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Reporting a multiple linear regression in apa
1. Reporting a Multiple Linear
Regression in APA Format
Amit Sharma
Associate Professor
Dept. of Pharmacy Practice
ISF COLLEGE OF PHARMACY
Ghal Kalan, Ferozpur GT Road, MOGA, 142001, Punjab
Mobile: 09646755140, 09418783145
Phone: No. 01636-650150, 650151
Website: - www.isfcp.org
2. Note ā 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.
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 .___. Participantsā predicted [DV] is equal to
__.___ ā __.___ (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. Wow, thatās a lot. Letās break it down using the
following example:
7. Wow, thatās a lot. Letās break it down using the
following example:
You have been asked to investigate the degree to which
height and sex predicts weight.
8. Wow, thatās a lot. Letās break it down using the
following example:
You have been asked to investigate the degree to which
height and sex predicts weight.
9. Wow, thatās a lot. Letās break it down using the
following example:
You have been asked to investigate the degree to which
height and sex predicts weight.
&
10. Wow, thatās a lot. Letās break it down using the
following example:
You have been asked to investigate the degree to which
height and sex predicts weight.
&
12. A multiple linear regression was calculated to predict
[DV] based on their [IV1] and [IV2].
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. 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. 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. 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.
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. 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. 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 .___.
Hereās the output:
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
t Sig.B St. Error Beta
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. 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
t Sig.B St. Error Beta
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. 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
t Sig.B St. Error Beta
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. 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
t Sig.B St. Error Beta
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. 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
t Sig.B St. Error Beta
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. 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
t Sig.B St. Error Beta
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. 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. 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.
Participantsā predicted [DV] is equal to __.___ + __.___ (IV2) +
_.___ (IV1), where [IV2] is coded or measured as _____________,
and [IV1] is coded or measured __________.
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.
Participantsā 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
t Sig.B St. Error Beta
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. 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.
Participantsā predicted [DV] is equal to __.___ + __.___ (IV1) +
_.___ (IV2), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to __.___ + __.___ (IV1) +
_.___ (IV2), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 + __.___ (IV1) +
_.___ (IV2), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (IV1) +
_.___ (IV1), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
_.___ (IV1), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (IV1), where [IV1] is coded or measured as _____________,
and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where [IV1] is coded or measured as
_____________, and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where sex is coded or measured as
_____________, and [IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and
[IV2] is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and
height is coded or measured __________.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and
height is measured in inches.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
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.
Participantsā predicted weight is equal to 47.138 ā 39.133 (SEX) +
2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and
height is measured in inches.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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
Independent Variable1: Height
Independent Variable2: Sex
Dependent Variable: Weight
42. Now for the second to last portion of the template:
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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches.
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. Participantsā predicted weight is
equal to 47.138 ā 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. 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. Participantsā predicted weight is
equal to 47.138 ā 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās weight increased 2.101 pounds for
each [IV1 unit of measure] and _.__ for each [IV2 unit of
measure].
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās weight increased 2.101 pounds for
each inch of height and _.__ for each [IV2 unit of measure].
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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
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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās weight increased 2.101 pounds for
each inch of height and males weighed 39.133 pounds
more than females.
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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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. 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. Participantsā predicted weight is
equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT), where sex
is coded as 1 = Male, 2 = Female, and height is measured in
inches. Participantās 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
t Sig.B St. Error Beta
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
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. Participantsā predicted weight
is equal to 47.138 ā 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. 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. Participantsā predicted weight
is equal to 47.138 ā 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. 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. Participantsā predicted weight
is equal to 47.138 ā 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. 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. Participantsā predicted weight
is equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT),
where sex is coded as 1 = Male, 2 = Female, and height
is measured in inches. Participantās 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. 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. Participantsā predicted weight
is equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT),
where sex is coded as 1 = Male, 2 = Female, and height
is measured in inches. Participantās 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. 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. Participantsā predicted weight
is equal to 47.138 ā 39.133 (SEX) + 2.101 (HEIGHT),
where sex is coded as 1 = Male, 2 = Female, and height
is measured in inches. Participantās 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.