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Ksju [112]
4 years ago
10

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.
Mathematics
1 answer:
I am Lyosha [343]4 years ago
3 0

Answer:

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.

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There are 109 terms in given series.

According to the statement

we have given that the sum of series is AP series and

This is 19 20.5 22 23.5 ... 181

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Now, we have to find the number of terms in the series.

Then we use the summation formula which is

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Substitute the all given values in it like

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After solve the equation for n

10,900= 100n

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