Reporting a single linear regression in apa

Reporting a single linear regression in apa

1. Reporting a Single Linear Regression in APA Format

2. Here’s the template:

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. A simple linear regression was calculated to predict [ dependent variable] based on [independent variable] . A significant regression equation was found (F(_,__)= __.___, p < .___) , with an R 2 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. Wow, that’s a lot. Let’s break it down using the following example:

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. 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. 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. Let’s begin with the first part of the template:

10. A simple linear regression was calculated to predict [dependent variable] based on [predictor variable ] .

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. 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. 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. 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. 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. Now onto the second part of the template:

17. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R 2 of .____.

18. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___) , with an R 2 of .____ .

19. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___) , with an R 2 of .____ . Here’s the output:

20. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found ( F(_,__)= __.___, p < .___) , with an R 2 of .____ . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

21. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found ( F( 1 ,__) = __.___, p < .___) , with an R 2 of .____ . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 of .____ . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 of .____ . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 of .____ . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 of .649 . Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806 a .649 .642 16.14801 ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 of .649 . Now for the next part of the template:

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 R 2 of .649. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [ unit of measure] .

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 R 2 of .649. Participants’ predicted weight is equal to -234.681 +______ (independent variable measure) [dependent variable] when [independent variable] is measured in [ unit of measure] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 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] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000

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 R 2 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] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when [independent variable] is measured in [unit of measure] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in [unit of measure] . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches . ANOVA a Model Sum of Squares df Mean Squares F Sig. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000 a Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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. 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 R 2 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. 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 R 2 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] . Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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] . Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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] . Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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] . Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

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 R 2 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 . Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B St. Error Beta (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000 Independent Variable: Height Dependent Variable: Weight

42. And there you are:

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 R 2 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.