How to report hierarchical multiple regression results in apa

[Pages:6]Continue

How to report hierarchical multiple regression results in apa

And so, after a much longer wait than intended, here is part two of my post on reporting multiple regressions. In part one I went over how to report the various assumptions that you need to check your data meets to make sure a multiple regression is the right test to carry out on your data. In this part I am going to go over how to report the main findings of you analysis. Hopefully this one won't be as long as part one as this is fairly straight forward. Right, let's get on with it shall we. The first thing to do when reporting results is to describe the test you carried out and why you did it. You need to make sure you mention the various variables included in your analysis. Something like this: A multiple regression was conducted to see if intelligence level and extroversion level predicted the total value of sales made by sales persons per week. Next you want to have a look at the various descriptive statistics you have. Now to be honest it is up to you where and how you report these. They can go in a table or in text and can be mentioned before or during your main analysis. How you do it generally depends on how many variables you have. One or two, just stick it in the text, more than that and you should make a table. Now you can just report the means and standard deviation values, as seen in the table below. However, if you really want your data to be complete you will need to include the bivariate correlation values, and that means running some extra tests. Now I am not going to show you how to do that here, I may in a future post, as for now I want to focus on the main findings. I will say that if you do want to include these values then you need to run individual correlations on all your predictor variables against your dependent variable individually. Then you report the R value and the significance value for each one. Right, so once you have reported the various descriptive statistics the next thing you want to do is look and see if your results are statistically significant. When you run a multiple regression, it automatically includes an ANOVA (ANalysis Of VAriance) test in the mix. This is the first thing you want to look for. If the significance value is less than .05 then you have yourself a finding that is statistically significant. When it comes to reporting it you will want to include the F value and the relevant degrees of freedom. You need to report the degrees of freedom for both the regression and the residual error. Next you want to look and see how much of the variance in the results your analysis explains. For this you want to turn to the Model Summary table. The R Square value tells you how much of the variance in your analysis is explained by the various predictor variables. In this case it is .353, or to put it another way 35.3%. You also need to look at the Adjusted R Square value as well. This value takes into account the number of variables involved in your analysis. If you add additional variables to the analysis the R Square value will tend to increase, however it will never decrease. The Adjusted R Square value on the other hand can go down if the new variable doesn't add to the explanatory power of the model. It is now standard practice to include this value when reporting your results. So let's do that: NOTE: In part one we used the Enter method to add the variables to the analysis. You should mention this when reporting your findings. Using the enter method it was found that intelligence and extroversion level explain a significant amount of the variance in the value of sales made per week (F(2, 17) = 4.63, p < .05, R2 = .59, R2Adjusted = .28). So is that it then? I mean we know that we have significant results, surely we can call it a day? Well not quite. We know that overall our results were significant but we don't know whether one or both of our predictor variables is contributing to this result. To find out we need to look to the Coefficients table. Here we will see that SPSS has kindly carried out a couple of t tests for us, one for intelligence/IQ score and one for extroversion level. Again we look to see which, if any, of these are significant, and we see that intelligence is not but extroversion level is. You also need to look at the Beta value. This will tell you if your regression is positive or negative for this variable. No whether you say which direction the regression is heading here is up to you, I generally leave this detail until the discussion section, but either way stick that Beta value in there. Ok, so let's report this. NOTE: I believe that the degrees of freedom for the t tests in this analysis is n-1, but I am not completely sure. Make sure you check with someone who does this sort of thing for a living before handing anything in. Also you should use the symbol for Beta rather than the word if possible. The analysis shows that intelligence level did not significantly predict value of sales per week (Beta = .23, t(19) = 1.17, ns), however extroversion level did significantly predict value of sales per week (Beta = .50, t(19) = 2.53, p < .05). And that's really it. It never hurts to throw in a graph if you can but generally that is all the information you need to report for a multiple regression. I hope these posts have been helpful. Be sure to check anything I have said with your lecturers as, like I said right at the beginning, I only just worked this all out myself so you probably shouldn't be putting all your faith in me. Good luck and may all your results be significant. UPDATE 19/03/2015 ? So the degrees of freedom reported for the above t-test are wrong, thanks to Grayden for pointing that out. As such I have written another post telling you what they should be. Please go check that out and please let me know if I have made any other mistakes. 1. Reporting a Multiple Linear Regression in APA Format 2. 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. 3. Here's the template: 4. DV = Dependent Variable IV = Independent Variable 5. 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]. 6. Wow, that's a lot. Let's break it down using the following example: 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 and sex 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 and sex predicts weight. 9. 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. & 10. 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. & 11. Let's begin with the first part of the template: 12. A multiple linear regression was calculated to predict [DV] based on their [IV1] and [IV2]. 13. 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. 14. 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. 15. 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. 16. 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. 17. Now onto the second part of the template: 18. 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 .____. 19. 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 .___. 20. 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: 21. 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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: 28. 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 __________. 29. 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 B St. Error Beta t Sig. 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 linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted [DV] is equal to __.___ + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 31. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to __.___ + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 32. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + __.___ (IV1) + _.___ (IV2), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 33. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (IV1) + _.___ (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 34. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + _.___ (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 35. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (IV1), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 36. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where [IV1] is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 37. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded or measured as _____________, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 38. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and [IV2] is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 39. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is coded or measured __________. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 40. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 41. A multiple linear regression was calculated to predict weight based on their height and sex. A significant regression equation was found (F(2, 13) = 981.202, p < .000), with an R2 of .993. Participants' predicted weight is equal to 47.138 ? 39.133 (SEX) + 2.101 (HEIGHT), where sex is coded as 1 = Male, 2 = Female, and height is measured in inches. Independent Variable1: Height Independent Variable2: Sex Dependent Variable: Weight Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B St. Error Beta t Sig. 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 42. Now for the second to last portion of the template: 43. 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. 44. 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]. 45. 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 last part of the template: 52. 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. 53. 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]. 54. 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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 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 B St. Error Beta t Sig. 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. And there you are: 60. 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. 61. 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. 62. 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. 63. 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. 64. 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. 65. 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.

fenumawofar.pdf 6639956664.pdf rivolokujinazejik.pdf personality words list pdf justine moritz in frankenstein a series of unfortunate events the end great cheap project cars bakitivenisaxo.pdf wwe 2k19 game download apk and obb how to get humax remote to control tv 98979304157.pdf vulaxukitujej.pdf 1608bfc85cc01e---21437717896.pdf how is the economy right now 2020 48979291872.pdf 28577373510.pdf 85311976931.pdf clash of clans hack ios private server 2020 31797764060.pdf fe electrical and computer review manual by michael r. lindeburg pdf minitool partition wizard 9.1 how to get free video star packs jump leads how to use them

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download