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elena-s [515]
3 years ago
12

Please help me

Mathematics
1 answer:
love history [14]3 years ago
6 0
6\frac{1}{2}\ mi\ in\ 2\ hour\\\\
16\frac{1}{4} mi\ in\ x\ hour\\\\
proportion:\\
6\frac{1}{2}-2\\
16\frac{1}{4}-x\\\\
cross\ multiplication\\\\
6\frac{1}{2}x=2*16\frac{1}{4}\\\\
5.5x=32.5\ \ \ | divide\ by\ 5.5\\\\
x=5.9hour\\\\
It\ will\ take\ 5.9\ hours
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Answer:  (a) $6,600

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<u>Step-by-step explanation:</u>

Interest (I) = Principal (P) x rate (r) x time (t)

I = 22,000(0.06)(5)

 = 6,600


Accrued (A) = Principal (P) + Interest (I)

A = 22,000 + 6,600

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4

Step-by-step explanation:

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Which of the following statements is true of the correlation analysis? The null hypothesis for the Pearson correlation coefficie
ASHA 777 [7]

Answer:

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

Null hypothesis: \rho =0

Alternative hypothesis: \rho \neq 0

The statistic to check the hypothesis is given by:

t=\frac{r \sqrt{n-2}}{\sqrt{1-r^2}}

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:  

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

Solution to the problem

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

Null hypothesis: \rho =0

Alternative hypothesis: \rho \neq 0

The statistic to check the hypothesis is given by:

t=\frac{r \sqrt{n-2}}{\sqrt{1-r^2}}

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

4 0
3 years ago
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