Answer:
Step-by-step explanation:
PART 1
y=mx+b
m is slope and slope is 0 sooo
y=0x+b
it passes through (3,7)
7=0(3)+b
7=0+b
b=7
y=0x+7
so just y=7
PART 2
y=mx+b
y=1/5x+b
1/2=1/5(-2/3)+b
1/2=-2/15+b
1/2+2/15=b
15/30+4/30=b
19/30=b
y=1/5x+19/30
Answer:
y = 1/2x - 7
Step-by-step explanation:
slope intercept form:
y = mx + b
y = -5
m(slope) = 1/2
x = 4
y = mx + b
<em>substitute </em>
-5 = 1/2*4 + b
-5 = 2 + b
-5 - 2 = b
-7 = b
y = 1/2x - 7
Answer:
8 is the side length of the greater square. 100=x^2+(x-2)^2
Step-by-step explanation:
64+36=100
8 squared plus 6 squared equals 10 squared
8 is 2 more than 6
100=x^2 +y^2
x-y=2
100=x^2+(x-2)^2
Answer:
Step-by-step explanation:
i got x=3.21007319
Answer:
![3.13](https://tex.z-dn.net/?f=3.13%3C%5Csigma%5E2%20%3C%204.91)
Step-by-step explanation:
We are given the following in the question:
Sample size, n = 67
Variance = 3.85
We have to find 80% confidence interval for the population variance of the weights.
Degree of freedom = 67 - 1 = 66
Level of significance = 0.2
Chi square critical value for lower tail =
![\chi^2_{1-\frac{\alpha}{2}}= 51.770](https://tex.z-dn.net/?f=%5Cchi%5E2_%7B1-%5Cfrac%7B%5Calpha%7D%7B2%7D%7D%3D%2051.770)
Chi square critical value for upper tail =
![\chi^2_{\frac{\alpha}{2}}= 81.085](https://tex.z-dn.net/?f=%5Cchi%5E2_%7B%5Cfrac%7B%5Calpha%7D%7B2%7D%7D%3D%2081.085)
80% confidence interval:
![\dfrac{(n-1)S^2}{\chi^2_{\frac{\alpha}{2}}} < \sigma^2 < \dfrac{(n-1)S^2}{\chi^2_{1-\frac{\alpha}{2}}}](https://tex.z-dn.net/?f=%5Cdfrac%7B%28n-1%29S%5E2%7D%7B%5Cchi%5E2_%7B%5Cfrac%7B%5Calpha%7D%7B2%7D%7D%7D%20%3C%20%5Csigma%5E2%20%3C%20%5Cdfrac%7B%28n-1%29S%5E2%7D%7B%5Cchi%5E2_%7B1-%5Cfrac%7B%5Calpha%7D%7B2%7D%7D%7D)
Putting values, we get,
![=\dfrac{(67-1)3.85}{81.085} < \sigma^2 < \dfrac{(67-1)3.85}{51.770}\\\\=3.13](https://tex.z-dn.net/?f=%3D%5Cdfrac%7B%2867-1%293.85%7D%7B81.085%7D%20%3C%20%5Csigma%5E2%20%3C%20%5Cdfrac%7B%2867-1%293.85%7D%7B51.770%7D%5C%5C%5C%5C%3D3.13%3C%5Csigma%5E2%20%3C%204.91)
Thus, (3.13,4.91) is the required 80% confidence interval for the population variance of the weights.