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Reporting a multiple linear regression in apa

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Reporting a multiple linear regression in apa

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Reporting a multiple linear regression in apa

  1. 1. Reporting a Multiple Linear Regression in APA Format
  2. 2. Note – the examples in this presentation come from, Cronk, B. C. (2012). How to Use SPSS Statistics: A Step-by-step Guide to Analysis and Interpretation. Pyrczak Pub.
  3. 3. Here’s the template:
  4. 4. DV = Dependent Variable IV = Independent Variable
  5. 5. DV = Dependent Variable IV = Independent Variable A multiple linear regression was calculated to predict [DV] based on [IV1] and [IV2]. A significant regression equation was found (F(_,__) = ___.___, p < .___), with an R2 of .___. Participants’ predicted [DV] is equal to __.___ – __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured as __________. Object of measurement increased _.__ [DV unit of measure] for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure]. Both [IV1] and [IV2] were significant predictors of [DV].
  6. 6. Wow, that’s a lot. Let’s break it down using the following example:
  7. 7. Wow, that’s a lot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight.
  8. 8. Wow, that’s a lot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight.
  9. 9. Wow, that’s a lot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight. &
  10. 10. Wow, that’s a lot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight. &
  11. 11. Let’s begin with the first part of the template:
  12. 12. A multiple linear regression was calculated to predict [DV] based on their [IV1] and [IV2].
  13. 13. A multiple linear regression was calculated to predict [DV] based on their [IV1] and [IV2]. You have been asked to investigate the degree to which height and sex predicts weight.
  14. 14. A multiple linear regression was calculated to predict weight based on their [IV1] and [IV2]. You have been asked to investigate the degree to which height and sex predicts weight.
  15. 15. A multiple linear regression was calculated to predict weight based on their height and [IV2]. You have been asked to investigate the degree to which height and sex predicts weight.
  16. 16. A multiple linear regression was calculated to predict weight based on their height and sex. You have been asked to investigate the degree to which height and sex predicts weight.
  17. 17. Now onto the second part of the template:
  18. 18. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(_,__) = __.___, p < .___), with an R2 of .____.
  19. 19. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(_,__) = ___.___, p < .___), with an R2 of .___.
  20. 20. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(_,__) = ___.___, p < .___), with an R2 of .___. Here’s the output:
  21. 21. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(_,__) = ___.___, p < .___), with an R2 of .___. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  22. 22. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2,__) = ___.___, p < .___), with an R2 of .___. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  23. 23. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = ___.___, p < .___), with an R2 of .___. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  24. 24. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .___), with an R2 of .___. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  25. 25. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .___. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  26. 26. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 2.29571 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  27. 27. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Now for the next part of the template:
  28. 28. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted [DV] is equal to __.___ + __.___ (IV2) + _.___ (IV1), where [IV2] is coded or measured as _____________, and [IV1] is coded or measured __________.
  29. 29. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted [DV] is equal to __.___ + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 10342.424 68.514 10410.938 2 13 15 5171.212 5.270 981.202 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  30. 30. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted [DV] is equal to __.___ + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  31. 31. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to __.___ + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  32. 32. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  33. 33. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (IV1) + _.___ (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  34. 34. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + _.___ (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  35. 35. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  36. 36. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  37. 37. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  38. 38. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  39. 39. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  40. 40. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  41. 41. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  42. 42. Now for the second to last portion of the template:
  43. 43. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches.
  44. 44. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Object of measurement increased _.__ [DV unit of measure] for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure].
  45. 45. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Object of measurement increased _.__ [DV unit of measure] for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  46. 46. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased _.__ [DV unit of measure] for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  47. 47. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 [DV unit of measure] for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  48. 48. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each [IV1 unit of measure] and _.__ for each [IV2 unit of measure]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  49. 49. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and _.__ for each [IV2 unit of measure]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  50. 50. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  51. 51. Finally, the last part of the template:
  52. 52. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females.
  53. 53. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both [IV1] and [IV2] were significant predictors of [DV].
  54. 54. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both [IV1] and [IV2] were significant predictors of [DV]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  55. 55. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and [IV2] were significant predictors of [DV]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  56. 56. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors of [DV]. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  57. 57. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors of [DV]. . Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  58. 58. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors of weight. . Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height Sex 47.138 2.101 -39.133 14.843 .198 1.501 .312 -7.67 -3.176 10.588 -25.071 .007 .000 .000
  59. 59. And there you are:
  60. 60. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Object of measurement increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors.
  61. 61. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Object of measurement increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors.
  62. 62. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Object of measurement increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors.
  63. 63. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors.
  64. 64. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors of weight.
  65. 65. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females. Both height and sex were significant predictors of weight.

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