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.