Reporting a single linear regression in apa

Ken Plummer
Ken PlummerFaculty Developer and Decision-Based Learning Creator em Brigham Young University
••
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

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Reporting a single linear regression in apa

Ken Plummer
Ken PlummerFaculty Developer and Decision-Based Learning Creator em Brigham Young University

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Reporting a single linear regression in apa

  • 1. Reporting a Single Linear Regression in APA Format
  • 3. 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.
  • 4. 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].
  • 5. Wow, that’s a lot. Let’s break it down using the following example:
  • 6. 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.
  • 7. 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.
  • 8. 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.
  • 9. Let’s begin with the first part of the template:
  • 10. A simple linear regression was calculated to predict [dependent variable] based on [predictor variable] .
  • 11. 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.
  • 12. 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.
  • 13. 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.
  • 14. 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.
  • 15. A simple linear regression was calculated to predict weight based on height. Problem: You have been asked to investigate how well height predicts weight.
  • 16. Now onto the second part of the template:
  • 17. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  • 18. A simple linear regression was calculated to predict weight based on height. A significant regression equation was found (F(_,__)= __.___, p < .___), with an R2 of .____.
  • 19. 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:
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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:
  • 27. 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].
  • 28. 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
  • 29. 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
  • 30. 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
  • 31. 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
  • 32. 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
  • 33. 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
  • 34. 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
  • 35. 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:
  • 36. 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].
  • 37. 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
  • 38. 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
  • 39. 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
  • 40. 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
  • 41. 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
  • 43. 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.