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 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. 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 R2 of .____.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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.