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Reporting a multiple linear regression in APA

A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.

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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
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 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
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
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
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
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
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
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 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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) 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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.

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Reporting a multiple linear regression in APA

  • 1.
    Reporting a MultipleLinear 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 – theexamples 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.
  • 3.
  • 4.
    DV = DependentVariable IV = Independent Variable
  • 5.
    DV = DependentVariable 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].
  • 6.
    Wow, that’s alot. Let’s break it down using the following example:
  • 7.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight.
  • 8.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight.
  • 9.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight. &
  • 10.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height and sex predicts weight. &
  • 11.
    Let’s begin withthe first part of the template:
  • 12.
    A multiple linearregression was calculated to predict [DV] based on their [IV1] and [IV2].
  • 13.
    A multiple linearregression 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 linearregression 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 linearregression 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 linearregression 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.
  • 17.
    Now onto thesecond part of the template:
  • 18.
    A multiple linearregression 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 linearregression 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 linearregression 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:
  • 21.
    A multiple linearregression 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 linearregression 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 linearregression 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 linearregression 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 linearregression 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 linearregression 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 linearregression 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 linearregression 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 __________.
  • 29.
    A multiple linearregression 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 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 linearregression 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) 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 thesecond to last portion of the template:
  • 43.
    A multiple linearregression 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.
  • 44.
    A multiple linearregression 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].
  • 45.
    A multiple linearregression 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 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 linearregression 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 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 linearregression 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 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 linearregression 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 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 linearregression 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 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 linearregression 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 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
  • 51.
    Finally, the lastpart of the template:
  • 52.
    A multiple linearregression 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.
  • 53.
    A multiple linearregression 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].
  • 54.
    A multiple linearregression 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 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 linearregression 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 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 linearregression 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 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 linearregression 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 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 linearregression 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 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
  • 59.
  • 60.
    A multiple linearregression 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.
  • 61.
    A multiple linearregression 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.
  • 62.
    A multiple linearregression 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.
  • 63.
    A multiple linearregression 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.
  • 64.
    A multiple linearregression 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.
  • 65.
    A multiple linearregression 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.