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Reporting a single linear regression in apa Slide 1 Reporting a single linear regression in apa Slide 2 Reporting a single linear regression in apa Slide 3 Reporting a single linear regression in apa Slide 4 Reporting a single linear regression in apa Slide 5 Reporting a single linear regression in apa Slide 6 Reporting a single linear regression in apa Slide 7 Reporting a single linear regression in apa Slide 8 Reporting a single linear regression in apa Slide 9 Reporting a single linear regression in apa Slide 10 Reporting a single linear regression in apa Slide 11 Reporting a single linear regression in apa Slide 12 Reporting a single linear regression in apa Slide 13 Reporting a single linear regression in apa Slide 14 Reporting a single linear regression in apa Slide 15 Reporting a single linear regression in apa Slide 16 Reporting a single linear regression in apa Slide 17 Reporting a single linear regression in apa Slide 18 Reporting a single linear regression in apa Slide 19 Reporting a single linear regression in apa Slide 20 Reporting a single linear regression in apa Slide 21 Reporting a single linear regression in apa Slide 22 Reporting a single linear regression in apa Slide 23 Reporting a single linear regression in apa Slide 24 Reporting a single linear regression in apa Slide 25 Reporting a single linear regression in apa Slide 26 Reporting a single linear regression in apa Slide 27 Reporting a single linear regression in apa Slide 28 Reporting a single linear regression in apa Slide 29 Reporting a single linear regression in apa Slide 30 Reporting a single linear regression in apa Slide 31 Reporting a single linear regression in apa Slide 32 Reporting a single linear regression in apa Slide 33 Reporting a single linear regression in apa Slide 34 Reporting a single linear regression in apa Slide 35 Reporting a single linear regression in apa Slide 36 Reporting a single linear regression in apa Slide 37 Reporting a single linear regression in apa Slide 38 Reporting a single linear regression in apa Slide 39 Reporting a single linear regression in apa Slide 40 Reporting a single linear regression in apa Slide 41 Reporting a single linear regression in apa Slide 42 Reporting a single linear regression in apa Slide 43
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Reporting a single linear regression in apa

  1. 1. Reporting a Single Linear Regression in APA Format
  2. 2. Here’s the template:
  3. 3. 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.
  4. 4. A simple linear regression was calculated to predict [dependent variable] based on [independent variable] . A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
  5. 5. Wow, that’s a lot. Let’s break it down using the following example:
  6. 6. 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 predicts weight.
  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 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 predicts weight.
  9. 9. Let’s begin with the first part of the template:
  10. 10. A simple linear regression was calculated to predict [dependent variable] based on [predictor variable] .
  11. 11. A simple linear regression was calculated to predict [dependent variable] based on [predictor variable]. You have been asked to investigate the degree to which height predicts weight.
  12. 12. A simple linear regression was calculated to predict [dependent variable] based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
  13. 13. A simple linear regression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
  14. 14. A simple linear regression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate how well height predicts weight.
  15. 15. A simple linear regression was calculated to predict weight based on height. Problem: You have been asked to investigate how well height predicts weight.
  16. 16. Now onto the second part of the template:
  17. 17. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  18. 18. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  19. 19. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Here’s the output:
  20. 20. A simple linear regression was calculated to predict weight based on height. 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 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  21. 21. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1,__) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  22. 22. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  23. 23. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  24. 24. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  25. 25. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  26. 26. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Now for the next part of the template:
  27. 27. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure].
  28. 28. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 +______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  29. 29. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  30. 30. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  31. 31. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  32. 32. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  33. 33. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  34. 34. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  35. 35. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. And the next part:
  36. 36. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
  37. 37. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  38. 38. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  39. 39. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  40. 40. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  41. 41. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of height. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  42. 42. And there you are:
  43. 43. A simple linear regression was calculated to predict participant’s weight based on their height. A significant regression equation was found (F(1,14)= 25.926, p < .001), with an R2 of .649. Participants’ predicted weight is equal to -234.58 +5.43 (Height) pounds when height is measured in inches. Participants’ average weight increased 5.43 pounds for each inch of height.
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Reporting a single linear regression in apa

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