<em>The answer is 13 pounds (lbs.) and 4 ounces (oz.). There are 2 tents and each is 6 pounds. Add those together and that is 12 pounds. All that is left is to add the ounces. 10+10=20. Since there are only 16 ounces in a pound and we have 20 ounces, we add an extra pound and are left with 4 ounces. </em>
A=6. I think that is the answer
Common factor is an expression that is common. Her we can see 2x+5 is a factor for both of the terms so it is the common factor.
Answer:
The estimate of a population proportion is approximately 541.
Step-by-step explanation:
We can solve the the problem by using the formula for minimum sample needed for interval estimate of a population proportion which is given by the formula
n = pq ((Z/2) / E)^2
As, p is not defined so we use the standard p and q which is 0.5 and 0.5.
The reason for this is we have to choose form 0.1 to 0.9 both values of p and q, we will find the maximum value of pq occurs when they both are 0.5.
Next, we will find the value of (Z/2) by looking at the Z-table, we will find that at 98% confidence (Z/2) = 2.326. Now we start substituting the values in the above formula
n = (0.5)×(0.5) × (2.326/0.05)^2
n = 541.027
n ≅ 541.
Answer:
Step-by-step explanation:
Hello!
a.
Given the data corresponding the variables:
x 2 6 6 7 9
y 3 2 6 9 5
The Scatterplot is attached.
b.
To compute the correlation coefficient you need several auxiliary calculations:
∑X= 30
∑X²= 206
∑Y= 25
∑Y²= 155
∑XY= 162
![r= \frac{sumXY-\frac{(sumX)(sumY)}{n} }{\sqrt{[sumX^2-\frac{(sumX)^2}{n} ][sumY^2-\frac{(sumY)^2}{n} ]} }](https://tex.z-dn.net/?f=r%3D%20%5Cfrac%7BsumXY-%5Cfrac%7B%28sumX%29%28sumY%29%7D%7Bn%7D%20%7D%7B%5Csqrt%7B%5BsumX%5E2-%5Cfrac%7B%28sumX%29%5E2%7D%7Bn%7D%20%5D%5BsumY%5E2-%5Cfrac%7B%28sumY%29%5E2%7D%7Bn%7D%20%5D%7D%20%7D)
![r= \frac{162-\frac{(30)*(25)}{5} }{\sqrt{[206-\frac{(30)^2}{5} ][155-\frac{(25)^2}{5} ]} }](https://tex.z-dn.net/?f=r%3D%20%5Cfrac%7B162-%5Cfrac%7B%2830%29%2A%2825%29%7D%7B5%7D%20%7D%7B%5Csqrt%7B%5B206-%5Cfrac%7B%2830%29%5E2%7D%7B5%7D%20%5D%5B155-%5Cfrac%7B%2825%29%5E2%7D%7B5%7D%20%5D%7D%20%7D)
r= 0.429 ≅ 0.43
c.
The critical value for r has n-2 degrees of freedom, let's say for example you have α:0.05

For a two-tailed test.
Because the correlation coefficient is <u>positive</u> and the absolute value of the correlation coefficient, _<u>0.43</u>___, is <u>not greater</u> than the critical value for this data set,_<u>0.878</u>__, <u>no </u>linear relationship exists between x and y.
I hope it helps!