Which of the following statements is true of the correlation analysis? The null hypothesis for the Pearson correlation coefficient states that there is always a strong association between two variables. The larger the correlation coefficient, the weaker the association between two variables. The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero. The Pearson correlation coefficient measures the degree of linear association that ranges from 1.0 to 10.0. The Pearson correlation coefficient measures the degree of linear association between three variables.

Answer :

Answer:

In order to test the hypothesis if the correlation coefficient it's significant we have the following hypothesis:

Null hypothesis: [tex]\rho =0[/tex]

Alternative hypothesis: [tex]\rho \neq 0[/tex]

The statistic to check the hypothesis is given by:

[tex]t=\frac{r \sqrt{n-2}}{\sqrt{1-r^2}}[/tex]

And is distributed with n-2 degreed of freedom. df=n-2=10-2=8

For this case the null hypothesis represent that we don't have association betwen the dependent variable Y and the independent variable X and that means r=0. So then the best option for this case is:

The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero

Step-by-step explanation:

Previous concepts

The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.

And in order to calculate the correlation coefficient we can use this formula:  

[tex]r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}[/tex]  

Solution to the problem

In order to test the hypothesis if the correlation coefficient it's significant we have the following hypothesis:

Null hypothesis: [tex]\rho =0[/tex]

Alternative hypothesis: [tex]\rho \neq 0[/tex]

The statistic to check the hypothesis is given by:

[tex]t=\frac{r \sqrt{n-2}}{\sqrt{1-r^2}}[/tex]

And is distributed with n-2 degreed of freedom. df=n-2=10-2=8

For this case the null hypothesis represent that we don't have association betwen the dependent variable Y and the independent variable X and that means r=0. So then the best option for this case is:

The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero

Correlation analysis is meant to determine the strength of a linear

relationship between variables.

  • A true statement of correlation analysis is that; The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero.

Reasons:

Correlation analysis is a test to determine the presence and or amount of relationship that exists between variables.

During an hypothesis test aimed at determining the existence of a correlation between variables, the null and alternative hypothesis are as follows;

Null hypothesis, H₀: There are no relationships between the variables

H₀: r = 0

Alternative hypothesis, Hₐ: The variables have an association

Hₐ: r ≠ 0

Therefore, the true statement of the correlation analysis is; The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero.

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