If you have a curvilinear relationship, then: (Hint: The two most important sources of bias in this context are probably lineari
ty and normality.) a. It is not appropriate to use Pearson's correlation because it assumes a linear relationship between variables.
b. Pearson's correlation can be used in the same way as it is for linear relationships.
c. You can use Pearson's correlation; you just need to remember that a curve indicates that the variables are not linearly related.
d. Transforming the data won't help.
b. Pearson's correlation can be used in the same way as it is for linear relationships
Explanation:
Pearson's correlation can also be termed "simple linear regression analysis" is a statistical measure used to determine if two numeric variables are significantly linearly related. Pearson's correlation coefficient is used to measures the statistical relationship or association between two continuous variables.
If each of the 28 students made at least $25, you would multiply 28 and 25 together to obtain the least amount of money the class raised. That gets, 28x25 = 700. The class made at least $700.
