# Lets revisit some data that we looked at in Chapter 8, in Table 8.1. Let X =

## Question:

Let€™s revisit some data that we looked at in Chapter 8, in Table 8.1. Let X = Gender, coded 1 = male, 2 = female. Let Y = height. Using SPSS, run a bivariate regression to predict Height from Gender. If you do not still have your output from analyses you ran in Chapter 8 also run the Pearson correlation between Gender and Height, and the independent samples t test comparing mean heights for male and female groups.
Here is the bivariate regression to predict height from gender:

Here is the Pearson r (which could also be called a point biserial correlation) between gender and height:

Here is the independent samples t test to compare mean height for gender groups with gender coded 1 = male and 2 = female:

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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