Lets revisit some data that we looked at in Chapter 8, in Table 8.1. Let X =
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Here is the bivariate regression to predict height from gender:
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-1.png)
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-2.png)
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-3.png)
Here is the Pearson r (which could also be called a point biserial correlation) between gender and height:
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-4.png)
Here is the independent samples t test to compare mean height for gender groups with gender coded 1 = male and 2 = female:
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-5.png)
![Let€™s revisit some data that we looked at in Chapter](https://dsd5zvtm8ll6.cloudfront.net/si.question.images/image/images9/597-M-S-L-R(4878)-6.png)
a. Compare the F for your bivariate regression with the t from your independent samples t test. How are these related?
b. Compare the multiple R from your bivariate regression with the r from your bivariate correlation; compare the R2 from the regression with an ï¨2 effect size computed by hand from your t test. How are these related?
c. What do you conclude regarding these three ways of analyzing the data?
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Related Book For
Applied Statistics From Bivariate Through Multivariate Techniques
ISBN: 9781412991346
2nd Edition
Authors: Rebecca M. Warner
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