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Reporting a single linear regression in apa

The document provides a template for reporting the results of a simple linear regression analysis in APA format. It explains that a linear regression was conducted to predict weight based on height. The regression equation was found to be significant, F(1,14)=25.925, p<.000, with an R2 of .649. The predicted weight is equal to -234.681 + 5.434 (height in inches) pounds.

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Reporting a Single Linear Regression in APA Format
Here’s the template:
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.
A simple linear regression was calculated to predict [dependent variable] based on [independent variable] . A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
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 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 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 predicts weight.
Let’s begin with the first part of the template:
A simple linear regression was calculated to predict [dependent variable] based on [predictor variable] .
A simple linear regression was calculated to predict [dependent variable] based on [predictor variable]. You have been asked to investigate the degree to which height predicts weight.
A simple linear regression was calculated to predict [dependent variable] based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
A simple linear regression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
A simple linear regression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate how well height predicts weight.
A simple linear regression was calculated to predict weight based on height. Problem: You have been asked to investigate how well height predicts weight.
Now onto the second part of the template:
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Here’s the output:
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1,__) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Now for the next part of the template:
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure].
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 +______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. And the next part:
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of height. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
And there you are:
A simple linear regression was calculated to predict participant’s weight based on their height. A significant regression equation was found (F(1,14)= 25.926, p < .001), with an R2 of .649. Participants’ predicted weight is equal to -234.58 +5.43 (Height) pounds when height is measured in inches. Participants’ average weight increased 5.43 pounds for each inch of height.

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Reporting a single linear regression in apa

  • 1.
    Reporting a SingleLinear Regression in APA Format
  • 2.
  • 3.
    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.
  • 4.
    A simple linearregression was calculated to predict [dependent variable] based on [independent variable] . A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
  • 5.
    Wow, that’s alot. Let’s break it down using the following example:
  • 6.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height predicts weight.
  • 7.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height predicts weight.
  • 8.
    Wow, that’s alot. Let’s break it down using the following example: You have been asked to investigate the degree to which height predicts weight.
  • 9.
    Let’s begin withthe first part of the template:
  • 10.
    A simple linearregression was calculated to predict [dependent variable] based on [predictor variable] .
  • 11.
    A simple linearregression was calculated to predict [dependent variable] based on [predictor variable]. You have been asked to investigate the degree to which height predicts weight.
  • 12.
    A simple linearregression was calculated to predict [dependent variable] based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
  • 13.
    A simple linearregression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate the degree to which height predicts weight.
  • 14.
    A simple linearregression was calculated to predict weight based on [predictor variable]. Problem: You have been asked to investigate how well height predicts weight.
  • 15.
    A simple linearregression was calculated to predict weight based on height. Problem: You have been asked to investigate how well height predicts weight.
  • 16.
    Now onto thesecond part of the template:
  • 17.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  • 18.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  • 19.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Here’s the output:
  • 20.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 21.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1,__) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 22.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = __.___, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 23.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .___), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 24.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .____. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 25.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .806a .649 .642 16.14801 ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 26.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Now for the next part of the template:
  • 27.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to _______+______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure].
  • 28.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 +______ (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 29.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable measure) [dependent variable] when [independent variable] is measured in [unit of measure]. ANOVAa Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 30.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (independent variable) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 31.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) [dependent variable measure] when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 32.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when [independent variable] is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 33.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in [unit of measure]. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 34.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. ANOVAa Independent Variable: Height Dependent Variable: Weight Model Sum of Squares df Mean Squares F Sig. 1. Regression Residual Total 6760.323 3650.614 10410.938 1 14 15 6780.323 280.758 25.925 .000a Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 35.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. And the next part:
  • 36.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable].
  • 37.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. [Dependent variable] increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 38.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased _____ for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 39.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each [unit of measure] of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 40.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of [independent variable]. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 41.
    A simple linearregression was calculated to predict weight based on height. A significant regression equation was found (F(1, 14) = 25.925, p < .000), with an R2 of .649. Participants’ predicted weight is equal to -234.681 + 5.434 (height) pounds when height is measured in inches. Participant’s weight increased 5.434 for each inch of height. Independent Variable: Height Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 1. (Constant) Height -234.681 5.434 71.552 1.067 .806 -3.280 5.092 .005 .000
  • 42.
  • 43.
    A simple linearregression was calculated to predict participant’s weight based on their height. A significant regression equation was found (F(1,14)= 25.926, p < .001), with an R2 of .649. Participants’ predicted weight is equal to -234.58 +5.43 (Height) pounds when height is measured in inches. Participants’ average weight increased 5.43 pounds for each inch of height.