Reporting a Multiple Linear Regression in APA Format

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.

Here’s the template:

DV = Dependent VariableIV = Independent Variable

DV = Dependent VariableIV = 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 .___. Participants’ predicted [DV] is equal to __.___ – __.___ (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].

Wow, that’s a lot. Let’s break it down using the following example:

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 and sex predicts weight.

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 and sex predicts weight.

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 and sex predicts weight.

&

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 and sex predicts weight.

&

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

A multiple linear regression was calculated to predict [DV] based on their [IV1] and [IV2].

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.

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.

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.

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.

Now onto the second part of the template:

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 .____.

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 .___.

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 .___.

Here’s the output:

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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 SquareAdjusted 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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:

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. Participants’ predicted [DV] is equal to __.___ + __.___ (IV2) + _.___ (IV1), where [IV2] is coded or measured as _____________, and [IV1] is coded or measured __________.

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. Participants’ 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

ResidualTotal

10342.42468.514

10410.938

21315

5171.2125.270

981.202 .000a

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B St. Error Beta

1. (Constant)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

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. Participants’ predicted weight is equal to 47.138 – 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)HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Independent Variable1: HeightIndependent Variable2: SexDependent Variable: Weight

Now for the second to last portion of the template:

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches.

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. Participants’ predicted weight is equal to 47.138 – 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].

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. Participants’ predicted weight is equal to 47.138 – 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

Finally, the last part of the template:

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s weight increased 2.101 pounds for each inch of height and males weighed 39.133 pounds more than females.

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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].

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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 Beta1. (Constant)

HeightSex

47.1382.101

-39.133

14.843.198

1.501.312

-7.67

-3.17610.588

-25.071

.007

.000

.000

And there you are:

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. Participants’ predicted weight is equal to 47.138 – 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.

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. Participants’ predicted weight is equal to 47.138 – 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.

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. Participants’ predicted weight is equal to 47.138 – 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.

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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.

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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.

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. Participants’ predicted weight is equal to 47.138 – 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Participant’s 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.