If you have a curvilinear relationship, then: (Hint: The two most important sources of bias in this context are probably linearity 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.

i'm going to search it

step-by-step explanation:

x = ±sqrt(10)

step-by-step explanation:

5 - 2x² = -15

subtract 5 from each side

5-5 - 2x² = -15-5

-2x^2   = -20

divide by -2 on each side

-2x^2   /-2= -20/-2

x^2 =10

take the square root on each side

sqrt(x^2 )=±sqrt(10)

x = ±sqrt(10)

check:

5 - 2x² = -15

+sqrt(10)

5 - 2(sqrt(10))² = -15

5 -2(10) = -15

5 -20 = -15

-15 = -15

-sqrt(10)

5 - 2(-sqrt(10))² = -15

5 -2(10) = -15

5 -20 = -15

-15 = -15