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Reporting a multiple linear regression in apa

- 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 Variable

IV = Independent Variable - 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 .___. 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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Adjusted

Std. Error of

Model

R

R Square

R Square

the Estimate

1

.997a

.993

.992

2.29571

ANOVAa

Model

Sum of Squares

df

Mean Squares

F

Sig.

1. Regression

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

10342.424

2

5171.212

981.202

.000a

Residual

68.514

13

5.270

Total

10410.938

15

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 __.___ + __.___ (IV1) +

_.___ (IV2), where [IV1] is coded or measured as _____________,

and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 + __.___ (IV1) +

_.___ (IV2), where [IV1] is coded or measured as _____________,

and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 (IV1) +

_.___ (IV1), where [IV1] is coded or measured as _____________,

and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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) +

_.___ (IV1), where [IV1] is coded or measured as _____________,

and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 (IV1), where [IV1] is coded or measured as _____________,

and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 [IV1] is coded or measured as

_____________, and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 or measured as

_____________, and [IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

[IV2] is coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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 coded or measured __________.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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.

Independent Variable : Height

1

Independent Variable : Sex

2

Dependent Variable: Weight

Coefficientsa

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.000 - 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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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

Unstandardized

Standardized

Coefficients

Coefficients

Model

t

Sig.

B

St. Error

Beta

1. (Constant)

47.138

14.843

-3.176

.007

Height

2.101

.198

.312

10.588

.000

Sex

-39.133

1.501

-7.67

-25.071

.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.