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Reporting a multiple linear regression in apa
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# Reporting a single linear regression in apa

Reporting a single linear regression in apa

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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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